Graph of Wikipedia
Project members:
- Vilhelm Agdur, Department of Mathematics, Uppsala University
- Henrik Ekström, Institution
- Simon Johansson, Institution
- Albin Toft, Department of Mathematics, KTH Royal Institute of Technology and Combient Mix AB, Stockholm.
Background
To assume that Wikipedia is a webiste known to most is perhaps not a controversial statement, however to avoid confusion in case someone is not familiar with the website, the following can be found on the Wikipedia article about Wikipedia:
When reading articles on Wikipedia, a reader will be faced with hyper-links to other articles in the Wiki-verse, which might lead a reader onto a path towards a completely different subject than what was on the first page that was read. This phenomena, together with a curiosity about the properties of a graph constructed using the articles and links of Wikipedia lead to this project, with a goal of exploring and analysing what will be reffered to as the "Wiki-Graph". For those unfamiliar with the term "Graph", a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related". The objects correspond to mathematical abstractions called vertices (also called nodes or points) and each of the related pairs of vertices is called an edge. For instance, a graph could be a social network, where the verices are the users and the edges are the friendship relationships among the users. Or as in our case, the vertices could be articles and the edges the hyperlinks between the articles.
Example of a directed graph.
Purpose
The main goal of the project was to explore the Wiki-Graph to answer some exploration oriented questions about the structure. These were questions such as: 1. What is the size of the graph? 2. How dense is it? 3. What can be said about the hierarchical structure of categories? 4. What is the fastest way to go from article A to B, using only the hyper-links? 5. Etc.
Methods
In order to answer these questions we have used the GraphFrames API with Apache Spark. GraphFrames provide tools for analyzing and querying large graphs in a distributed and scalable fashion, with built-in implementations for algorithms such as PageRank, LabelPropagation, BFS and many more.
Data
The data used for the project came from WikiData database dumps. More about precisely what data, how it was ingested, preprocessed and finally joined to produce a GraphFrame, will be presented in upcoming notebooks.
Loading of the Wikipedia data
The data from Wikipedia is available as .sql-file dumps here. So we need to do a little bit of work to get these SQL files into an actual database on the cloud.
For the redirects table, it was small enough to fit into memory on the driver, so we could do it in a fairly simple way. The page and pagelinks tables are too big for that, so we need to be a bit trickier.
As a first step, we download the .sql file:
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
FileUtils.copyURLToFile(new URL("https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-redirect.sql.gz"), new File("/tmp/enwiki-latest-redirect.sql.gz"))
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
Having done this, we first unzip the file, and then move the file from local storage to the DBFS:
gzip -d /tmp/enwiki-latest-redirect.sql.gz
mv file:/tmp/enwiki-latest-redirect.sql /enwiki-latest-redirect.sql
res1: Boolean = true
Having gotten the data onto the DBFS, we can now read it into Spark:
val rawSQLdump = spark.read.textFile("/enwiki-latest-redirect.sql")
rawSQLdump: org.apache.spark.sql.Dataset[String] = [value: string]
The first forty lines are setting up the database, then we get a lot of very long INSERT INTO lines with many many entries being inserted.
println(rawSQLdump.take(40).mkString("\n"))
-- MySQL dump 10.19 Distrib 10.3.34-MariaDB, for debian-linux-gnu (x86_64)
--
-- Host: db1106 Database: enwiki
-- ------------------------------------------------------
-- Server version 10.4.25-MariaDB-log
/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;
/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;
/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;
/*!40101 SET NAMES utf8mb4 */;
/*!40103 SET @OLD_TIME_ZONE=@@TIME_ZONE */;
/*!40103 SET TIME_ZONE='+00:00' */;
/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;
/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;
/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;
/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;
--
-- Table structure for table `redirect`
--
DROP TABLE IF EXISTS `redirect`;
/*!40101 SET @saved_cs_client = @@character_set_client */;
/*!40101 SET character_set_client = utf8 */;
CREATE TABLE `redirect` (
`rd_from` int(8) unsigned NOT NULL DEFAULT 0,
`rd_namespace` int(11) NOT NULL DEFAULT 0,
`rd_title` varbinary(255) NOT NULL DEFAULT '',
`rd_interwiki` varbinary(32) DEFAULT NULL,
`rd_fragment` varbinary(255) DEFAULT NULL,
PRIMARY KEY (`rd_from`),
KEY `rd_ns_title` (`rd_namespace`,`rd_title`,`rd_from`)
) ENGINE=InnoDB DEFAULT CHARSET=binary ROW_FORMAT=COMPRESSED;
/*!40101 SET character_set_client = @saved_cs_client */;
--
-- Dumping data for table `redirect`
--
/*!40000 ALTER TABLE `redirect` DISABLE KEYS */;
The remaining rows look something like this, except much much longer:
println(rawSQLdump.take(41)(40).substring(0,188) + ",...,"+rawSQLdump.take(41)(40).substring(rawSQLdump.take(41)(40).length()-75, rawSQLdump.take(41)(40).length()))
INSERT INTO `redirect` VALUES (10,0,'Computer_accessibility','',''),(13,0,'History_of_Afghanistan','',''),(14,0,'Geography_of_Afghanistan','',''),(15,0,'Demographics_of_Afghanistan','',''),...,(170997,0,'Supersaturation','',''),(171002,0,'Dubbing','','ADR/post-sync');
Next up, let us strip out the INSERT INTO bit and the initial and final parentheses, then split at each ),(, so that we get each entry as its own string.
val pageDataRows = rawSQLdump.filter(x => x.startsWith("INSERT INTO"))
.flatMap(x => x.substring(31, x.length()-2).split("""\),\("""))
pageDataRows: org.apache.spark.sql.Dataset[String] = [value: string]
So now our data looks like this:
println(pageDataRows.take(20).mkString("\n"))
10,0,'Computer_accessibility','',''
13,0,'History_of_Afghanistan','',''
14,0,'Geography_of_Afghanistan','',''
15,0,'Demographics_of_Afghanistan','',''
18,0,'Communications_in_Afghanistan','',''
19,0,'Transport_in_Afghanistan','',''
20,0,'Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan','',''
21,0,'Foreign_relations_of_Afghanistan','',''
23,0,'Assistive_technology','',''
24,0,'Amoeba','',''
25,0,'Autism_spectrum','',''
27,0,'History_of_Albania','',''
29,0,'Demographics_of_Albania','',''
30,0,'As_We_May_Think','',''
35,0,'Politics_of_Albania','',''
36,0,'Economy_of_Albania','',''
40,0,'Afroasiatic_languages','',''
42,0,'Constructed_language','',''
46,0,'Abacus','',''
47,0,'Abalone','',''
This table has a modest amount of rows, only 12.9 million.
pageDataRows.count()
res15: Long = 12937954
The above looks a whole lot like a CSV file, doesn't it? Let's write it to file as such. Note that we write it as text instead of as CSV because our data is in the format of a single string per row.
pageDataRows.toDF().write.mode("overwrite").text("/WikipediaData/enwiki-redirect.csv")
Now we want to read this back in, but with the right schema and column names and so on. So we start by creating the schema. In order to be sure that all the rows got parsed correctly, we add an extra column named _corrupt_record, which will get the raw CSV text whenever it couldn't be parsed right, and otherwise be set to NULL.
import org.apache.spark.sql.types._
// Start by creating a case class of a row entry:
case class WikiRedirect(rd_from:Int,
rd_namespace:Int,
rd_title:String,
rd_interwiki:String,
rd_fragment:String
)
// then we generate a schema object from the case class: (code copypasted from here: https://sparkbyexamples.com/spark/convert-case-class-to-spark-schema/)
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
val pageSchema = ScalaReflection.schemaFor[WikiRedirect].dataType.asInstanceOf[StructType].add("_corrupt_record", StringType, true)
import org.apache.spark.sql.types._
defined class WikiRedirect
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
pageSchema: org.apache.spark.sql.types.StructType = StructType(StructField(rd_from,IntegerType,false),StructField(rd_namespace,IntegerType,false),StructField(rd_title,StringType,true),StructField(rd_interwiki,StringType,true),StructField(rd_fragment,StringType,true),StructField(_corrupt_record,StringType,true))
Then we read it back in with the schema we just created:
val readFromCSV = spark.read
.options(Map("quote" -> "'", "mode" -> "PERMISSIVE", "columnNameOfCorruptRecord" -> "_corrupt_record"))
.schema(pageSchema)
.csv("/WikipediaData/enwiki-redirect.csv")
readFromCSV: org.apache.spark.sql.DataFrame = [rd_from: int, rd_namespace: int ... 4 more fields]
Let's have a look at what we just created:
display(readFromCSV)
| rd_from | rd_namespace | rd_title | rd_interwiki | rd_fragment | _corrupt_record |
|---|---|---|---|---|---|
| 10.0 | 0.0 | Computer_accessibility | null | null | null |
| 13.0 | 0.0 | History_of_Afghanistan | null | null | null |
| 14.0 | 0.0 | Geography_of_Afghanistan | null | null | null |
| 15.0 | 0.0 | Demographics_of_Afghanistan | null | null | null |
| 18.0 | 0.0 | Communications_in_Afghanistan | null | null | null |
| 19.0 | 0.0 | Transport_in_Afghanistan | null | null | null |
| 20.0 | 0.0 | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan | null | null | null |
| 21.0 | 0.0 | Foreign_relations_of_Afghanistan | null | null | null |
| 23.0 | 0.0 | Assistive_technology | null | null | null |
| 24.0 | 0.0 | Amoeba | null | null | null |
| 25.0 | 0.0 | Autism_spectrum | null | null | null |
| 27.0 | 0.0 | History_of_Albania | null | null | null |
| 29.0 | 0.0 | Demographics_of_Albania | null | null | null |
| 30.0 | 0.0 | As_We_May_Think | null | null | null |
| 35.0 | 0.0 | Politics_of_Albania | null | null | null |
| 36.0 | 0.0 | Economy_of_Albania | null | null | null |
| 40.0 | 0.0 | Afroasiatic_languages | null | null | null |
| 42.0 | 0.0 | Constructed_language | null | null | null |
| 46.0 | 0.0 | Abacus | null | null | null |
| 47.0 | 0.0 | Abalone | null | null | null |
| 48.0 | 0.0 | Abbadid_dynasty | null | null | null |
| 49.0 | 0.0 | Abbess | null | null | null |
| 50.0 | 0.0 | Abbeville | null | null | null |
| 51.0 | 0.0 | Abbey | null | null | null |
| 52.0 | 0.0 | Abbot | null | null | null |
| 53.0 | 0.0 | Abbreviation | null | null | null |
| 54.0 | 0.0 | Atlas_Shrugged | null | null | null |
| 56.0 | 0.0 | Constructed_language | null | null | null |
| 58.0 | 0.0 | List_of_Atlas_Shrugged_characters | null | null | null |
| 59.0 | 0.0 | Atlas_Shrugged | null | null | null |
| 60.0 | 0.0 | Atlas_Shrugged | null | null | null |
| 241.0 | 0.0 | African_Americans | null | null | null |
| 242.0 | 0.0 | Adolf_Hitler | null | null | null |
| 247.0 | 0.0 | Abecedarian | null | null | null |
| 248.0 | 0.0 | Cain_and_Abel | null | null | null |
| 249.0 | 0.0 | Abensberg | null | null | null |
| 251.0 | 0.0 | Aberdeen,_South_Dakota | null | null | null |
| 254.0 | 0.0 | Arthur_Koestler | null | null | null |
| 255.0 | 0.0 | Ayn_Rand | null | null | null |
| 256.0 | 0.0 | Alexander_the_Great | null | null | null |
| 258.0 | 0.0 | Anchorage,_Alaska | null | null | null |
| 259.0 | 0.0 | Logical_form | null | null | null |
| 260.0 | 0.0 | Existence_of_God | null | null | null |
| 263.0 | 0.0 | Anarchy | null | null | null |
| 264.0 | 0.0 | ASCII_art | null | null | null |
| 269.0 | 0.0 | Academy_Awards | null | null | null |
| 270.0 | 0.0 | Academy_Award_for_Best_Picture | null | null | null |
| 271.0 | 0.0 | Austrian_German | null | null | null |
| 272.0 | 0.0 | Elitism | null | null | null |
| 274.0 | 0.0 | Axiom_of_choice | null | null | null |
| 276.0 | 0.0 | American_football | null | null | null |
| 278.0 | 0.0 | United_States | null | null | null |
| 279.0 | 0.0 | Anna_Kournikova | null | null | null |
| 280.0 | 0.0 | Andorra | null | null | null |
| 287.0 | 0.0 | Austroasiatic_languages | null | null | null |
| 289.0 | 0.0 | Lists_of_actors | null | null | null |
| 291.0 | 0.0 | Anarcho-capitalism | null | null | null |
| 293.0 | 0.0 | Anarcho-capitalism | null | null | null |
| 296.0 | 0.0 | Lists_of_actors | null | null | null |
| 299.0 | 0.0 | An_American_in_Paris | null | null | null |
| 301.0 | 0.0 | Automorphism | null | null | null |
| 302.0 | 0.0 | Action_film | null | null | null |
| 304.0 | 0.0 | Africa | null | null | null |
| 306.0 | 0.0 | Statistics | null | null | null |
| 325.0 | 0.0 | Action_film | null | null | null |
| 338.0 | 0.0 | Auto_racing | null | null | null |
| 347.0 | 0.0 | Demographics_of_Algeria | null | null | null |
| 353.0 | 0.0 | Foreign_relations_of_Algeria | null | null | null |
| 369.0 | 0.0 | Atlas_Shrugged | null | null | null |
| 583.0 | 0.0 | Amoeba | null | null | null |
| 589.0 | 0.0 | Ashmore_and_Cartier_Islands | null | null | null |
| 596.0 | 0.0 | Artificial_language | null | null | null |
| 598.0 | 0.0 | Afroasiatic_languages | null | null | null |
| 609.0 | 0.0 | Foreign_relations_of_Andorra | null | null | null |
| 617.0 | 0.0 | Al_Gore | null | null | null |
| 618.0 | 0.0 | An_Enquiry_Concerning_Human_Understanding | null | null | null |
| 622.0 | 0.0 | Al_Gore | null | null | null |
| 626.0 | 0.0 | Auteur | null | null | null |
| 629.0 | 0.0 | Abstract_algebra | null | null | null |
| 635.0 | 0.0 | Analysis_of_variance | null | null | null |
| 644.0 | 0.0 | Arithmetic_logic_unit | null | null | null |
| 648.0 | 0.0 | Actor | null | null | null |
| 654.0 | 0.0 | Computer_accessibility | null | null | null |
| 668.0 | 0.0 | Logical_form | null | null | null |
| 669.0 | 0.0 | Allotropy | null | null | null |
| 686.0 | 0.0 | Amalthea_(mythology) | null | null | null |
| 687.0 | 0.0 | Analysis_of_variance | null | null | null |
| 693.0 | 0.0 | Broch | null | null | null |
| 696.0 | 0.0 | AA | null | Rivers | null |
| 724.0 | 4.0 | Nupedia_and_Wikipedia | null | null | null |
| 726.0 | 5.0 | Nupedia_and_Wikipedia | null | null | null |
| 727.0 | 0.0 | History_of_astronomy | null | null | null |
| 731.0 | 0.0 | History_of_astronomy | null | null | null |
| 735.0 | 0.0 | Al_Gore | null | null | null |
| 743.0 | 0.0 | Antigua_and_Barbuda | null | null | null |
| 749.0 | 0.0 | Astronomer | null | null | null |
| 755.0 | 0.0 | History_of_Albania | null | null | null |
| 758.0 | 0.0 | Foreign_relations_of_Albania | null | null | null |
| 759.0 | 0.0 | Demographics_of_Albania | null | null | null |
| 763.0 | 0.0 | Foreign_relations_of_Albania | null | null | null |
| 767.0 | 0.0 | A._E._van_Vogt | null | null | null |
| 807.0 | 0.0 | Telecommunications_in_Albania | null | null | null |
| 813.0 | 0.0 | History_of_Afghanistan | null | null | null |
| 814.0 | 0.0 | Geography_of_Afghanistan | null | null | null |
| 815.0 | 0.0 | Government_of_the_Islamic_Emirate_of_Afghanistan | null | null | null |
| 816.0 | 0.0 | Demographics_of_Afghanistan | null | null | null |
| 817.0 | 0.0 | Economy_of_Afghanistan | null | null | null |
| 818.0 | 0.0 | Communications_in_Afghanistan | null | null | null |
| 820.0 | 0.0 | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan | null | null | null |
| 821.0 | 0.0 | Foreign_relations_of_Afghanistan | null | null | null |
| 822.0 | 0.0 | Afghanistan | null | null | null |
| 832.0 | 0.0 | Foreign_relations_of_Austria | null | null | null |
| 839.0 | 0.0 | Anglicanism | null | null | null |
| 855.0 | 0.0 | Abiotic_component | null | null | null |
| 858.0 | 0.0 | Au | null | null | null |
| 860.0 | 0.0 | Åland | null | null | null |
| 873.0 | 0.0 | Civilization | null | null | null |
| 882.0 | 0.0 | Supermajority | null | Majority of the entire membership | null |
| 891.0 | 0.0 | Accounting | null | null | null |
| 907.0 | 0.0 | AWK | null | null | null |
| 908.0 | 0.0 | Nomic | null | null | null |
| 918.0 | 0.0 | Antisemitism | null | null | null |
| 919.0 | 0.0 | Antisemitism | null | null | null |
| 923.0 | 0.0 | A._A._Milne | null | null | null |
| 926.0 | 0.0 | Alumni | null | null | null |
| 935.0 | 0.0 | Automated_Alice | null | null | null |
| 936.0 | 0.0 | Automated_Alice | null | null | null |
| 937.0 | 0.0 | Automated_Alice | null | null | null |
| 938.0 | 0.0 | Automated_Alice | null | null | null |
| 939.0 | 0.0 | Automated_Alice | null | null | null |
| 940.0 | 0.0 | Automated_Alice | null | null | null |
| 941.0 | 0.0 | Automated_Alice | null | null | null |
| 942.0 | 0.0 | Automated_Alice | null | null | null |
| 943.0 | 0.0 | Automated_Alice | null | null | null |
| 944.0 | 0.0 | Automated_Alice | null | null | null |
| 945.0 | 0.0 | Automated_Alice | null | null | null |
| 946.0 | 0.0 | Automated_Alice | null | null | null |
| 959.0 | 0.0 | Voiced_velar_nasal | null | null | null |
| 963.0 | 0.0 | Existence_of_God | null | null | null |
| 970.0 | 0.0 | Ambient_calculus | null | null | null |
| 972.0 | 0.0 | Necronomicon | null | Fictional history | null |
| 973.0 | 0.0 | A_priori_and_a_posteriori | null | null | null |
| 975.0 | 0.0 | Ambient_calculus | null | null | null |
| 982.0 | 0.0 | A_priori_and_a_posteriori | null | null | null |
| 1026.0 | 0.0 | Anarcho-capitalism | null | null | null |
| 1035.0 | 0.0 | AAL | null | null | null |
| 1059.0 | 0.0 | Statistics | null | Applications | null |
| 1061.0 | 0.0 | Analysis_of_variance | null | Random-effects models | null |
| 1062.0 | 0.0 | Analysis_of_variance | null | null | null |
| 1075.0 | 0.0 | Foreign_relations_of_Antigua_and_Barbuda | null | null | null |
| 1083.0 | 0.0 | Demographics_of_Azerbaijan | null | null | null |
| 1085.0 | 0.0 | Telecommunications_in_Azerbaijan | null | null | null |
| 1089.0 | 0.0 | Foreign_relations_of_Azerbaijan | null | null | null |
| 1105.0 | 0.0 | Foreign_relations_of_Argentina | null | null | null |
| 1108.0 | 0.0 | Foreign_relations_of_Argentina | null | null | null |
| 1109.0 | 0.0 | American_Samoa | null | Geography | null |
| 1114.0 | 0.0 | American_Samoa | null | null | null |
| 1116.0 | 0.0 | American_Samoa | null | null | null |
| 1123.0 | 0.0 | Foreign_relations_of_Australia | null | null | null |
| 1151.0 | 0.0 | AK-47 | null | null | null |
| 1153.0 | 0.0 | Amhrán_na_bhFiann | null | null | null |
| 1186.0 | 0.0 | Aphex_Twin | null | null | null |
| 1189.0 | 0.0 | Creed | null | null | null |
| 1190.0 | 0.0 | Alternate_history | null | null | null |
| 1195.0 | 0.0 | Allotropy | null | null | null |
| 1199.0 | 0.0 | Angles | null | null | null |
| 1205.0 | 0.0 | Atomic_orbital | null | null | null |
| 1220.0 | 0.0 | Anguilla | null | null | null |
| 1221.0 | 0.0 | Anguilla | null | null | null |
| 1228.0 | 0.0 | Ashmore_and_Cartier_Islands | null | Geography | null |
| 1229.0 | 0.0 | Ashmore_and_Cartier_Islands | null | null | null |
| 1230.0 | 0.0 | Ashmore_and_Cartier_Islands | null | Government | null |
| 1231.0 | 0.0 | Ashmore_and_Cartier_Islands | null | null | null |
| 1232.0 | 0.0 | Ashmore_and_Cartier_Islands | null | Economy and migration | null |
| 1233.0 | 0.0 | Ashmore_and_Cartier_Islands | null | null | null |
| 1238.0 | 0.0 | Nuclear_weapon | null | null | null |
| 1245.0 | 0.0 | Alpha_particle | null | null | null |
| 1246.0 | 0.0 | Alfonso_Arau | null | null | null |
| 1255.0 | 0.0 | Astronomical_unit | null | null | null |
| 1262.0 | 0.0 | Cant_(language) | null | Argot | null |
| 1268.0 | 0.0 | Artificial_intelligence | null | null | null |
| 1276.0 | 0.0 | Antarctica | null | Economic activity and tourism | null |
| 1277.0 | 0.0 | Antarctic_Treaty_System | null | null | null |
| 1280.0 | 0.0 | Military_activity_in_the_Antarctic | null | null | null |
| 1290.0 | 0.0 | Antarctic_Treaty_System | null | null | null |
| 1292.0 | 0.0 | Algernon_Charles_Swinburne | null | null | null |
| 1295.0 | 0.0 | American_League_Championship_Series | null | null | null |
| 1297.0 | 0.0 | Hebrew_Bible | null | null | null |
| 1299.0 | 0.0 | Abbadid_dynasty | null | null | null |
| 1302.0 | 0.0 | Abdomen | null | null | null |
| 1311.0 | 0.0 | Ada_Lovelace | null | Ada Byron's notes on the analytical engine | null |
| 1312.0 | 0.0 | Augustine_of_Hippo | null | null | null |
| 1321.0 | 0.0 | Sagrada_Família | null | null | null |
| 1328.0 | 0.0 | Anno_Domini | null | null | null |
| 1339.0 | 0.0 | Americans_with_Disabilities_Act_of_1990 | null | null | null |
| 1340.0 | 0.0 | Americans_with_Disabilities_Act_of_1990 | null | null | null |
| 1341.0 | 0.0 | Americans_with_Disabilities_Act_of_1990 | null | null | null |
| 1342.0 | 0.0 | Anno_Domini | null | null | null |
| 1345.0 | 0.0 | Apache_HTTP_Server | null | null | null |
| 1355.0 | 0.0 | Anderitum | null | null | null |
| 1399.0 | 0.0 | Attention_deficit_hyperactivity_disorder | null | null | null |
| 1406.0 | 0.0 | Amine | null | null | null |
| 1407.0 | 0.0 | Antonie_van_Leeuwenhoek | null | null | null |
| 1410.0 | 0.0 | Antonie_van_Leeuwenhoek | null | null | null |
| 1415.0 | 0.0 | Pope_Adrian_I | null | null | null |
| 1426.0 | 0.0 | Pope_Adrian_II | null | null | null |
| 1429.0 | 0.0 | Pope_Adrian_IV | null | null | null |
| 1434.0 | 0.0 | Abgar_V | null | null | null |
| 1457.0 | 0.0 | Alzheimer's_disease | null | null | null |
| 1459.0 | 0.0 | Vitamin_C | null | null | null |
| 1476.0 | 0.0 | Prime_Minister_of_Australia | null | null | null |
| 1502.0 | 0.0 | List_of_minor_characters_in_the_Alice_series | null | The Eaglet, the Lory, the Duck, and the Dodo | null |
| 1511.0 | 0.0 | Albert_I_of_Germany | null | null | null |
| 1515.0 | 0.0 | Albert_III,_Duke_of_Saxony | null | null | null |
| 1516.0 | 0.0 | Albert_II,_Margrave_of_Meissen | null | null | null |
| 1517.0 | 0.0 | Albert_of_Aix | null | null | null |
| 1533.0 | 0.0 | Aachen | null | null | null |
| 1535.0 | 0.0 | Acorn | null | null | null |
| 1539.0 | 0.0 | Adirondack_Mountains | null | null | null |
| 1561.0 | 0.0 | Áedán_mac_Gabráin | null | null | null |
| 1572.0 | 0.0 | Al-Battani | null | null | null |
| 1609.0 | 0.0 | Pope_Alexander_VI | null | null | null |
| 1610.0 | 0.0 | Pope_Alexander_VII | null | null | null |
| 1611.0 | 0.0 | Pope_Alexander_VIII | null | null | null |
| 1626.0 | 0.0 | Aleksandr_Solzhenitsyn | null | null | null |
| 1636.0 | 0.0 | Antoine_de_Saint-Exupéry | null | null | null |
| 1641.0 | 0.0 | Alfred,_Duke_of_Saxe-Coburg_and_Gotha | null | null | null |
| 1651.0 | 0.0 | Alfred_of_Beverley | null | null | null |
| 1672.0 | 0.0 | Alfonso_VIII_of_Castile | null | null | null |
| 1673.0 | 0.0 | Alfonso_IX_of_León | null | null | null |
| 1678.0 | 0.0 | Alfonso_de_Cartagena | null | null | null |
| 1682.0 | 0.0 | Ahmose_I | null | null | null |
| 1699.0 | 0.0 | Alfonso_VI_of_León_and_Castile | null | null | null |
| 1703.0 | 0.0 | Alfonso_VII_of_León_and_Castile | null | null | null |
| 1704.0 | 0.0 | Alfonso_VIII_of_Castile | null | null | null |
| 1705.0 | 0.0 | Alfonso_IX_of_León | null | null | null |
| 1706.0 | 0.0 | Alfonso_X_of_Castile | null | null | null |
| 1707.0 | 0.0 | Alfonso_XI_of_Castile | null | null | null |
| 1708.0 | 0.0 | Alfonso_XII | null | null | null |
| 1709.0 | 0.0 | Alfonso_XIII | null | null | null |
| 1733.0 | 0.0 | Anacreon | null | null | null |
| 1744.0 | 0.0 | Pope_Anastasius_III | null | null | null |
| 1745.0 | 0.0 | Pope_Anastasius_IV | null | null | null |
| 1766.0 | 0.0 | Asteroid_belt | null | null | null |
| 1768.0 | 0.0 | Alice | null | null | null |
| 1769.0 | 0.0 | An_Enquiry_Concerning_Human_Understanding | null | null | null |
| 1771.0 | 0.0 | Apollo_program | null | null | null |
| 1772.0 | 0.0 | Arthritis | null | null | null |
| 1775.0 | 0.0 | Discrete_mathematics | null | null | null |
| 1809.0 | 0.0 | Thomas_Aquinas | null | null | null |
| 1811.0 | 0.0 | Hydrolysis | null | Hydrolysis of amide links | null |
| 1821.0 | 0.0 | Antoine_Lavoisier | null | null | null |
| 1824.0 | 0.0 | Footage | null | null | null |
| 1830.0 | 0.0 | Air_pollution | null | null | null |
| 1831.0 | 0.0 | Protocol_on_Environmental_Protection_to_the_Antarctic_Treaty | null | null | null |
| 1833.0 | 0.0 | Americentrism | null | null | null |
| 1838.0 | 0.0 | Amazon_River | null | null | null |
| 1852.0 | 0.0 | Ancient_Greece | null | null | null |
| 1855.0 | 0.0 | History_of_Africa | null | null | null |
| 1858.0 | 0.0 | Aromatic_compound | null | null | null |
| 1876.0 | 0.0 | Adémar_de_Chabannes | null | null | null |
| 1877.0 | 0.0 | Catharism | null | null | null |
| 1885.0 | 0.0 | Erotic_asphyxiation | null | null | null |
| 1889.0 | 0.0 | Assault_weapons_ban | null | null | null |
| 1903.0 | 0.0 | American_Airlines_Flight_77 | null | null | null |
| 1904.0 | 0.0 | American_Airlines_Flight_11 | null | null | null |
| 1906.0 | 0.0 | Aberration_(astronomy) | null | null | null |
| 1936.0 | 0.0 | Astronomical_unit | null | null | null |
| 1952.0 | 0.0 | Industry_Standard_Architecture | null | null | null |
| 1959.0 | 0.0 | Telephone_exchange | null | Early automatic exchanges | null |
| 1972.0 | 0.0 | Aviation | null | null | null |
| 1976.0 | 0.0 | Adomnán | null | null | null |
| 1978.0 | 0.0 | Assassin_(disambiguation) | null | null | null |
| 1982.0 | 0.0 | Alice | null | Acronyms | null |
| 1984.0 | 0.0 | Arab_world | null | null | null |
| 1993.0 | 0.0 | Alan_Ayckbourn | null | null | null |
| 2001.0 | 0.0 | Al-Qaeda | null | null | null |
| 2002.0 | 0.0 | Argumentum_ad_populum | null | null | null |
| 2005.0 | 0.0 | Addiction | null | null | null |
| 2008.0 | 0.0 | Al-Qaeda | null | null | null |
| 2043.0 | 0.0 | Anti-Americanism | null | null | null |
| 2050.0 | 0.0 | Archaeology | null | null | null |
| 2051.0 | 0.0 | Anarchism | null | null | null |
| 2058.0 | 0.0 | Atheism | null | null | null |
| 2071.0 | 0.0 | Afro_Celt_Sound_System | null | null | null |
| 2073.0 | 0.0 | Andrew_Jackson | null | null | null |
| 2074.0 | 0.0 | Andrew_Jackson | null | null | null |
| 2079.0 | 0.0 | Autumnal_equinox | null | null | null |
| 2090.0 | 0.0 | Albert_of_Hohenzollern | null | null | null |
| 2095.0 | 0.0 | Parapsychology | null | null | null |
| 2128.0 | 0.0 | Los_Angeles_Angels | null | null | null |
| 2132.0 | 0.0 | Ara_Pacis | null | null | null |
| 2145.0 | 0.0 | Catharism | null | null | null |
| 2146.0 | 0.0 | Aleksandr_Solzhenitsyn | null | null | null |
| 2149.0 | 0.0 | Armour | null | null | null |
| 2153.0 | 0.0 | Elitism | null | null | null |
| 2164.0 | 0.0 | Peremptory_plea | null | null | null |
| 2165.0 | 0.0 | Peremptory_plea | null | null | null |
| 2188.0 | 0.0 | Accident_(philosophy) | null | null | null |
| 2190.0 | 0.0 | Alternate_history | null | null | null |
| 2203.0 | 0.0 | Religion_in_Poland | null | null | null |
| 2206.0 | 0.0 | Ampere | null | null | null |
| 2211.0 | 0.0 | Folklore_of_the_United_States | null | null | null |
| 2213.0 | 0.0 | Modus_ponens | null | null | null |
| 2220.0 | 0.0 | Acts_of_the_Apostles | null | null | null |
| 2223.0 | 0.0 | Slaughterhouse | null | null | null |
| 2227.0 | 0.0 | Argumentum_a_fortiori | null | null | null |
| 2228.0 | 0.0 | Ad_hominem | null | null | null |
| 2249.0 | 0.0 | Amplification | null | null | null |
| 2258.0 | 0.0 | Anglicanism | null | null | null |
| 2260.0 | 0.0 | Analog_Science_Fiction_and_Fact | null | null | null |
| 2261.0 | 0.0 | Analog_Science_Fiction_and_Fact | null | null | null |
| 2262.0 | 0.0 | Analog_Science_Fiction_and_Fact | null | null | null |
| 2264.0 | 0.0 | Heptarchy | null | List of Anglo-Saxon kingdoms | null |
| 2269.0 | 0.0 | Asynchronous_Transfer_Mode | null | null | null |
| 2271.0 | 0.0 | Asymmetric_digital_subscriber_line | null | null | null |
| 2280.0 | 0.0 | Giant_panda | null | null | null |
| 2281.0 | 0.0 | Arctic_fox | null | null | null |
| 2285.0 | 0.0 | Tank_destroyer | null | null | null |
| 2290.0 | 0.0 | Indigenous_peoples | null | null | null |
| 2295.0 | 0.0 | Arhat | null | null | null |
| 2297.0 | 0.0 | Springbok | null | null | null |
| 2298.0 | 0.0 | Blue_crane | null | null | null |
| 2302.0 | 0.0 | Aramaic | null | null | null |
| 2306.0 | 0.0 | AT&T | null | null | null |
| 2320.0 | 0.0 | Audio_codec | null | null | null |
| 2324.0 | 0.0 | All_Saints'_Day | null | null | null |
| 2351.0 | 0.0 | HIV/AIDS | null | null | null |
| 2354.0 | 0.0 | Outline_of_archaeology | null | null | null |
| 2367.0 | 0.0 | HIV/AIDS | null | null | null |
| 2379.0 | 0.0 | Binary_relation | null | null | null |
| 2404.0 | 0.0 | Aon_(company) | null | null | null |
| 2419.0 | 0.0 | Alloy | null | null | null |
| 2432.0 | 0.0 | Albrecht_III_Achilles,_Elector_of_Brandenburg | null | null | null |
| 2446.0 | 0.0 | Appalachian_dulcimer | null | null | null |
| 2462.0 | 0.0 | Anti-globalization_movement | null | null | null |
| 2464.0 | 0.0 | Anti-globalization_movement | null | null | null |
| 2468.0 | 0.0 | Aaron's_rod | null | null | null |
| 2469.0 | 0.0 | AB | null | null | null |
| 2478.0 | 0.0 | Barada | null | null | null |
| 2479.0 | 0.0 | Manama | null | null | null |
| 2486.0 | 0.0 | Chrysoberyl | null | Alexandrite | null |
| 2488.0 | 1.0 | Chrysoberyl | null | null | null |
| 2489.0 | 0.0 | Abandon | null | null | null |
| 2492.0 | 0.0 | Anal_sex | null | null | null |
| 2495.0 | 0.0 | Aurochs | null | null | null |
| 2496.0 | 0.0 | Etiology | null | null | null |
| 2520.0 | 0.0 | Addition | null | Natural numbers | null |
| 2523.0 | 0.0 | Alien | null | null | null |
| 2525.0 | 0.0 | Al_Jazeera | null | null | null |
| 2527.0 | 0.0 | Ruhollah_Khomeini | null | null | null |
| 2533.0 | 0.0 | Alphorn | null | null | null |
| 2535.0 | 0.0 | AW | null | null | null |
| 2537.0 | 0.0 | Analog_Science_Fiction_and_Fact | null | null | null |
| 2549.0 | 0.0 | Analog_Science_Fiction_and_Fact | null | null | null |
| 2561.0 | 0.0 | List_of_federal_political_scandals_in_the_United_States | null | null | null |
| 2565.0 | 0.0 | Albert,_Duke_of_Prussia | null | null | null |
| 2567.0 | 0.0 | Academy_Awards | null | null | null |
| 2568.0 | 0.0 | Apsis | null | Perihelion and aphelion | null |
| 2569.0 | 0.0 | Apsis | null | null | null |
| 2571.0 | 0.0 | Rope_(film) | null | null | null |
| 2572.0 | 0.0 | Arianism | null | null | null |
| 2595.0 | 0.0 | Atlas_(computer) | null | null | null |
| 2599.0 | 0.0 | AA | null | null | null |
| 2600.0 | 0.0 | Aaron's_rod | null | null | null |
| 2601.0 | 0.0 | Abandon | null | null | null |
| 2603.0 | 0.0 | Abaris_the_Hyperborean | null | null | null |
| 2612.0 | 0.0 | Abbo_of_Fleury | null | null | null |
| 2615.0 | 0.0 | Charles_Farrar_Browne | null | null | null |
| 2631.0 | 0.0 | Ælfric | null | null | null |
| 2636.0 | 0.0 | Accounting | null | null | null |
| 2638.0 | 0.0 | ACID | null | null | null |
| 2643.0 | 0.0 | Ajax_the_Lesser | null | null | null |
| 2644.0 | 0.0 | Ajax_the_Great | null | null | null |
| 2647.0 | 0.0 | American_Indians | null | null | null |
| 2648.0 | 0.0 | Abandon | null | null | null |
| 2649.0 | 0.0 | Abandonment_(legal) | null | null | null |
| 2650.0 | 0.0 | Abandonment_(legal) | null | Abandonment of easement | null |
| 2651.0 | 0.0 | Abandonment_(legal) | null | null | null |
| 2652.0 | 0.0 | Nuisance_abatement | null | null | null |
| 2653.0 | 0.0 | Abatement | null | null | null |
| 2655.0 | 0.0 | Abatement | null | null | null |
| 2656.0 | 0.0 | Abatement | null | null | null |
| 2657.0 | 0.0 | Abatement | null | null | null |
| 2658.0 | 0.0 | Abatement_(heraldry) | null | null | null |
| 2659.0 | 0.0 | American_Revolutionary_War | null | null | null |
| 2664.0 | 0.0 | Affirmation_(law) | null | null | null |
| 2675.0 | 0.0 | Abd_al-Rahman | null | null | null |
| 2682.0 | 0.0 | Abdul_Qadir | null | null | null |
| 2683.0 | 0.0 | Abdelaziz_of_Morocco | null | null | null |
| 2688.0 | 0.0 | Pneumatic_motor | null | null | null |
| 2697.0 | 0.0 | Abraham_ibn_Ezra | null | null | null |
| 2711.0 | 0.0 | Aberdeenshire_(historic) | null | null | null |
| 2713.0 | 0.0 | Aberdyfi | null | null | null |
| 2725.0 | 0.0 | Aesthetics | null | null | null |
| 2746.0 | 0.0 | Same-sex_relationship | null | Forms of same-sex relationships throughout history | null |
| 2751.0 | 0.0 | The_Angry_Brigade | null | null | null |
| 2760.0 | 0.0 | Arab_(disambiguation) | null | null | null |
| 2765.0 | 0.0 | Anatomical_Therapeutic_Chemical_Classification_System | null | null | null |
| 2768.0 | 0.0 | Antiarrhythmic_agent | null | null | null |
| 2771.0 | 0.0 | Air_conditioning | null | null | null |
| 2774.0 | 0.0 | Alfred_Kinsey | null | null | null |
| 2775.0 | 0.0 | Auto_racing | null | null | null |
| 2776.0 | 0.0 | Antisemitism | null | null | null |
| 2789.0 | 0.0 | James_Tiptree_Jr. | null | null | null |
| 2793.0 | 0.0 | Application_software | null | null | null |
| 2804.0 | 0.0 | Application_firewall | null | null | null |
| 2808.0 | 0.0 | Nuclear_weapon | null | null | null |
| 2821.0 | 0.0 | Set_theory | null | Axiomatic set theory | null |
| 2828.0 | 0.0 | Abipón | null | null | null |
| 2831.0 | 0.0 | Abkhazia | null | null | null |
| 2842.0 | 0.0 | Bohr_model | null | null | null |
| 2855.0 | 0.0 | Latin_American_Integration_Association | null | null | null |
| 2863.0 | 0.0 | AT&T | null | null | null |
| 2872.0 | 0.0 | Arthur,_Prince_of_Wales | null | null | null |
| 2880.0 | 0.0 | Anti-ballistic_missile | null | null | null |
| 2884.0 | 5.0 | WikiProject_Computer_science/Manual_of_style | null | null | null |
| 2888.0 | 0.0 | Amorphous_solid | null | null | null |
| 2897.0 | 0.0 | Indigenous_peoples_of_Arizona | null | null | null |
| 2898.0 | 0.0 | Abdul_Rashid_Dostum | null | null | null |
| 2903.0 | 0.0 | The_Diary_of_a_Young_Girl | null | null | null |
| 2904.0 | 0.0 | Kabylia | null | null | null |
| 2912.0 | 0.0 | Archaeoastronomy | null | null | null |
| 2914.0 | 0.0 | French_hip_hop | null | null | null |
| 2915.0 | 0.0 | Gh_hip_hop | null | null | null |
| 2918.0 | 0.0 | Argument_from_ignorance | null | null | null |
| 2922.0 | 0.0 | AIM_(software) | null | null | null |
| 2929.0 | 0.0 | Armillary_sphere | null | null | null |
| 2937.0 | 0.0 | Algemeen_Nijmeegs_Studentenblad | null | null | null |
| 2951.0 | 0.0 | Louis_Althusser | null | null | null |
| 2969.0 | 0.0 | Aurora | null | null | null |
| 2970.0 | 0.0 | Aurora | null | null | null |
| 2971.0 | 0.0 | Abstraction_(computer_science) | null | Abstraction in object oriented programming | null |
| 2977.0 | 0.0 | American_Sign_Language | null | null | null |
| 2993.0 | 0.0 | Amputation | null | null | null |
| 2996.0 | 0.0 | HMS_Ark_Royal | null | null | null |
| 2998.0 | 0.0 | Acceleration | null | null | null |
| 3000.0 | 0.0 | AD_Police_Files | null | Manga | null |
| 3005.0 | 0.0 | Apadravya | null | null | null |
| 3006.0 | 0.0 | Ampallang | null | null | null |
| 3008.0 | 0.0 | Albinism | null | null | null |
| 3009.0 | 0.0 | Analcime | null | null | null |
| 3023.0 | 0.0 | Archimedes'_screw | null | null | null |
| 3024.0 | 0.0 | Multiplication | null | null | null |
| 3033.0 | 0.0 | Antenna_(radio) | null | null | null |
| 3039.0 | 0.0 | Shadrach,_Meshach,_and_Abednego | null | null | null |
| 3041.0 | 0.0 | Acanthocephala | null | null | null |
| 3042.0 | 0.0 | Alcobaça | null | null | null |
| 3051.0 | 0.0 | Clan_McDuck | null | Angus \"Pothole\" McDuck | null |
| 3057.0 | 0.0 | List_of_Donald_Duck_universe_characters | null | April, May, and June | null |
| 3059.0 | 0.0 | Athlon | null | null | null |
| 3062.0 | 0.0 | Duck_family_(Disney) | null | Whitewater Duck | null |
| 3063.0 | 0.0 | Asperger_syndrome | null | null | null |
| 3066.0 | 0.0 | Authoritarianism | null | null | null |
| 3086.0 | 0.0 | İskenderun | null | null | null |
| 3099.0 | 0.0 | AbiWord | null | null | null |
| 3106.0 | 0.0 | AirPort | null | null | null |
| 3114.0 | 0.0 | Amiga_500 | null | Amiga 500 Plus | null |
| 3126.0 | 0.0 | Ahriman | null | null | null |
| 3136.0 | 0.0 | Concept | null | null | null |
| 3139.0 | 0.0 | Apostle_(disambiguation) | null | Religion | null |
| 3154.0 | 0.0 | Fairchild_Republic_A-10_Thunderbolt_II | null | null | null |
| 3156.0 | 0.0 | Albrecht_Dürer | null | null | null |
| 3163.0 | 0.0 | Anthroposophy | null | null | null |
| 3164.0 | 0.0 | Evidence_of_common_descent | null | null | null |
| 3166.0 | 0.0 | A.C._Milan | null | null | null |
| 3180.0 | 0.0 | Anomaly | null | null | null |
| 3182.0 | 0.0 | Avenger | null | null | null |
| 3187.0 | 0.0 | Agglutination | null | null | null |
| 3190.0 | 0.0 | Ascending_chain_condition | null | null | null |
| 3197.0 | 0.0 | A._E._Housman | null | null | null |
| 3208.0 | 0.0 | Antidepressant | null | null | null |
| 3210.0 | 0.0 | Alexander_Rutskoy | null | null | null |
| 3215.0 | 0.0 | Multivibrator | null | Astable | null |
| 3219.0 | 0.0 | Actor | null | null | null |
| 3220.0 | 0.0 | Artificial_intelligence | null | null | null |
| 3223.0 | 0.0 | Ai | null | null | null |
| 3227.0 | 0.0 | Azores | null | null | null |
| 3230.0 | 0.0 | Relative_atomic_mass | null | null | null |
| 3232.0 | 0.0 | Anthropic_principle | null | null | null |
| 3247.0 | 0.0 | Roman_Catholic_Archdiocese_for_the_Military_Services,_USA | null | null | null |
| 3248.0 | 0.0 | Archaeopteryx | null | null | null |
| 3254.0 | 0.0 | Amuck! | null | null | null |
| 3260.0 | 0.0 | Line_Islands | null | null | null |
| 3264.0 | 0.0 | Aborigine | null | null | null |
| 3276.0 | 0.0 | Antiterrorism_and_Effective_Death_Penalty_Act_of_1996 | null | null | null |
| 3280.0 | 0.0 | Bomis | null | null | null |
| 3281.0 | 0.0 | Biblical_hermeneutics | null | null | null |
| 3282.0 | 0.0 | Baltic_Sea | null | null | null |
| 3283.0 | 0.0 | Ballroom_dance | null | null | null |
| 3284.0 | 0.0 | Biology | null | null | null |
| 3288.0 | 0.0 | Bill_Clinton | null | null | null |
| 3290.0 | 0.0 | Biblical_canon | null | null | null |
| 3298.0 | 0.0 | The_Buddha | null | null | null |
| 3299.0 | 0.0 | Bijection,_injection_and_surjection | null | null | null |
| 3300.0 | 0.0 | Buddhism | null | null | null |
| 3303.0 | 0.0 | Baltimore_Ravens | null | null | null |
| 3307.0 | 0.0 | Aaron | null | null | null |
| 3311.0 | 0.0 | List_of_business_schools_in_Asia | null | null | null |
| 3317.0 | 0.0 | The_Birth_of_a_Nation | null | null | null |
| 3318.0 | 0.0 | Boethius | null | null | null |
| 3320.0 | 0.0 | Mental_event | null | null | null |
| 3322.0 | 0.0 | Business_school | null | null | null |
| 3323.0 | 0.0 | Britney_Spears | null | null | null |
| 3326.0 | 0.0 | Baby_One_More_Time | null | null | null |
| 3327.0 | 0.0 | Binomial_distribution | null | null | null |
| 3329.0 | 0.0 | Binomial_distribution | null | null | null |
| 3330.0 | 0.0 | Biochemistry | null | null | null |
| 3342.0 | 0.0 | Germany | null | null | null |
| 3344.0 | 0.0 | Basic | null | null | null |
| 3346.0 | 0.0 | Robert_Byrd | null | null | null |
| 3349.0 | 0.0 | Business_school | null | null | null |
| 3366.0 | 0.0 | Commonwealth_of_Nations | null | null | null |
| 3369.0 | 0.0 | Board_game | null | null | null |
| 3373.0 | 0.0 | Outline_of_biology | null | null | null |
| 3407.0 | 0.0 | Baruch_Spinoza | null | null | null |
| 3409.0 | 0.0 | Ontology | null | Overview | null |
| 3413.0 | 0.0 | Batch_processing | null | null | null |
| 3418.0 | 0.0 | Basil | null | null | null |
| 3424.0 | 0.0 | BBC_Radio_1 | null | null | null |
| 3425.0 | 0.0 | BBC_Online | null | null | null |
| 3433.0 | 0.0 | Visual_impairment | null | null | null |
| 3445.0 | 0.0 | Alcohol_intoxication | null | null | null |
| 3448.0 | 0.0 | Steer_wrestling | null | null | null |
| 3480.0 | 0.0 | Royal_Bahamas_Defence_Force | null | null | null |
| 3481.0 | 0.0 | Foreign_relations_of_the_Bahamas | null | null | null |
| 3484.0 | 0.0 | Bahrain | null | null | null |
| 3492.0 | 0.0 | Baker_Island | null | null | null |
| 3493.0 | 0.0 | Baker_Island | null | null | null |
| 3494.0 | 0.0 | Baker_Island | null | null | null |
| 3496.0 | 0.0 | Baker_Island | null | Description | null |
| 3509.0 | 0.0 | Foreign_relations_of_Bangladesh | null | null | null |
| 3510.0 | 0.0 | Foreign_relations_of_Bangladesh | null | null | null |
| 3519.0 | 0.0 | Foreign_relations_of_Barbados | null | null | null |
| 3522.0 | 0.0 | Bassas_da_India | null | null | null |
| 3524.0 | 0.0 | Bassas_da_India | null | null | null |
| 3527.0 | 0.0 | Bassas_da_India | null | null | null |
| 3529.0 | 0.0 | Bassas_da_India | null | null | null |
| 3539.0 | 0.0 | Telecommunications_in_Belarus | null | null | null |
| 3548.0 | 0.0 | Foreign_relations_of_Belgium | null | null | null |
| 3549.0 | 0.0 | Belgium | null | null | null |
| 3550.0 | 0.0 | Foreign_relations_of_Belgium | null | null | null |
| 3551.0 | 0.0 | Belgium | null | null | null |
| 3578.0 | 0.0 | Bermuda | null | null | null |
| 3587.0 | 0.0 | Bhutan | null | null | null |
| 3600.0 | 0.0 | Cultural_depictions_of_blindness | null | null | null |
| 3619.0 | 0.0 | Botswana_Defence_Force | null | null | null |
| 3622.0 | 0.0 | Bouvet_Island | null | Geography and geology | null |
| 3623.0 | 0.0 | Bouvet_Island | null | null | null |
| 3624.0 | 0.0 | Bouvet_Island | null | null | null |
| 3625.0 | 0.0 | Bouvet_Island | null | null | null |
| 3626.0 | 0.0 | Bouvet_Island | null | null | null |
| 3627.0 | 0.0 | Bouvet_Island | null | null | null |
| 3628.0 | 0.0 | Bouvet_Island | null | null | null |
| 3640.0 | 0.0 | British_Indian_Ocean_Territory | null | null | null |
| 3641.0 | 0.0 | British_Indian_Ocean_Territory | null | Demographics | null |
| 3642.0 | 0.0 | British_Indian_Ocean_Territory | null | null | null |
| 3643.0 | 0.0 | British_Indian_Ocean_Territory | null | null | null |
| 3644.0 | 0.0 | British_Indian_Ocean_Territory | null | null | null |
| 3645.0 | 0.0 | British_Indian_Ocean_Territory | null | null | null |
| 3646.0 | 0.0 | British_Indian_Ocean_Territory | null | null | null |
| 3647.0 | 0.0 | British_Indian_Ocean_Territory | null | null | null |
| 3656.0 | 0.0 | British_Virgin_Islands | null | null | null |
| 3686.0 | 0.0 | Geography_of_Myanmar | null | null | null |
| 3689.0 | 0.0 | Economy_of_Myanmar | null | null | null |
| 3690.0 | 0.0 | Telecommunications_in_Myanmar | null | null | null |
| 3723.0 | 0.0 | BSE | null | null | null |
| 3726.0 | 0.0 | Breakdancing | null | null | null |
| 3732.0 | 0.0 | Bhangra | null | null | null |
| 3737.0 | 0.0 | Baptists | null | null | null |
| 3739.0 | 0.0 | BSD_licenses | null | null | null |
| 3762.0 | 0.0 | Länder | null | null | null |
| 3763.0 | 0.0 | Bavaria | null | null | null |
| 3767.0 | 0.0 | Bundeskanzler | null | null | null |
| 3770.0 | 0.0 | Cabinet_of_Germany | null | null | null |
| 3773.0 | 0.0 | Der_Blaue_Reiter | null | null | null |
| 3781.0 | 0.0 | Mumbai | null | null | null |
| 3790.0 | 0.0 | Bodybuilding | null | null | null |
| 3791.0 | 0.0 | Bryan_MacLean | null | null | null |
| 3796.0 | 0.0 | Biblical_canon | null | null | null |
| 3803.0 | 0.0 | Strike_zone | null | null | null |
| 3804.0 | 0.0 | Slugging_percentage | null | null | null |
| 3818.0 | 0.0 | Babel_fish | null | null | null |
| 3820.0 | 0.0 | Mental_event | null | null | null |
| 3824.0 | 0.0 | Babel_fish | null | null | null |
| 3830.0 | 0.0 | Bryce_Canyon_National_Park | null | null | null |
| 3831.0 | 0.0 | Encyclopædia_Britannica | null | null | null |
| 3847.0 | 0.0 | Taste | null | Basic tastes | null |
| 3855.0 | 0.0 | Origins_of_baseball | null | null | null |
| 3871.0 | 0.0 | Substance_theory | null | null | null |
| 3879.0 | 0.0 | Statistics | null | Business statistics | null |
| 3913.0 | 0.0 | Binary_operation | null | null | null |
| 3920.0 | 0.0 | The_Beatles | null | null | null |
| 3922.0 | 0.0 | Road_bicycle | null | null | null |
| 3934.0 | 0.0 | Baby_boom | null | null | null |
| 3935.0 | 0.0 | Buddhism | null | null | null |
| 3966.0 | 0.0 | Border_Gateway_Protocol | null | null | null |
| 3972.0 | 0.0 | Cycling | null | null | null |
| 3991.0 | 0.0 | BITS | null | null | null |
| 3994.0 | 0.0 | Benoit_Mandelbrot | null | null | null |
| 4003.0 | 0.0 | Pierre_Beaumarchais | null | null | null |
| 4014.0 | 0.0 | Bipolar_disorder | null | null | null |
| 4021.0 | 0.0 | Common_Era | null | null | null |
| 4022.0 | 0.0 | Common_Era | null | null | null |
| 4025.0 | 0.0 | BC | null | null | null |
| 4026.0 | 0.0 | Buckminster_Fuller | null | null | null |
| 4034.0 | 0.0 | Encyclopædia_Britannica_Eleventh_Edition | null | null | null |
| 4038.0 | 0.0 | Banach–Tarski_paradox | null | null | null |
| 4040.0 | 0.0 | BC | null | null | null |
| 4090.0 | 0.0 | Bitwise_operation | null | AND | null |
| 4105.0 | 0.0 | Outline_of_biochemistry | null | null | null |
| 4122.0 | 0.0 | B-roll | null | null | null |
| 4126.0 | 0.0 | Ballroom_dance | null | null | null |
| 4129.0 | 0.0 | CIM-10_Bomarc | null | null | null |
| 4151.0 | 0.0 | Brainfuck | null | null | null |
| 4167.0 | 0.0 | Utility_knife | null | null | null |
| 4174.0 | 0.0 | Six_Degrees_of_Kevin_Bacon | null | Bacon numbers | null |
| 4186.0 | 0.0 | Bacteriostatic_agent | null | null | null |
| 4201.0 | 0.0 | Francesco_Borromini | null | null | null |
| 4212.0 | 0.0 | Bolsheviks | null | null | null |
| 4215.0 | 0.0 | Brian_De_Palma | null | null | null |
| 4221.0 | 0.0 | North_American_B-25_Mitchell | null | null | null |
| 4222.0 | 0.0 | Berry_Berenson | null | null | null |
| 4226.0 | 0.0 | Brewster's_angle | null | null | null |
| 4229.0 | 1.0 | Bipolar_disorder | null | null | null |
| 4238.0 | 0.0 | The_Bronx | null | null | null |
| 4252.0 | 0.0 | Baháʼí_Faith | null | null | null |
| 4253.0 | 0.0 | Red_Army_Faction | null | null | null |
| 4265.0 | 0.0 | Titius–Bode_law | null | null | null |
| 4268.0 | 0.0 | The_Boston_Globe | null | null | null |
| 4272.0 | 0.0 | Elbląg | null | null | null |
| 4273.0 | 0.0 | Elbląg | null | null | null |
| 4275.0 | 0.0 | Gdańsk | null | null | null |
| 4276.0 | 0.0 | Oder | null | null | null |
| 4290.0 | 0.0 | Buddhism | null | null | null |
| 4291.0 | 0.0 | Buddhism | null | null | null |
| 4303.0 | 0.0 | University_of_Brighton | null | null | null |
| 4328.0 | 0.0 | Bohemia | null | null | null |
| 4336.0 | 0.0 | Bosnia_and_Herzegovina | null | null | null |
| 4412.0 | 0.0 | Binary_Synchronous_Communications | null | null | null |
| 4415.0 | 0.0 | ETA_(separatist_group) | null | null | null |
| 4426.0 | 0.0 | Brownian_motion | null | null | null |
| 4428.0 | 0.0 | Bacillus_thuringiensis | null | null | null |
| 4435.0 | 0.0 | Baltic_languages | null | null | null |
| 4439.0 | 0.0 | Baptists | null | null | null |
| 4464.0 | 0.0 | Book_of_Zechariah | null | null | null |
| 4466.0 | 0.0 | Black_Sox_Scandal | null | null | null |
| 4486.0 | 0.0 | Buckminsterfullerene | null | null | null |
| 4509.0 | 0.0 | GNU_Free_Documentation_License | null | null | null |
| 4521.0 | 0.0 | Bubble_sort | null | null | null |
| 4523.0 | 0.0 | Bipolar_disorder | null | Bipolar spectrum | null |
| 4530.0 | 0.0 | Blue_screen | null | null | null |
| 4562.0 | 0.0 | Pub | null | null | null |
| 4564.0 | 0.0 | Bitter_(beer) | null | null | null |
| 4586.0 | 0.0 | Greek_fire | null | null | null |
| 4590.0 | 0.0 | Brachycephaly | null | null | null |
| 4593.0 | 0.0 | Battleship_(game) | null | null | null |
| 4597.0 | 0.0 | Beryl | null | null | null |
| 4598.0 | 1.0 | Bolesław_I_the_Brave | null | null | null |
| 4599.0 | 0.0 | Boleslaus_I | null | null | null |
| 4600.0 | 0.0 | Bolesław_III_Wrymouth | null | null | null |
| 4605.0 | 0.0 | Battle_of_the_Nile | null | null | null |
| 4612.0 | 0.0 | Bird | null | null | null |
| 4623.0 | 0.0 | Great_Britain_and_Ireland | null | null | null |
| 4632.0 | 0.0 | Monarchy_of_the_United_Kingdom | null | null | null |
| 4634.0 | 0.0 | Bombardier | null | null | null |
| 4655.0 | 0.0 | Alliance_90/The_Greens | null | null | null |
| 4656.0 | 0.0 | Shogun | null | Shogunate | null |
| 4657.0 | 0.0 | Arbitration | null | null | null |
| 4663.0 | 0.0 | Basil_of_Caesarea | null | null | null |
| 4666.0 | 0.0 | C*-algebra | null | null | null |
| 4678.0 | 0.0 | Computer_font | null | BITMAP | null |
| 4696.0 | 0.0 | Prime_Minister_of_the_United_Kingdom | null | null | null |
| 4697.0 | 0.0 | List_of_United_Kingdom_general_elections | null | null | null |
| 4703.0 | 0.0 | Bob_Dylan | null | null | null |
| 4716.0 | 0.0 | Bohemia | null | null | null |
| 4720.0 | 0.0 | Epistle_to_the_Hebrews | null | null | null |
| 4740.0 | 0.0 | International_Bureau_of_Weights_and_Measures | null | null | null |
| 4747.0 | 0.0 | Blu_Tack | null | null | null |
| 4750.0 | 0.0 | Bodhidharma | null | null | null |
| 4773.0 | 0.0 | Balfour_Declaration | null | null | null |
| 4784.0 | 0.0 | Normal_distribution | null | null | null |
| 4790.0 | 0.0 | German_Navy | null | null | null |
| 4798.0 | 0.0 | Bronze_Age | null | null | null |
| 4799.0 | 0.0 | Bicameral_mentality | null | null | null |
| 4808.0 | 0.0 | Arbitrary-precision_arithmetic | null | null | null |
| 4812.0 | 0.0 | Battle_of_Świecino | null | null | null |
| 4830.0 | 0.0 | Bohr_model | null | null | null |
| 4837.0 | 0.0 | Befehlshaber_der_U-Boote | null | null | null |
| 4844.0 | 0.0 | Symmetry_in_biology | null | Bilateral symmetry | null |
| 4846.0 | 0.0 | Symmetry_in_biology | null | Bilateral symmetry | null |
| 4853.0 | 0.0 | Wrocław | null | null | null |
| 4855.0 | 0.0 | Basso_continuo | null | null | null |
| 4889.0 | 0.0 | Semi-trailer_truck | null | null | null |
| 4891.0 | 0.0 | Ballet | null | null | null |
| 4901.0 | 0.0 | Daiquiri | null | null | null |
| 4903.0 | 0.0 | Boson | null | null | null |
| 4919.0 | 0.0 | Bipolar_II_disorder | null | null | null |
| 4920.0 | 0.0 | October_Revolution | null | null | null |
| 4923.0 | 0.0 | List_of_Bubblegum_Crisis_characters | null | Boomers | null |
| 4932.0 | 0.0 | Basal_body_temperature | null | null | null |
| 4938.0 | 0.0 | Branch_predictor | null | null | null |
| 4939.0 | 0.0 | Gambling | null | null | null |
| 4954.0 | 0.0 | Battle_of_Świecino | null | null | null |
| 4962.0 | 0.0 | Batting_average_(baseball) | null | null | null |
| 4977.0 | 0.0 | Battle_of_Adrianople | null | null | null |
| 4984.0 | 0.0 | Battle_of_Adrianople | null | null | null |
| 4985.0 | 0.0 | Battle_of_the_Ardennes | null | null | null |
| 4998.0 | 0.0 | Operation_Aphrodite | null | null | null |
| 5010.0 | 0.0 | Mexican_tetra | null | null | null |
| 5012.0 | 0.0 | The_Adventures_of_Brisco_County,_Jr. | null | null | null |
| 5017.0 | 0.0 | The_Book_of_Counted_Sorrows | null | null | null |
| 5018.0 | 0.0 | Anal_sex | null | null | null |
| 5022.0 | 0.0 | B._F._Skinner | null | null | null |
| 5044.0 | 0.0 | Beast_of_Bodmin_Moor | null | null | null |
| 5054.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5055.0 | 0.0 | Computing | null | null | null |
| 5056.0 | 0.0 | Software | null | null | null |
| 5057.0 | 0.0 | Common_sense | null | null | null |
| 5058.0 | 0.0 | Celtic_music | null | null | null |
| 5060.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5061.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5062.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5063.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5064.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5065.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5066.0 | 0.0 | COBOL | null | null | null |
| 5067.0 | 0.0 | Christianity | null | null | null |
| 5068.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5069.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5070.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5071.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5072.0 | 0.0 | Country | null | null | null |
| 5073.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5074.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5075.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5076.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5077.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5078.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5079.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5080.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5081.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5082.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5085.0 | 0.0 | Berlin | null | null | null |
| 5088.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5089.0 | 0.0 | Cantor_set | null | null | null |
| 5093.0 | 0.0 | Cold_War | null | null | null |
| 5097.0 | 0.0 | Cryptography | null | null | null |
| 5098.0 | 0.0 | Cryptography | null | null | null |
| 5099.0 | 0.0 | Cryptanalysis | null | null | null |
| 5100.0 | 0.0 | Code | null | null | null |
| 5101.0 | 0.0 | Encryption | null | null | null |
| 5103.0 | 0.0 | Charleston | null | null | null |
| 5104.0 | 0.0 | Consequentialism | null | null | null |
| 5105.0 | 0.0 | On_the_Consolation_of_Philosophy | null | null | null |
| 5107.0 | 0.0 | Regress_argument | null | null | null |
| 5110.0 | 0.0 | Consciousness | null | null | null |
| 5112.0 | 0.0 | Charlie_Chaplin | null | null | null |
| 5115.0 | 0.0 | Khmer_language | null | null | null |
| 5120.0 | 0.0 | Chordate | null | null | null |
| 5121.0 | 0.0 | Combinatorics | null | null | null |
| 5122.0 | 0.0 | Constellation | null | null | null |
| 5123.0 | 0.0 | Cognitive_therapy | null | null | null |
| 5125.0 | 0.0 | Category_theory | null | null | null |
| 5126.0 | 0.0 | Summary_statistics | null | null | null |
| 5128.0 | 0.0 | Comedy_film | null | null | null |
| 5129.0 | 0.0 | Cult_film | null | null | null |
| 5130.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5133.0 | 0.0 | Charlize_Theron | null | null | null |
| 5137.0 | 0.0 | Cluster_sampling | null | null | null |
| 5138.0 | 0.0 | Cumulative_distribution_function | null | null | null |
| 5140.0 | 0.0 | Comedy_film | null | null | null |
| 5141.0 | 0.0 | Cult_film | null | null | null |
| 5143.0 | 0.0 | Cryptography | null | null | null |
| 5146.0 | 0.0 | Hash_function | null | null | null |
| 5149.0 | 0.0 | Computer_hardware | null | null | null |
| 5167.0 | 0.0 | Central_tendency | null | null | null |
| 5168.0 | 0.0 | Checkers | null | null | null |
| 5173.0 | 0.0 | Probability_distribution | null | Continuous probability distribution | null |
| 5181.0 | 0.0 | Continent | null | null | null |
| 5182.0 | 0.0 | Constitution | null | null | null |
| 5186.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5198.0 | 0.0 | Canadian_Armed_Forces | null | null | null |
| 5202.0 | 0.0 | List_of_cities_in_Canada | null | null | null |
| 5206.0 | 0.0 | Algorithmic_art | null | null | null |
| 5208.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5209.0 | 0.0 | The_World_Factbook | null | null | null |
| 5210.0 | 0.0 | C._S._Lewis | null | null | null |
| 5214.0 | 1.0 | C._S._Lewis | null | null | null |
| 5220.0 | 0.0 | Complex_number | null | null | null |
| 5227.0 | 0.0 | Chessboard | null | null | null |
| 5231.0 | 0.0 | Old_World_monkey | null | null | null |
| 5238.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5239.0 | 0.0 | Countable_set | null | null | null |
| 5242.0 | 0.0 | Ciliate | null | null | null |
| 5258.0 | 0.0 | Computer_data_storage | null | null | null |
| 5264.0 | 0.0 | Computer_monitor | null | null | null |
| 5276.0 | 1.0 | Computer_monitor | null | null | null |
| 5283.0 | 0.0 | Cryptomonad | null | null | null |
| 5287.0 | 0.0 | Classical_music | null | null | null |
| 5289.0 | 0.0 | Card_game | null | null | null |
| 5290.0 | 0.0 | Casino_game | null | null | null |
| 5291.0 | 0.0 | PC_game | null | null | null |
| 5292.0 | 0.0 | Collectible_card_game | null | null | null |
| 5297.0 | 0.0 | Character_(computing) | null | null | null |
| 5303.0 | 0.0 | Conic_section | null | null | null |
| 5310.0 | 0.0 | Computer_hardware | null | null | null |
| 5318.0 | 0.0 | Time-sharing | null | null | null |
| 5319.0 | 0.0 | Computer_multitasking | null | null | null |
| 5341.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5343.0 | 0.0 | Constitution_of_Canada | null | null | null |
| 5345.0 | 0.0 | Colloid | null | null | null |
| 5356.0 | 0.0 | Cancer_cluster | null | null | null |
| 5359.0 | 0.0 | Collectible_card_game | null | null | null |
| 5365.0 | 0.0 | Ichthys | null | null | null |
| 5369.0 | 0.0 | Birth_control | null | null | null |
| 5392.0 | 0.0 | Coriander | null | null | null |
| 5393.0 | 0.0 | Coriander | null | null | null |
| 5396.0 | 0.0 | Chris_Morris | null | null | null |
| 5400.0 | 0.0 | List_of_sovereign_states | null | null | null |
| 5410.0 | 0.0 | Poales | null | Cyperales | null |
| 5414.0 | 0.0 | Wargame | null | null | null |
| 5418.0 | 0.0 | Capitalism | null | null | null |
| 5419.0 | 0.0 | Computer | null | null | null |
| 5423.0 | 0.0 | Cross-examination | null | null | null |
| 5425.0 | 0.0 | Class_conflict | null | null | null |
| 5426.0 | 0.0 | Compression | null | null | null |
| 5435.0 | 0.0 | Royal_Cambodian_Armed_Forces | null | null | null |
| 5441.0 | 0.0 | C_(programming_language) | null | null | null |
| 5442.0 | 0.0 | Constructed_language | null | null | null |
| 5444.0 | 0.0 | Regress_argument | null | null | null |
| 5445.0 | 0.0 | Class_conflict | null | null | null |
| 5457.0 | 0.0 | Civilization_(video_game) | null | null | null |
| 5476.0 | 0.0 | Cayman_Islands | null | Law enforcement | null |
| 5501.0 | 0.0 | Christmas_Island | null | History | null |
| 5502.0 | 0.0 | Christmas_Island | null | Geography | null |
| 5503.0 | 0.0 | Christmas_Island | null | Demographics | null |
| 5504.0 | 0.0 | Christmas_Island | null | Government | null |
| 5505.0 | 0.0 | Christmas_Island | null | Economy | null |
| 5506.0 | 0.0 | Christmas_Island | null | null | null |
| 5507.0 | 0.0 | Christmas_Island | null | Transport | null |
| 5508.0 | 0.0 | Christmas_Island | null | null | null |
| 5511.0 | 0.0 | Clipperton_Island | null | History | null |
| 5512.0 | 0.0 | Clipperton_Island | null | Geography | null |
| 5513.0 | 0.0 | Clipperton_Island | null | null | null |
| 5514.0 | 0.0 | Clipperton_Island | null | null | null |
| 5515.0 | 0.0 | Clipperton_Island | null | null | null |
| 5516.0 | 0.0 | Clipperton_Island | null | null | null |
| 5517.0 | 0.0 | Clipperton_Island | null | null | null |
| 5518.0 | 0.0 | Clipperton_Island | null | null | null |
| 5521.0 | 0.0 | Cocos_(Keeling)_Islands | null | History | null |
| 5522.0 | 0.0 | Cocos_(Keeling)_Islands | null | Geography | null |
| 5524.0 | 0.0 | Cocos_(Keeling)_Islands | null | null | null |
| 5525.0 | 0.0 | Cocos_(Keeling)_Islands | null | Economy | null |
| 5526.0 | 0.0 | Cocos_(Keeling)_Islands | null | null | null |
| 5527.0 | 0.0 | Cocos_(Keeling)_Islands | null | Communications and transport | null |
| 5528.0 | 0.0 | Cocos_(Keeling)_Islands | null | null | null |
| 5542.0 | 0.0 | Coral_Sea_Islands | null | History and status | null |
| 5543.0 | 0.0 | Coral_Sea_Islands | null | Geography | null |
| 5544.0 | 0.0 | Coral_Sea_Islands | null | null | null |
| 5545.0 | 0.0 | Coral_Sea_Islands | null | null | null |
| 5546.0 | 0.0 | Coral_Sea_Islands | null | null | null |
| 5547.0 | 0.0 | Coral_Sea_Islands | null | null | null |
| 5548.0 | 0.0 | Coral_Sea_Islands | null | null | null |
| 5549.0 | 0.0 | Coral_Sea_Islands | null | null | null |
| 5601.0 | 0.0 | Cypriot_National_Guard | null | null | null |
| 5604.0 | 0.0 | Czech_Republic | null | null | null |
| 5607.0 | 0.0 | Demographics_of_the_Czech_Republic | null | null | null |
| 5608.0 | 0.0 | Politics_of_the_Czech_Republic | null | null | null |
| 5612.0 | 0.0 | Army_of_the_Czech_Republic | null | null | null |
| 5613.0 | 0.0 | Foreign_relations_of_the_Czech_Republic | null | null | null |
| 5616.0 | 0.0 | Creutzfeldt–Jakob_disease | null | null | null |
| 5618.0 | 0.0 | A_Clockwork_Orange | null | null | null |
| 5620.0 | 0.0 | Stroke | null | null | null |
| 5628.0 | 0.0 | Compiler | null | null | null |
| 5631.0 | 0.0 | Gruyère_cheese | null | null | null |
| 5632.0 | 0.0 | Cheese_Shop_sketch | null | null | null |
| 5634.0 | 0.0 | List_of_decades,_centuries,_and_millennia | null | null | null |
| 5650.0 | 0.0 | Comet | null | null | null |
| 5652.0 | 0.0 | Computer_network | null | null | null |
| 5677.0 | 0.0 | Cerebrospinal_fluid | null | null | null |
| 5680.0 | 0.0 | Chief_executive_officer | null | null | null |
| 5683.0 | 0.0 | Trade_fair | null | null | null |
| 5687.0 | 0.0 | University_of_Cambridge | null | null | null |
| 5731.0 | 0.0 | Capitalism | null | null | null |
| 5737.0 | 0.0 | Cross-cutting | null | null | null |
| 5741.0 | 0.0 | Monetary_policy | null | null | null |
| 5746.0 | 0.0 | Hash_function | null | null | null |
| 5747.0 | 0.0 | Key_(cryptography) | null | null | null |
| 5753.0 | 0.0 | Sexual_intercourse | null | null | null |
| 5764.0 | 0.0 | Charlie_Chaplin | null | null | null |
| 5773.0 | 0.0 | Carroll_O'Connor | null | null | null |
| 5780.0 | 0.0 | Chaco_Culture_National_Historical_Park | null | null | null |
| 5788.0 | 0.0 | Cretaceous–Paleogene_extinction_event | null | null | null |
| 5792.0 | 0.0 | Probability_distribution | null | Absolutely continuous probability distribution | null |
| 5798.0 | 0.0 | Closeted | null | null | null |
| 5799.0 | 0.0 | Coming_out | null | null | null |
| 5801.0 | 0.0 | Ecumenical_council | null | null | null |
| 5802.0 | 0.0 | Council_of_Trent | null | null | null |
| 5803.0 | 0.0 | Second_Vatican_Council | null | null | null |
| 5842.0 | 0.0 | Foreign_relations_of_Colombia | null | null | null |
| 5852.0 | 0.0 | Foreign_relations_of_the_Czech_Republic | null | null | null |
| 5856.0 | 0.0 | Holy_Roman_Empire | null | null | null |
| 5870.0 | 0.0 | Comics | null | null | null |
| 5871.0 | 0.0 | Tachycardia | null | null | null |
| 5875.0 | 0.0 | Jargon | null | null | null |
| 5877.0 | 0.0 | CORAL | null | null | null |
| 5880.0 | 0.0 | Comment_(computer_programming) | null | null | null |
| 5900.0 | 0.0 | Megacorporation | null | null | null |
| 5908.0 | 0.0 | Counterpoint | null | null | null |
| 5911.0 | 0.0 | Continuum_hypothesis | null | null | null |
| 5913.0 | 0.0 | Catalysis | null | null | null |
| 5915.0 | 0.0 | Catalysis | null | null | null |
| 5924.0 | 0.0 | Christian_eschatology | null | null | null |
| 5925.0 | 0.0 | Color | null | null | null |
| 5953.0 | 0.0 | Claude_Monet | null | null | null |
| 5960.0 | 0.0 | Genetic_code | null | Codons | null |
| 5968.0 | 0.0 | Computer_music | null | Computer-generated music | null |
| 5975.0 | 0.0 | Call_of_Cthulhu_(role-playing_game) | null | null | null |
| 5978.0 | 0.0 | Kyoto_Protocol | null | null | null |
| 5983.0 | 0.0 | Computer_science | null | null | null |
| 5994.0 | 4.0 | Nupedia_and_Wikipedia | null | null | null |
| 6012.0 | 0.0 | Church–Turing_thesis | null | null | null |
| 6017.0 | 0.0 | Cruise_missile | null | null | null |
| 6018.0 | 0.0 | Call_of_Cthulhu | null | null | null |
| 6022.0 | 0.0 | Cell_biology | null | null | null |
| 6030.0 | 0.0 | Chronic_fatigue_syndrome | null | null | null |
| 6031.0 | 0.0 | Chronic_fatigue_syndrome | null | null | null |
| 6032.0 | 0.0 | Chronic_fatigue_syndrome | null | null | null |
| 6033.0 | 0.0 | Chronic_fatigue_syndrome | null | null | null |
| 6037.0 | 0.0 | Continuous_function | null | null | null |
| 6043.0 | 0.0 | Critical_point_(thermodynamics) | null | null | null |
| 6053.0 | 0.0 | CE | null | null | null |
| 6054.0 | 0.0 | CE | null | null | null |
| 6055.0 | 0.0 | CD-ROM | null | null | null |
| 6063.0 | 0.0 | Cartoonist | null | null | null |
| 6065.0 | 0.0 | Sine_and_cosine | null | null | null |
| 6067.0 | 0.0 | Common_Lisp | null | null | null |
| 6070.0 | 0.0 | Orange_(colour) | null | null | null |
| 6071.0 | 0.0 | Black | null | null | null |
| 6074.0 | 0.0 | Orange_(colour) | null | null | null |
| 6076.0 | 0.0 | Cyan | null | null | null |
| 6077.0 | 0.0 | Black | null | null | null |
| 6078.0 | 0.0 | White | null | null | null |
| 6086.0 | 0.0 | Cauchy_sequence | null | null | null |
| 6087.0 | 0.0 | Nicolaus_Copernicus | null | null | null |
| 6089.0 | 0.0 | Creationism | null | null | null |
| 6098.0 | 0.0 | Carolingian_Renaissance | null | null | null |
| 6142.0 | 0.0 | Cardinal_number | null | null | null |
| 6150.0 | 0.0 | Blanching_(cooking) | null | null | null |
| 6178.0 | 0.0 | Cardinal | null | null | null |
| 6179.0 | 0.0 | Buddhist_cuisine | null | null | null |
| 6190.0 | 0.0 | Five-spice_powder | null | null | null |
| 6196.0 | 0.0 | Self-replicating_machine | null | null | null |
| 6197.0 | 0.0 | Self-replicating_machine | null | null | null |
| 6202.0 | 0.0 | London_Convention_on_the_Prevention_of_Marine_Pollution_by_Dumping_of_Wastes_and_Other_Matter | null | null | null |
| 6204.0 | 0.0 | Ramsar_Convention | null | null | null |
| 6219.0 | 0.0 | Claudio_Monteverdi | null | null | null |
| 6223.0 | 0.0 | Comics | null | null | null |
| 6228.0 | 0.0 | List_of_ancient_Celtic_peoples_and_tribes | null | null | null |
| 6236.0 | 0.0 | Champagne_socialist | null | null | null |
| 6240.0 | 0.0 | Celtic_languages | null | null | null |
| 6242.0 | 0.0 | Glossary_of_climbing_terms | null | on-sight | null |
| 6243.0 | 0.0 | Cascade_Range | null | null | null |
| 6263.0 | 0.0 | Charles_Darwin | null | null | null |
| 6266.0 | 0.0 | Climate_change | null | null | null |
| 6269.0 | 0.0 | Wipe_(transition) | null | null | null |
| 6278.0 | 0.0 | Banach_space | null | null | null |
| 6287.0 | 0.0 | Lists_of_cities_by_country | null | null | null |
| 6302.0 | 0.0 | Classical_element | null | null | null |
| 6307.0 | 0.0 | Aether_(classical_element) | null | null | null |
| 6311.0 | 0.0 | College_football | null | null | null |
| 6345.0 | 0.0 | Central_dogma_of_molecular_biology | null | null | null |
| 6348.0 | 0.0 | Medal_of_Honor | null | null | null |
| 6368.0 | 0.0 | Chōshū | null | null | null |
| 6453.0 | 2.0 | ClaudineChionh | null | null | null |
| 6461.0 | 0.0 | Wuxing_(Chinese_philosophy) | null | null | null |
| 6464.0 | 0.0 | Mobile_phone | null | null | null |
| 6470.0 | 0.0 | Computational_linguistics | null | null | null |
| 6500.0 | 0.0 | Lists_of_universities_and_colleges | null | null | null |
| 6502.0 | 0.0 | Clean_Air_Act_(United_States) | null | null | null |
| 6510.0 | 0.0 | Color_space | null | null | null |
| 6515.0 | 0.0 | Lists_of_atheists | null | null | null |
| 6522.0 | 0.0 | Chief_executive_officer | null | null | null |
| 6524.0 | 0.0 | Clam_dip | null | null | null |
| 6531.0 | 0.0 | Chinese_cuisine | null | null | null |
| 6553.0 | 0.0 | Context-free_grammar | null | null | null |
| 6554.0 | 0.0 | Computer_graphics | null | null | null |
| 6564.0 | 0.0 | Conjunction_elimination | null | null | null |
| 6573.0 | 0.0 | Widewuto | null | null | null |
| 6581.0 | 0.0 | Musique_concrète | null | null | null |
| 6594.0 | 0.0 | Casimir_IV_Jagiellon | null | null | null |
| 6595.0 | 0.0 | Computer_vision | null | null | null |
| 6605.0 | 0.0 | Citric_acid_cycle | null | null | null |
| 6609.0 | 0.0 | Stork | null | null | null |
| 6622.0 | 0.0 | Coelenterata | null | null | null |
| 6625.0 | 0.0 | Catholic_Church | null | null | null |
| 6646.0 | 0.0 | List_of_ancient_Germanic_peoples | null | null | null |
| 6657.0 | 0.0 | Catholic_Church | null | null | null |
| 6668.0 | 0.0 | Mousse | null | null | null |
Next, let us check that we got all the data, and there are no corrupted records:
readFromCSV.createOrReplaceTempView("redirects")
SELECT * FROM redirects WHERE _corrupt_record IS NOT NULL
| rd_from | rd_namespace | rd_title | rd_interwiki | rd_fragment | _corrupt_record |
|---|---|---|---|---|---|
| null | null | null | null | null | 7),11' |
A single bad row seems fine, we can just drop that one with no harm done.
Let us now write this to the Delta Lake, having filtered out all the bad rows and irrelevant rows, and dropped the columns we don't need. In particular, we remove all redirects to non-main-Wikipedia articles and non-English Wiki articles. (There is exactly one redirect to an article on a different Wiki, and that's on a user talk page.)
SELECT rd_from, rd_title FROM redirects WHERE (rd_from IS NOT NULL) AND (rd_namespace = 0) AND (rd_title IS NOT NULL) AND (rd_interwiki IS NULL) AND (_corrupt_record IS NULL)
| rd_from | rd_title |
|---|---|
| 10.0 | Computer_accessibility |
| 13.0 | History_of_Afghanistan |
| 14.0 | Geography_of_Afghanistan |
| 15.0 | Demographics_of_Afghanistan |
| 18.0 | Communications_in_Afghanistan |
| 19.0 | Transport_in_Afghanistan |
| 20.0 | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan |
| 21.0 | Foreign_relations_of_Afghanistan |
| 23.0 | Assistive_technology |
| 24.0 | Amoeba |
| 25.0 | Autism_spectrum |
| 27.0 | History_of_Albania |
| 29.0 | Demographics_of_Albania |
| 30.0 | As_We_May_Think |
| 35.0 | Politics_of_Albania |
| 36.0 | Economy_of_Albania |
| 40.0 | Afroasiatic_languages |
| 42.0 | Constructed_language |
| 46.0 | Abacus |
| 47.0 | Abalone |
| 48.0 | Abbadid_dynasty |
| 49.0 | Abbess |
| 50.0 | Abbeville |
| 51.0 | Abbey |
| 52.0 | Abbot |
| 53.0 | Abbreviation |
| 54.0 | Atlas_Shrugged |
| 56.0 | Constructed_language |
| 58.0 | List_of_Atlas_Shrugged_characters |
| 59.0 | Atlas_Shrugged |
| 60.0 | Atlas_Shrugged |
| 241.0 | African_Americans |
| 242.0 | Adolf_Hitler |
| 247.0 | Abecedarian |
| 248.0 | Cain_and_Abel |
| 249.0 | Abensberg |
| 251.0 | Aberdeen,_South_Dakota |
| 254.0 | Arthur_Koestler |
| 255.0 | Ayn_Rand |
| 256.0 | Alexander_the_Great |
| 258.0 | Anchorage,_Alaska |
| 259.0 | Logical_form |
| 260.0 | Existence_of_God |
| 263.0 | Anarchy |
| 264.0 | ASCII_art |
| 269.0 | Academy_Awards |
| 270.0 | Academy_Award_for_Best_Picture |
| 271.0 | Austrian_German |
| 272.0 | Elitism |
| 274.0 | Axiom_of_choice |
| 276.0 | American_football |
| 278.0 | United_States |
| 279.0 | Anna_Kournikova |
| 280.0 | Andorra |
| 287.0 | Austroasiatic_languages |
| 289.0 | Lists_of_actors |
| 291.0 | Anarcho-capitalism |
| 293.0 | Anarcho-capitalism |
| 296.0 | Lists_of_actors |
| 299.0 | An_American_in_Paris |
| 301.0 | Automorphism |
| 302.0 | Action_film |
| 304.0 | Africa |
| 306.0 | Statistics |
| 325.0 | Action_film |
| 338.0 | Auto_racing |
| 347.0 | Demographics_of_Algeria |
| 353.0 | Foreign_relations_of_Algeria |
| 369.0 | Atlas_Shrugged |
| 583.0 | Amoeba |
| 589.0 | Ashmore_and_Cartier_Islands |
| 596.0 | Artificial_language |
| 598.0 | Afroasiatic_languages |
| 609.0 | Foreign_relations_of_Andorra |
| 617.0 | Al_Gore |
| 618.0 | An_Enquiry_Concerning_Human_Understanding |
| 622.0 | Al_Gore |
| 626.0 | Auteur |
| 629.0 | Abstract_algebra |
| 635.0 | Analysis_of_variance |
| 644.0 | Arithmetic_logic_unit |
| 648.0 | Actor |
| 654.0 | Computer_accessibility |
| 668.0 | Logical_form |
| 669.0 | Allotropy |
| 686.0 | Amalthea_(mythology) |
| 687.0 | Analysis_of_variance |
| 693.0 | Broch |
| 696.0 | AA |
| 727.0 | History_of_astronomy |
| 731.0 | History_of_astronomy |
| 735.0 | Al_Gore |
| 743.0 | Antigua_and_Barbuda |
| 749.0 | Astronomer |
| 755.0 | History_of_Albania |
| 758.0 | Foreign_relations_of_Albania |
| 759.0 | Demographics_of_Albania |
| 763.0 | Foreign_relations_of_Albania |
| 767.0 | A._E._van_Vogt |
| 807.0 | Telecommunications_in_Albania |
| 813.0 | History_of_Afghanistan |
| 814.0 | Geography_of_Afghanistan |
| 815.0 | Government_of_the_Islamic_Emirate_of_Afghanistan |
| 816.0 | Demographics_of_Afghanistan |
| 817.0 | Economy_of_Afghanistan |
| 818.0 | Communications_in_Afghanistan |
| 820.0 | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan |
| 821.0 | Foreign_relations_of_Afghanistan |
| 822.0 | Afghanistan |
| 832.0 | Foreign_relations_of_Austria |
| 839.0 | Anglicanism |
| 855.0 | Abiotic_component |
| 858.0 | Au |
| 860.0 | Åland |
| 873.0 | Civilization |
| 882.0 | Supermajority |
| 891.0 | Accounting |
| 907.0 | AWK |
| 908.0 | Nomic |
| 918.0 | Antisemitism |
| 919.0 | Antisemitism |
| 923.0 | A._A._Milne |
| 926.0 | Alumni |
| 935.0 | Automated_Alice |
| 936.0 | Automated_Alice |
| 937.0 | Automated_Alice |
| 938.0 | Automated_Alice |
| 939.0 | Automated_Alice |
| 940.0 | Automated_Alice |
| 941.0 | Automated_Alice |
| 942.0 | Automated_Alice |
| 943.0 | Automated_Alice |
| 944.0 | Automated_Alice |
| 945.0 | Automated_Alice |
| 946.0 | Automated_Alice |
| 959.0 | Voiced_velar_nasal |
| 963.0 | Existence_of_God |
| 970.0 | Ambient_calculus |
| 972.0 | Necronomicon |
| 973.0 | A_priori_and_a_posteriori |
| 975.0 | Ambient_calculus |
| 982.0 | A_priori_and_a_posteriori |
| 1026.0 | Anarcho-capitalism |
| 1035.0 | AAL |
| 1059.0 | Statistics |
| 1061.0 | Analysis_of_variance |
| 1062.0 | Analysis_of_variance |
| 1075.0 | Foreign_relations_of_Antigua_and_Barbuda |
| 1083.0 | Demographics_of_Azerbaijan |
| 1085.0 | Telecommunications_in_Azerbaijan |
| 1089.0 | Foreign_relations_of_Azerbaijan |
| 1105.0 | Foreign_relations_of_Argentina |
| 1108.0 | Foreign_relations_of_Argentina |
| 1109.0 | American_Samoa |
| 1114.0 | American_Samoa |
| 1116.0 | American_Samoa |
| 1123.0 | Foreign_relations_of_Australia |
| 1151.0 | AK-47 |
| 1153.0 | Amhrán_na_bhFiann |
| 1186.0 | Aphex_Twin |
| 1189.0 | Creed |
| 1190.0 | Alternate_history |
| 1195.0 | Allotropy |
| 1199.0 | Angles |
| 1205.0 | Atomic_orbital |
| 1220.0 | Anguilla |
| 1221.0 | Anguilla |
| 1228.0 | Ashmore_and_Cartier_Islands |
| 1229.0 | Ashmore_and_Cartier_Islands |
| 1230.0 | Ashmore_and_Cartier_Islands |
| 1231.0 | Ashmore_and_Cartier_Islands |
| 1232.0 | Ashmore_and_Cartier_Islands |
| 1233.0 | Ashmore_and_Cartier_Islands |
| 1238.0 | Nuclear_weapon |
| 1245.0 | Alpha_particle |
| 1246.0 | Alfonso_Arau |
| 1255.0 | Astronomical_unit |
| 1262.0 | Cant_(language) |
| 1268.0 | Artificial_intelligence |
| 1276.0 | Antarctica |
| 1277.0 | Antarctic_Treaty_System |
| 1280.0 | Military_activity_in_the_Antarctic |
| 1290.0 | Antarctic_Treaty_System |
| 1292.0 | Algernon_Charles_Swinburne |
| 1295.0 | American_League_Championship_Series |
| 1297.0 | Hebrew_Bible |
| 1299.0 | Abbadid_dynasty |
| 1302.0 | Abdomen |
| 1311.0 | Ada_Lovelace |
| 1312.0 | Augustine_of_Hippo |
| 1321.0 | Sagrada_Família |
| 1328.0 | Anno_Domini |
| 1339.0 | Americans_with_Disabilities_Act_of_1990 |
| 1340.0 | Americans_with_Disabilities_Act_of_1990 |
| 1341.0 | Americans_with_Disabilities_Act_of_1990 |
| 1342.0 | Anno_Domini |
| 1345.0 | Apache_HTTP_Server |
| 1355.0 | Anderitum |
| 1399.0 | Attention_deficit_hyperactivity_disorder |
| 1406.0 | Amine |
| 1407.0 | Antonie_van_Leeuwenhoek |
| 1410.0 | Antonie_van_Leeuwenhoek |
| 1415.0 | Pope_Adrian_I |
| 1426.0 | Pope_Adrian_II |
| 1429.0 | Pope_Adrian_IV |
| 1434.0 | Abgar_V |
| 1457.0 | Alzheimer's_disease |
| 1459.0 | Vitamin_C |
| 1476.0 | Prime_Minister_of_Australia |
| 1502.0 | List_of_minor_characters_in_the_Alice_series |
| 1511.0 | Albert_I_of_Germany |
| 1515.0 | Albert_III,_Duke_of_Saxony |
| 1516.0 | Albert_II,_Margrave_of_Meissen |
| 1517.0 | Albert_of_Aix |
| 1533.0 | Aachen |
| 1535.0 | Acorn |
| 1539.0 | Adirondack_Mountains |
| 1561.0 | Áedán_mac_Gabráin |
| 1572.0 | Al-Battani |
| 1609.0 | Pope_Alexander_VI |
| 1610.0 | Pope_Alexander_VII |
| 1611.0 | Pope_Alexander_VIII |
| 1626.0 | Aleksandr_Solzhenitsyn |
| 1636.0 | Antoine_de_Saint-Exupéry |
| 1641.0 | Alfred,_Duke_of_Saxe-Coburg_and_Gotha |
| 1651.0 | Alfred_of_Beverley |
| 1672.0 | Alfonso_VIII_of_Castile |
| 1673.0 | Alfonso_IX_of_León |
| 1678.0 | Alfonso_de_Cartagena |
| 1682.0 | Ahmose_I |
| 1699.0 | Alfonso_VI_of_León_and_Castile |
| 1703.0 | Alfonso_VII_of_León_and_Castile |
| 1704.0 | Alfonso_VIII_of_Castile |
| 1705.0 | Alfonso_IX_of_León |
| 1706.0 | Alfonso_X_of_Castile |
| 1707.0 | Alfonso_XI_of_Castile |
| 1708.0 | Alfonso_XII |
| 1709.0 | Alfonso_XIII |
| 1733.0 | Anacreon |
| 1744.0 | Pope_Anastasius_III |
| 1745.0 | Pope_Anastasius_IV |
| 1766.0 | Asteroid_belt |
| 1768.0 | Alice |
| 1769.0 | An_Enquiry_Concerning_Human_Understanding |
| 1771.0 | Apollo_program |
| 1772.0 | Arthritis |
| 1775.0 | Discrete_mathematics |
| 1809.0 | Thomas_Aquinas |
| 1811.0 | Hydrolysis |
| 1821.0 | Antoine_Lavoisier |
| 1824.0 | Footage |
| 1830.0 | Air_pollution |
| 1831.0 | Protocol_on_Environmental_Protection_to_the_Antarctic_Treaty |
| 1833.0 | Americentrism |
| 1838.0 | Amazon_River |
| 1852.0 | Ancient_Greece |
| 1855.0 | History_of_Africa |
| 1858.0 | Aromatic_compound |
| 1876.0 | Adémar_de_Chabannes |
| 1877.0 | Catharism |
| 1885.0 | Erotic_asphyxiation |
| 1889.0 | Assault_weapons_ban |
| 1903.0 | American_Airlines_Flight_77 |
| 1904.0 | American_Airlines_Flight_11 |
| 1906.0 | Aberration_(astronomy) |
| 1936.0 | Astronomical_unit |
| 1952.0 | Industry_Standard_Architecture |
| 1959.0 | Telephone_exchange |
| 1972.0 | Aviation |
| 1976.0 | Adomnán |
| 1978.0 | Assassin_(disambiguation) |
| 1982.0 | Alice |
| 1984.0 | Arab_world |
| 1993.0 | Alan_Ayckbourn |
| 2001.0 | Al-Qaeda |
| 2002.0 | Argumentum_ad_populum |
| 2005.0 | Addiction |
| 2008.0 | Al-Qaeda |
| 2043.0 | Anti-Americanism |
| 2050.0 | Archaeology |
| 2051.0 | Anarchism |
| 2058.0 | Atheism |
| 2071.0 | Afro_Celt_Sound_System |
| 2073.0 | Andrew_Jackson |
| 2074.0 | Andrew_Jackson |
| 2079.0 | Autumnal_equinox |
| 2090.0 | Albert_of_Hohenzollern |
| 2095.0 | Parapsychology |
| 2128.0 | Los_Angeles_Angels |
| 2132.0 | Ara_Pacis |
| 2145.0 | Catharism |
| 2146.0 | Aleksandr_Solzhenitsyn |
| 2149.0 | Armour |
| 2153.0 | Elitism |
| 2164.0 | Peremptory_plea |
| 2165.0 | Peremptory_plea |
| 2188.0 | Accident_(philosophy) |
| 2190.0 | Alternate_history |
| 2203.0 | Religion_in_Poland |
| 2206.0 | Ampere |
| 2211.0 | Folklore_of_the_United_States |
| 2213.0 | Modus_ponens |
| 2220.0 | Acts_of_the_Apostles |
| 2223.0 | Slaughterhouse |
| 2227.0 | Argumentum_a_fortiori |
| 2228.0 | Ad_hominem |
| 2249.0 | Amplification |
| 2258.0 | Anglicanism |
| 2260.0 | Analog_Science_Fiction_and_Fact |
| 2261.0 | Analog_Science_Fiction_and_Fact |
| 2262.0 | Analog_Science_Fiction_and_Fact |
| 2264.0 | Heptarchy |
| 2269.0 | Asynchronous_Transfer_Mode |
| 2271.0 | Asymmetric_digital_subscriber_line |
| 2280.0 | Giant_panda |
| 2281.0 | Arctic_fox |
| 2285.0 | Tank_destroyer |
| 2290.0 | Indigenous_peoples |
| 2295.0 | Arhat |
| 2297.0 | Springbok |
| 2298.0 | Blue_crane |
| 2302.0 | Aramaic |
| 2306.0 | AT&T |
| 2320.0 | Audio_codec |
| 2324.0 | All_Saints'_Day |
| 2351.0 | HIV/AIDS |
| 2354.0 | Outline_of_archaeology |
| 2367.0 | HIV/AIDS |
| 2379.0 | Binary_relation |
| 2404.0 | Aon_(company) |
| 2419.0 | Alloy |
| 2432.0 | Albrecht_III_Achilles,_Elector_of_Brandenburg |
| 2446.0 | Appalachian_dulcimer |
| 2462.0 | Anti-globalization_movement |
| 2464.0 | Anti-globalization_movement |
| 2468.0 | Aaron's_rod |
| 2469.0 | AB |
| 2478.0 | Barada |
| 2479.0 | Manama |
| 2486.0 | Chrysoberyl |
| 2489.0 | Abandon |
| 2492.0 | Anal_sex |
| 2495.0 | Aurochs |
| 2496.0 | Etiology |
| 2520.0 | Addition |
| 2523.0 | Alien |
| 2525.0 | Al_Jazeera |
| 2527.0 | Ruhollah_Khomeini |
| 2533.0 | Alphorn |
| 2535.0 | AW |
| 2537.0 | Analog_Science_Fiction_and_Fact |
| 2549.0 | Analog_Science_Fiction_and_Fact |
| 2561.0 | List_of_federal_political_scandals_in_the_United_States |
| 2565.0 | Albert,_Duke_of_Prussia |
| 2567.0 | Academy_Awards |
| 2568.0 | Apsis |
| 2569.0 | Apsis |
| 2571.0 | Rope_(film) |
| 2572.0 | Arianism |
| 2595.0 | Atlas_(computer) |
| 2599.0 | AA |
| 2600.0 | Aaron's_rod |
| 2601.0 | Abandon |
| 2603.0 | Abaris_the_Hyperborean |
| 2612.0 | Abbo_of_Fleury |
| 2615.0 | Charles_Farrar_Browne |
| 2631.0 | Ælfric |
| 2636.0 | Accounting |
| 2638.0 | ACID |
| 2643.0 | Ajax_the_Lesser |
| 2644.0 | Ajax_the_Great |
| 2647.0 | American_Indians |
| 2648.0 | Abandon |
| 2649.0 | Abandonment_(legal) |
| 2650.0 | Abandonment_(legal) |
| 2651.0 | Abandonment_(legal) |
| 2652.0 | Nuisance_abatement |
| 2653.0 | Abatement |
| 2655.0 | Abatement |
| 2656.0 | Abatement |
| 2657.0 | Abatement |
| 2658.0 | Abatement_(heraldry) |
| 2659.0 | American_Revolutionary_War |
| 2664.0 | Affirmation_(law) |
| 2675.0 | Abd_al-Rahman |
| 2682.0 | Abdul_Qadir |
| 2683.0 | Abdelaziz_of_Morocco |
| 2688.0 | Pneumatic_motor |
| 2697.0 | Abraham_ibn_Ezra |
| 2711.0 | Aberdeenshire_(historic) |
| 2713.0 | Aberdyfi |
| 2725.0 | Aesthetics |
| 2746.0 | Same-sex_relationship |
| 2751.0 | The_Angry_Brigade |
| 2760.0 | Arab_(disambiguation) |
| 2765.0 | Anatomical_Therapeutic_Chemical_Classification_System |
| 2768.0 | Antiarrhythmic_agent |
| 2771.0 | Air_conditioning |
| 2774.0 | Alfred_Kinsey |
| 2775.0 | Auto_racing |
| 2776.0 | Antisemitism |
| 2789.0 | James_Tiptree_Jr. |
| 2793.0 | Application_software |
| 2804.0 | Application_firewall |
| 2808.0 | Nuclear_weapon |
| 2821.0 | Set_theory |
| 2828.0 | Abipón |
| 2831.0 | Abkhazia |
| 2842.0 | Bohr_model |
| 2855.0 | Latin_American_Integration_Association |
| 2863.0 | AT&T |
| 2872.0 | Arthur,_Prince_of_Wales |
| 2880.0 | Anti-ballistic_missile |
| 2888.0 | Amorphous_solid |
| 2897.0 | Indigenous_peoples_of_Arizona |
| 2898.0 | Abdul_Rashid_Dostum |
| 2903.0 | The_Diary_of_a_Young_Girl |
| 2904.0 | Kabylia |
| 2912.0 | Archaeoastronomy |
| 2914.0 | French_hip_hop |
| 2915.0 | Gh_hip_hop |
| 2918.0 | Argument_from_ignorance |
| 2922.0 | AIM_(software) |
| 2929.0 | Armillary_sphere |
| 2937.0 | Algemeen_Nijmeegs_Studentenblad |
| 2951.0 | Louis_Althusser |
| 2969.0 | Aurora |
| 2970.0 | Aurora |
| 2971.0 | Abstraction_(computer_science) |
| 2977.0 | American_Sign_Language |
| 2993.0 | Amputation |
| 2996.0 | HMS_Ark_Royal |
| 2998.0 | Acceleration |
| 3000.0 | AD_Police_Files |
| 3005.0 | Apadravya |
| 3006.0 | Ampallang |
| 3008.0 | Albinism |
| 3009.0 | Analcime |
| 3023.0 | Archimedes'_screw |
| 3024.0 | Multiplication |
| 3033.0 | Antenna_(radio) |
| 3039.0 | Shadrach,_Meshach,_and_Abednego |
| 3041.0 | Acanthocephala |
| 3042.0 | Alcobaça |
| 3051.0 | Clan_McDuck |
| 3057.0 | List_of_Donald_Duck_universe_characters |
| 3059.0 | Athlon |
| 3062.0 | Duck_family_(Disney) |
| 3063.0 | Asperger_syndrome |
| 3066.0 | Authoritarianism |
| 3086.0 | İskenderun |
| 3099.0 | AbiWord |
| 3106.0 | AirPort |
| 3114.0 | Amiga_500 |
| 3126.0 | Ahriman |
| 3136.0 | Concept |
| 3139.0 | Apostle_(disambiguation) |
| 3154.0 | Fairchild_Republic_A-10_Thunderbolt_II |
| 3156.0 | Albrecht_Dürer |
| 3163.0 | Anthroposophy |
| 3164.0 | Evidence_of_common_descent |
| 3166.0 | A.C._Milan |
| 3180.0 | Anomaly |
| 3182.0 | Avenger |
| 3187.0 | Agglutination |
| 3190.0 | Ascending_chain_condition |
| 3197.0 | A._E._Housman |
| 3208.0 | Antidepressant |
| 3210.0 | Alexander_Rutskoy |
| 3215.0 | Multivibrator |
| 3219.0 | Actor |
| 3220.0 | Artificial_intelligence |
| 3223.0 | Ai |
| 3227.0 | Azores |
| 3230.0 | Relative_atomic_mass |
| 3232.0 | Anthropic_principle |
| 3247.0 | Roman_Catholic_Archdiocese_for_the_Military_Services,_USA |
| 3248.0 | Archaeopteryx |
| 3254.0 | Amuck! |
| 3260.0 | Line_Islands |
| 3264.0 | Aborigine |
| 3276.0 | Antiterrorism_and_Effective_Death_Penalty_Act_of_1996 |
| 3280.0 | Bomis |
| 3281.0 | Biblical_hermeneutics |
| 3282.0 | Baltic_Sea |
| 3283.0 | Ballroom_dance |
| 3284.0 | Biology |
| 3288.0 | Bill_Clinton |
| 3290.0 | Biblical_canon |
| 3298.0 | The_Buddha |
| 3299.0 | Bijection,_injection_and_surjection |
| 3300.0 | Buddhism |
| 3303.0 | Baltimore_Ravens |
| 3307.0 | Aaron |
| 3311.0 | List_of_business_schools_in_Asia |
| 3317.0 | The_Birth_of_a_Nation |
| 3318.0 | Boethius |
| 3320.0 | Mental_event |
| 3322.0 | Business_school |
| 3323.0 | Britney_Spears |
| 3326.0 | Baby_One_More_Time |
| 3327.0 | Binomial_distribution |
| 3329.0 | Binomial_distribution |
| 3330.0 | Biochemistry |
| 3342.0 | Germany |
| 3344.0 | Basic |
| 3346.0 | Robert_Byrd |
| 3349.0 | Business_school |
| 3366.0 | Commonwealth_of_Nations |
| 3369.0 | Board_game |
| 3373.0 | Outline_of_biology |
| 3407.0 | Baruch_Spinoza |
| 3409.0 | Ontology |
| 3413.0 | Batch_processing |
| 3418.0 | Basil |
| 3424.0 | BBC_Radio_1 |
| 3425.0 | BBC_Online |
| 3433.0 | Visual_impairment |
| 3445.0 | Alcohol_intoxication |
| 3448.0 | Steer_wrestling |
| 3480.0 | Royal_Bahamas_Defence_Force |
| 3481.0 | Foreign_relations_of_the_Bahamas |
| 3484.0 | Bahrain |
| 3492.0 | Baker_Island |
| 3493.0 | Baker_Island |
| 3494.0 | Baker_Island |
| 3496.0 | Baker_Island |
| 3509.0 | Foreign_relations_of_Bangladesh |
| 3510.0 | Foreign_relations_of_Bangladesh |
| 3519.0 | Foreign_relations_of_Barbados |
| 3522.0 | Bassas_da_India |
| 3524.0 | Bassas_da_India |
| 3527.0 | Bassas_da_India |
| 3529.0 | Bassas_da_India |
| 3539.0 | Telecommunications_in_Belarus |
| 3548.0 | Foreign_relations_of_Belgium |
| 3549.0 | Belgium |
| 3550.0 | Foreign_relations_of_Belgium |
| 3551.0 | Belgium |
| 3578.0 | Bermuda |
| 3587.0 | Bhutan |
| 3600.0 | Cultural_depictions_of_blindness |
| 3619.0 | Botswana_Defence_Force |
| 3622.0 | Bouvet_Island |
| 3623.0 | Bouvet_Island |
| 3624.0 | Bouvet_Island |
| 3625.0 | Bouvet_Island |
| 3626.0 | Bouvet_Island |
| 3627.0 | Bouvet_Island |
| 3628.0 | Bouvet_Island |
| 3640.0 | British_Indian_Ocean_Territory |
| 3641.0 | British_Indian_Ocean_Territory |
| 3642.0 | British_Indian_Ocean_Territory |
| 3643.0 | British_Indian_Ocean_Territory |
| 3644.0 | British_Indian_Ocean_Territory |
| 3645.0 | British_Indian_Ocean_Territory |
| 3646.0 | British_Indian_Ocean_Territory |
| 3647.0 | British_Indian_Ocean_Territory |
| 3656.0 | British_Virgin_Islands |
| 3686.0 | Geography_of_Myanmar |
| 3689.0 | Economy_of_Myanmar |
| 3690.0 | Telecommunications_in_Myanmar |
| 3723.0 | BSE |
| 3726.0 | Breakdancing |
| 3732.0 | Bhangra |
| 3737.0 | Baptists |
| 3739.0 | BSD_licenses |
| 3762.0 | Länder |
| 3763.0 | Bavaria |
| 3767.0 | Bundeskanzler |
| 3770.0 | Cabinet_of_Germany |
| 3773.0 | Der_Blaue_Reiter |
| 3781.0 | Mumbai |
| 3790.0 | Bodybuilding |
| 3791.0 | Bryan_MacLean |
| 3796.0 | Biblical_canon |
| 3803.0 | Strike_zone |
| 3804.0 | Slugging_percentage |
| 3818.0 | Babel_fish |
| 3820.0 | Mental_event |
| 3824.0 | Babel_fish |
| 3830.0 | Bryce_Canyon_National_Park |
| 3831.0 | Encyclopædia_Britannica |
| 3847.0 | Taste |
| 3855.0 | Origins_of_baseball |
| 3871.0 | Substance_theory |
| 3879.0 | Statistics |
| 3913.0 | Binary_operation |
| 3920.0 | The_Beatles |
| 3922.0 | Road_bicycle |
| 3934.0 | Baby_boom |
| 3935.0 | Buddhism |
| 3966.0 | Border_Gateway_Protocol |
| 3972.0 | Cycling |
| 3991.0 | BITS |
| 3994.0 | Benoit_Mandelbrot |
| 4003.0 | Pierre_Beaumarchais |
| 4014.0 | Bipolar_disorder |
| 4021.0 | Common_Era |
| 4022.0 | Common_Era |
| 4025.0 | BC |
| 4026.0 | Buckminster_Fuller |
| 4034.0 | Encyclopædia_Britannica_Eleventh_Edition |
| 4038.0 | Banach–Tarski_paradox |
| 4040.0 | BC |
| 4090.0 | Bitwise_operation |
| 4105.0 | Outline_of_biochemistry |
| 4122.0 | B-roll |
| 4126.0 | Ballroom_dance |
| 4129.0 | CIM-10_Bomarc |
| 4151.0 | Brainfuck |
| 4167.0 | Utility_knife |
| 4174.0 | Six_Degrees_of_Kevin_Bacon |
| 4186.0 | Bacteriostatic_agent |
| 4201.0 | Francesco_Borromini |
| 4212.0 | Bolsheviks |
| 4215.0 | Brian_De_Palma |
| 4221.0 | North_American_B-25_Mitchell |
| 4222.0 | Berry_Berenson |
| 4226.0 | Brewster's_angle |
| 4238.0 | The_Bronx |
| 4252.0 | Baháʼí_Faith |
| 4253.0 | Red_Army_Faction |
| 4265.0 | Titius–Bode_law |
| 4268.0 | The_Boston_Globe |
| 4272.0 | Elbląg |
| 4273.0 | Elbląg |
| 4275.0 | Gdańsk |
| 4276.0 | Oder |
| 4290.0 | Buddhism |
| 4291.0 | Buddhism |
| 4303.0 | University_of_Brighton |
| 4328.0 | Bohemia |
| 4336.0 | Bosnia_and_Herzegovina |
| 4412.0 | Binary_Synchronous_Communications |
| 4415.0 | ETA_(separatist_group) |
| 4426.0 | Brownian_motion |
| 4428.0 | Bacillus_thuringiensis |
| 4435.0 | Baltic_languages |
| 4439.0 | Baptists |
| 4464.0 | Book_of_Zechariah |
| 4466.0 | Black_Sox_Scandal |
| 4486.0 | Buckminsterfullerene |
| 4509.0 | GNU_Free_Documentation_License |
| 4521.0 | Bubble_sort |
| 4523.0 | Bipolar_disorder |
| 4530.0 | Blue_screen |
| 4562.0 | Pub |
| 4564.0 | Bitter_(beer) |
| 4586.0 | Greek_fire |
| 4590.0 | Brachycephaly |
| 4593.0 | Battleship_(game) |
| 4597.0 | Beryl |
| 4599.0 | Boleslaus_I |
| 4600.0 | Bolesław_III_Wrymouth |
| 4605.0 | Battle_of_the_Nile |
| 4612.0 | Bird |
| 4623.0 | Great_Britain_and_Ireland |
| 4632.0 | Monarchy_of_the_United_Kingdom |
| 4634.0 | Bombardier |
| 4655.0 | Alliance_90/The_Greens |
| 4656.0 | Shogun |
| 4657.0 | Arbitration |
| 4663.0 | Basil_of_Caesarea |
| 4666.0 | C*-algebra |
| 4678.0 | Computer_font |
| 4696.0 | Prime_Minister_of_the_United_Kingdom |
| 4697.0 | List_of_United_Kingdom_general_elections |
| 4703.0 | Bob_Dylan |
| 4716.0 | Bohemia |
| 4720.0 | Epistle_to_the_Hebrews |
| 4740.0 | International_Bureau_of_Weights_and_Measures |
| 4747.0 | Blu_Tack |
| 4750.0 | Bodhidharma |
| 4773.0 | Balfour_Declaration |
| 4784.0 | Normal_distribution |
| 4790.0 | German_Navy |
| 4798.0 | Bronze_Age |
| 4799.0 | Bicameral_mentality |
| 4808.0 | Arbitrary-precision_arithmetic |
| 4812.0 | Battle_of_Świecino |
| 4830.0 | Bohr_model |
| 4837.0 | Befehlshaber_der_U-Boote |
| 4844.0 | Symmetry_in_biology |
| 4846.0 | Symmetry_in_biology |
| 4853.0 | Wrocław |
| 4855.0 | Basso_continuo |
| 4889.0 | Semi-trailer_truck |
| 4891.0 | Ballet |
| 4901.0 | Daiquiri |
| 4903.0 | Boson |
| 4919.0 | Bipolar_II_disorder |
| 4920.0 | October_Revolution |
| 4923.0 | List_of_Bubblegum_Crisis_characters |
| 4932.0 | Basal_body_temperature |
| 4938.0 | Branch_predictor |
| 4939.0 | Gambling |
| 4954.0 | Battle_of_Świecino |
| 4962.0 | Batting_average_(baseball) |
| 4977.0 | Battle_of_Adrianople |
| 4984.0 | Battle_of_Adrianople |
| 4985.0 | Battle_of_the_Ardennes |
| 4998.0 | Operation_Aphrodite |
| 5010.0 | Mexican_tetra |
| 5012.0 | The_Adventures_of_Brisco_County,_Jr. |
| 5017.0 | The_Book_of_Counted_Sorrows |
| 5018.0 | Anal_sex |
| 5022.0 | B._F._Skinner |
| 5044.0 | Beast_of_Bodmin_Moor |
| 5054.0 | List_of_sovereign_states |
| 5055.0 | Computing |
| 5056.0 | Software |
| 5057.0 | Common_sense |
| 5058.0 | Celtic_music |
| 5060.0 | List_of_sovereign_states |
| 5061.0 | List_of_sovereign_states |
| 5062.0 | List_of_sovereign_states |
| 5063.0 | List_of_sovereign_states |
| 5064.0 | List_of_sovereign_states |
| 5065.0 | List_of_sovereign_states |
| 5066.0 | COBOL |
| 5067.0 | Christianity |
| 5068.0 | List_of_sovereign_states |
| 5069.0 | List_of_sovereign_states |
| 5070.0 | List_of_sovereign_states |
| 5071.0 | List_of_sovereign_states |
| 5072.0 | Country |
| 5073.0 | List_of_sovereign_states |
| 5074.0 | List_of_sovereign_states |
| 5075.0 | List_of_sovereign_states |
| 5076.0 | List_of_sovereign_states |
| 5077.0 | List_of_sovereign_states |
| 5078.0 | List_of_sovereign_states |
| 5079.0 | List_of_sovereign_states |
| 5080.0 | List_of_sovereign_states |
| 5081.0 | List_of_sovereign_states |
| 5082.0 | List_of_sovereign_states |
| 5085.0 | Berlin |
| 5088.0 | List_of_sovereign_states |
| 5089.0 | Cantor_set |
| 5093.0 | Cold_War |
| 5097.0 | Cryptography |
| 5098.0 | Cryptography |
| 5099.0 | Cryptanalysis |
| 5100.0 | Code |
| 5101.0 | Encryption |
| 5103.0 | Charleston |
| 5104.0 | Consequentialism |
| 5105.0 | On_the_Consolation_of_Philosophy |
| 5107.0 | Regress_argument |
| 5110.0 | Consciousness |
| 5112.0 | Charlie_Chaplin |
| 5115.0 | Khmer_language |
| 5120.0 | Chordate |
| 5121.0 | Combinatorics |
| 5122.0 | Constellation |
| 5123.0 | Cognitive_therapy |
| 5125.0 | Category_theory |
| 5126.0 | Summary_statistics |
| 5128.0 | Comedy_film |
| 5129.0 | Cult_film |
| 5130.0 | List_of_sovereign_states |
| 5133.0 | Charlize_Theron |
| 5137.0 | Cluster_sampling |
| 5138.0 | Cumulative_distribution_function |
| 5140.0 | Comedy_film |
| 5141.0 | Cult_film |
| 5143.0 | Cryptography |
| 5146.0 | Hash_function |
| 5149.0 | Computer_hardware |
| 5167.0 | Central_tendency |
| 5168.0 | Checkers |
| 5173.0 | Probability_distribution |
| 5181.0 | Continent |
| 5182.0 | Constitution |
| 5186.0 | List_of_sovereign_states |
| 5198.0 | Canadian_Armed_Forces |
| 5202.0 | List_of_cities_in_Canada |
| 5206.0 | Algorithmic_art |
| 5208.0 | List_of_sovereign_states |
| 5209.0 | The_World_Factbook |
| 5210.0 | C._S._Lewis |
| 5220.0 | Complex_number |
| 5227.0 | Chessboard |
| 5231.0 | Old_World_monkey |
| 5238.0 | List_of_sovereign_states |
| 5239.0 | Countable_set |
| 5242.0 | Ciliate |
| 5258.0 | Computer_data_storage |
| 5264.0 | Computer_monitor |
| 5283.0 | Cryptomonad |
| 5287.0 | Classical_music |
| 5289.0 | Card_game |
| 5290.0 | Casino_game |
| 5291.0 | PC_game |
| 5292.0 | Collectible_card_game |
| 5297.0 | Character_(computing) |
| 5303.0 | Conic_section |
| 5310.0 | Computer_hardware |
| 5318.0 | Time-sharing |
| 5319.0 | Computer_multitasking |
| 5341.0 | List_of_sovereign_states |
| 5343.0 | Constitution_of_Canada |
| 5345.0 | Colloid |
| 5356.0 | Cancer_cluster |
| 5359.0 | Collectible_card_game |
| 5365.0 | Ichthys |
| 5369.0 | Birth_control |
| 5392.0 | Coriander |
| 5393.0 | Coriander |
| 5396.0 | Chris_Morris |
| 5400.0 | List_of_sovereign_states |
| 5410.0 | Poales |
| 5414.0 | Wargame |
| 5418.0 | Capitalism |
| 5419.0 | Computer |
| 5423.0 | Cross-examination |
| 5425.0 | Class_conflict |
| 5426.0 | Compression |
| 5435.0 | Royal_Cambodian_Armed_Forces |
| 5441.0 | C_(programming_language) |
| 5442.0 | Constructed_language |
| 5444.0 | Regress_argument |
| 5445.0 | Class_conflict |
| 5457.0 | Civilization_(video_game) |
| 5476.0 | Cayman_Islands |
| 5501.0 | Christmas_Island |
| 5502.0 | Christmas_Island |
| 5503.0 | Christmas_Island |
| 5504.0 | Christmas_Island |
| 5505.0 | Christmas_Island |
| 5506.0 | Christmas_Island |
| 5507.0 | Christmas_Island |
| 5508.0 | Christmas_Island |
| 5511.0 | Clipperton_Island |
| 5512.0 | Clipperton_Island |
| 5513.0 | Clipperton_Island |
| 5514.0 | Clipperton_Island |
| 5515.0 | Clipperton_Island |
| 5516.0 | Clipperton_Island |
| 5517.0 | Clipperton_Island |
| 5518.0 | Clipperton_Island |
| 5521.0 | Cocos_(Keeling)_Islands |
| 5522.0 | Cocos_(Keeling)_Islands |
| 5524.0 | Cocos_(Keeling)_Islands |
| 5525.0 | Cocos_(Keeling)_Islands |
| 5526.0 | Cocos_(Keeling)_Islands |
| 5527.0 | Cocos_(Keeling)_Islands |
| 5528.0 | Cocos_(Keeling)_Islands |
| 5542.0 | Coral_Sea_Islands |
| 5543.0 | Coral_Sea_Islands |
| 5544.0 | Coral_Sea_Islands |
| 5545.0 | Coral_Sea_Islands |
| 5546.0 | Coral_Sea_Islands |
| 5547.0 | Coral_Sea_Islands |
| 5548.0 | Coral_Sea_Islands |
| 5549.0 | Coral_Sea_Islands |
| 5601.0 | Cypriot_National_Guard |
| 5604.0 | Czech_Republic |
| 5607.0 | Demographics_of_the_Czech_Republic |
| 5608.0 | Politics_of_the_Czech_Republic |
| 5612.0 | Army_of_the_Czech_Republic |
| 5613.0 | Foreign_relations_of_the_Czech_Republic |
| 5616.0 | Creutzfeldt–Jakob_disease |
| 5618.0 | A_Clockwork_Orange |
| 5620.0 | Stroke |
| 5628.0 | Compiler |
| 5631.0 | Gruyère_cheese |
| 5632.0 | Cheese_Shop_sketch |
| 5634.0 | List_of_decades,_centuries,_and_millennia |
| 5650.0 | Comet |
| 5652.0 | Computer_network |
| 5677.0 | Cerebrospinal_fluid |
| 5680.0 | Chief_executive_officer |
| 5683.0 | Trade_fair |
| 5687.0 | University_of_Cambridge |
| 5731.0 | Capitalism |
| 5737.0 | Cross-cutting |
| 5741.0 | Monetary_policy |
| 5746.0 | Hash_function |
| 5747.0 | Key_(cryptography) |
| 5753.0 | Sexual_intercourse |
| 5764.0 | Charlie_Chaplin |
| 5773.0 | Carroll_O'Connor |
| 5780.0 | Chaco_Culture_National_Historical_Park |
| 5788.0 | Cretaceous–Paleogene_extinction_event |
| 5792.0 | Probability_distribution |
| 5798.0 | Closeted |
| 5799.0 | Coming_out |
| 5801.0 | Ecumenical_council |
| 5802.0 | Council_of_Trent |
| 5803.0 | Second_Vatican_Council |
| 5842.0 | Foreign_relations_of_Colombia |
| 5852.0 | Foreign_relations_of_the_Czech_Republic |
| 5856.0 | Holy_Roman_Empire |
| 5870.0 | Comics |
| 5871.0 | Tachycardia |
| 5875.0 | Jargon |
| 5877.0 | CORAL |
| 5880.0 | Comment_(computer_programming) |
| 5900.0 | Megacorporation |
| 5908.0 | Counterpoint |
| 5911.0 | Continuum_hypothesis |
| 5913.0 | Catalysis |
| 5915.0 | Catalysis |
| 5924.0 | Christian_eschatology |
| 5925.0 | Color |
| 5953.0 | Claude_Monet |
| 5960.0 | Genetic_code |
| 5968.0 | Computer_music |
| 5975.0 | Call_of_Cthulhu_(role-playing_game) |
| 5978.0 | Kyoto_Protocol |
| 5983.0 | Computer_science |
| 6012.0 | Church–Turing_thesis |
| 6017.0 | Cruise_missile |
| 6018.0 | Call_of_Cthulhu |
| 6022.0 | Cell_biology |
| 6030.0 | Chronic_fatigue_syndrome |
| 6031.0 | Chronic_fatigue_syndrome |
| 6032.0 | Chronic_fatigue_syndrome |
| 6033.0 | Chronic_fatigue_syndrome |
| 6037.0 | Continuous_function |
| 6043.0 | Critical_point_(thermodynamics) |
| 6053.0 | CE |
| 6054.0 | CE |
| 6055.0 | CD-ROM |
| 6063.0 | Cartoonist |
| 6065.0 | Sine_and_cosine |
| 6067.0 | Common_Lisp |
| 6070.0 | Orange_(colour) |
| 6071.0 | Black |
| 6074.0 | Orange_(colour) |
| 6076.0 | Cyan |
| 6077.0 | Black |
| 6078.0 | White |
| 6086.0 | Cauchy_sequence |
| 6087.0 | Nicolaus_Copernicus |
| 6089.0 | Creationism |
| 6098.0 | Carolingian_Renaissance |
| 6142.0 | Cardinal_number |
| 6150.0 | Blanching_(cooking) |
| 6178.0 | Cardinal |
| 6179.0 | Buddhist_cuisine |
| 6190.0 | Five-spice_powder |
| 6196.0 | Self-replicating_machine |
| 6197.0 | Self-replicating_machine |
| 6202.0 | London_Convention_on_the_Prevention_of_Marine_Pollution_by_Dumping_of_Wastes_and_Other_Matter |
| 6204.0 | Ramsar_Convention |
| 6219.0 | Claudio_Monteverdi |
| 6223.0 | Comics |
| 6228.0 | List_of_ancient_Celtic_peoples_and_tribes |
| 6236.0 | Champagne_socialist |
| 6240.0 | Celtic_languages |
| 6242.0 | Glossary_of_climbing_terms |
| 6243.0 | Cascade_Range |
| 6263.0 | Charles_Darwin |
| 6266.0 | Climate_change |
| 6269.0 | Wipe_(transition) |
| 6278.0 | Banach_space |
| 6287.0 | Lists_of_cities_by_country |
| 6302.0 | Classical_element |
| 6307.0 | Aether_(classical_element) |
| 6311.0 | College_football |
| 6345.0 | Central_dogma_of_molecular_biology |
| 6348.0 | Medal_of_Honor |
| 6368.0 | Chōshū |
| 6461.0 | Wuxing_(Chinese_philosophy) |
| 6464.0 | Mobile_phone |
| 6470.0 | Computational_linguistics |
| 6500.0 | Lists_of_universities_and_colleges |
| 6502.0 | Clean_Air_Act_(United_States) |
| 6510.0 | Color_space |
| 6515.0 | Lists_of_atheists |
| 6522.0 | Chief_executive_officer |
| 6524.0 | Clam_dip |
| 6531.0 | Chinese_cuisine |
| 6553.0 | Context-free_grammar |
| 6554.0 | Computer_graphics |
| 6564.0 | Conjunction_elimination |
| 6573.0 | Widewuto |
| 6581.0 | Musique_concrète |
| 6594.0 | Casimir_IV_Jagiellon |
| 6595.0 | Computer_vision |
| 6605.0 | Citric_acid_cycle |
| 6609.0 | Stork |
| 6622.0 | Coelenterata |
| 6625.0 | Catholic_Church |
| 6646.0 | List_of_ancient_Germanic_peoples |
| 6657.0 | Catholic_Church |
| 6668.0 | Mousse |
| 6676.0 | Consociationalism |
| 6685.0 | Coca-Cola |
| 6699.0 | Plato |
| 6709.0 | Tree_(data_structure) |
| 6712.0 | Compressor |
| 6714.0 | Comic_book |
| 6726.0 | Antisemitism_in_Christianity |
| 6737.0 | Dhole |
| 6738.0 | Red_wolf |
| 6740.0 | Coyote |
val rowsToSave = spark.sql("SELECT rd_from, rd_title FROM redirects WHERE (rd_from IS NOT NULL) AND (rd_namespace = 0) AND (rd_title IS NOT NULL) AND (rd_interwiki IS NULL) AND (_corrupt_record IS NULL)")
rowsToSave.write.saveAsTable("enwiki_redirect")
rowsToSave: org.apache.spark.sql.DataFrame = [rd_from: int, rd_title: string]
DESCRIBE DETAIL enwiki_redirect
Looks like our data is safely in Delta Lake now. Nice.
Loading of the Wikipedia data
The data from Wikipedia is available as .sql-file dumps here. So we need to do a little bit of work to get these SQL files into an actual database on the cloud.
For the redirects table, it was small enough to fit into memory on the driver, so we could do it in a fairly simple way. The page and pagelinks tables are too big for that, so we need to be a bit trickier.
As a first step, we download the .sql file:
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
FileUtils.copyURLToFile(new URL("https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-page.sql.gz"), new File("/tmp/enwiki-latest-page.sql.gz"))
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
Having done this, we first unzip the file, and then move the file from local storage to the DBFS:
gzip -d /tmp/enwiki-latest-page.sql.gz
mv file:/tmp/enwiki-latest-page.sql /enwiki-latest-page.sql
res1: Boolean = true
Having gotten the data onto the DBFS, we can now read it into Spark:
val rawSQLdump = spark.read.textFile("/enwiki-latest-page.sql")
rawSQLdump: org.apache.spark.sql.Dataset[String] = [value: string]
The first fifty lines are setting up the database, then we get a lot of very long INSERT INTO lines with many many entries being inserted.
println(rawSQLdump.take(50).mkString("\n"))
-- MySQL dump 10.19 Distrib 10.3.34-MariaDB, for debian-linux-gnu (x86_64)
--
-- Host: db1106 Database: enwiki
-- ------------------------------------------------------
-- Server version 10.4.25-MariaDB-log
/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;
/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;
/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;
/*!40101 SET NAMES utf8mb4 */;
/*!40103 SET @OLD_TIME_ZONE=@@TIME_ZONE */;
/*!40103 SET TIME_ZONE='+00:00' */;
/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;
/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;
/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;
/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;
--
-- Table structure for table `page`
--
DROP TABLE IF EXISTS `page`;
/*!40101 SET @saved_cs_client = @@character_set_client */;
/*!40101 SET character_set_client = utf8 */;
CREATE TABLE `page` (
`page_id` int(8) unsigned NOT NULL AUTO_INCREMENT,
`page_namespace` int(11) NOT NULL DEFAULT 0,
`page_title` varbinary(255) NOT NULL DEFAULT '',
`page_is_redirect` tinyint(1) unsigned NOT NULL DEFAULT 0,
`page_is_new` tinyint(1) unsigned NOT NULL DEFAULT 0,
`page_random` double unsigned NOT NULL DEFAULT 0,
`page_touched` binary(14) NOT NULL,
`page_links_updated` varbinary(14) DEFAULT NULL,
`page_latest` int(8) unsigned NOT NULL DEFAULT 0,
`page_len` int(8) unsigned NOT NULL DEFAULT 0,
`page_content_model` varbinary(32) DEFAULT NULL,
`page_lang` varbinary(35) DEFAULT NULL,
PRIMARY KEY (`page_id`),
UNIQUE KEY `page_name_title` (`page_namespace`,`page_title`),
KEY `page_random` (`page_random`),
KEY `page_len` (`page_len`),
KEY `page_redirect_namespace_len` (`page_is_redirect`,`page_namespace`,`page_len`)
) ENGINE=InnoDB AUTO_INCREMENT=72155458 DEFAULT CHARSET=binary ROW_FORMAT=COMPRESSED;
/*!40101 SET character_set_client = @saved_cs_client */;
--
-- Dumping data for table `page`
--
/*!40000 ALTER TABLE `page` DISABLE KEYS */;
The remaining rows look something like this, except much much longer:
println(rawSQLdump.take(51)(50).substring(0,252) + ",...,"+rawSQLdump.take(51)(50).substring(rawSQLdump.take(51)(50).length()-112, rawSQLdump.take(51)(50).length()))
INSERT INTO `page` VALUES (10,0,'AccessibleComputing',1,0,0.33167112649574004,'20221023042651','20221023043017',1002250816,111,'wikitext',NULL),(12,0,'Anarchism',0,0,0.786172332974311,'20221031175348','20221031175436',1119287356,108971,'wikitext',NULL),...,(12488,0,'Gospel_of_mark',1,0,0.831610906712348,'20221026225428','20221023045452',783863262,93,'wikitext',NULL);
Next up, let us strip out the INSERT INTO bit and the initial and final parentheses, then split at each ),(, so that we get each entry as its own string.
val pageDataRows = rawSQLdump.filter(x => x.startsWith("INSERT INTO"))
.flatMap(x => x.substring(27, x.length()-2).split("""\),\("""))
pageDataRows: org.apache.spark.sql.Dataset[String] = [value: string]
So now our data looks like this:
println(pageDataRows.take(20).mkString("\n"))
10,0,'AccessibleComputing',1,0,0.33167112649574004,'20221023042651','20221023043017',1002250816,111,'wikitext',NULL
12,0,'Anarchism',0,0,0.786172332974311,'20221031175348','20221031175436',1119287356,108971,'wikitext',NULL
13,0,'AfghanistanHistory',1,0,0.0621502865684687,'20221031175320','20221023043017',783865149,90,'wikitext',NULL
14,0,'AfghanistanGeography',1,0,0.952234464653055,'20221023042651','20221023043017',783865160,92,'wikitext',NULL
15,0,'AfghanistanPeople',1,0,0.574721494293512,'20221030081456','20221023043017',783865293,95,'wikitext',NULL
18,0,'AfghanistanCommunications',1,0,0.7510681513241201,'20221023042651','20221023043017',783865299,97,'wikitext',NULL
19,0,'AfghanistanTransportations',1,0,0.674272520164282,'20221023042651','20221023043017',783821589,113,'wikitext',NULL
20,0,'AfghanistanMilitary',1,0,0.118158177582694,'20221023042651','20221023043017',1093067805,154,'wikitext',NULL
21,0,'AfghanistanTransnationalIssues',1,0,0.567973358154272,'20221031093955','20221023043017',783821743,101,'wikitext',NULL
23,0,'AssistiveTechnology',1,0,0.72304140005544,'20221023042651','20221023043017',783865310,88,'wikitext',NULL
24,0,'AmoeboidTaxa',1,0,0.159030164740076,'20221023042651','20221023043017',783865319,74,'wikitext',NULL
25,0,'Autism',1,0,0.626026654267708,'20221030132922','20221023043017',1094874534,150,'wikitext',NULL
27,0,'AlbaniaHistory',1,0,0.387134107190309,'20221023042651','20221023043017',783865328,86,'wikitext',NULL
29,0,'AlbaniaPeople',1,0,0.721308424809304,'20221031202408','20221023043017',783865341,91,'wikitext',NULL
30,0,'AsWeMayThink',1,0,0.6769157253922109,'20221023042651','20221023043017',783821752,84,'wikitext',NULL
35,0,'AlbaniaGovernment',1,0,0.326255799575016,'20221023042651','20221023043017',783822027,87,'wikitext',NULL
36,0,'AlbaniaEconomy',1,0,0.774375843605377,'20221024180150','20221023043017',783822029,86,'wikitext',NULL
39,0,'Albedo',0,0,0.14243175009492,'20221030013136','20221030013529',1118971142,61598,'wikitext',NULL
40,0,'AfroAsiaticLanguages',1,0,0.0328232311018028,'20221023042651','20221023043017',783822032,89,'wikitext',NULL
42,0,'ArtificalLanguages',1,0,0.736820935957898,'20221023042651','20221023043017',899426448,160,'wikitext',NULL
With quite a lot of rows - 56.8 million, to be particular.
pageDataRows.count()
res4: Long = 56841730
The above looks a whole lot like a CSV file, doesn't it? Let's write it to file as such. Note that we write it as text instead of as CSV because our data is in the format of a single string per row.
pageDataRows.toDF().write.mode("overwrite").text("/WikipediaData/enwiki-page.csv")
Now we want to read this back in, but with the right schema and column names and so on. So we start by creating the schema. In order to be sure that all the rows got parsed correctly, we add an extra column named _corrupt_record, which will get the raw CSV text whenever it couldn't be parsed right, and otherwise be set to NULL.
import org.apache.spark.sql.types._
// Start by creating a case class of a row entry:
case class WikiPage(page_id:Int,
page_namespace:Int,
page_title:String,
page_is_redirect:Int,
page_is_new:Int,
page_random:Double,
page_touched:String,
page_links_updated:String,
page_latest:Int,
page_len:Int,
page_content_model:String,
page_lang:String)
// then we generate a schema object from the case class: (code copypasted from here: https://sparkbyexamples.com/spark/convert-case-class-to-spark-schema/)
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
val pageSchema = ScalaReflection.schemaFor[WikiPage].dataType.asInstanceOf[StructType].add("_corrupt_record", StringType, true)
import org.apache.spark.sql.types._
defined class WikiPage
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
pageSchema: org.apache.spark.sql.types.StructType = StructType(StructField(page_id,IntegerType,false),StructField(page_namespace,IntegerType,false),StructField(page_title,StringType,true),StructField(page_is_redirect,IntegerType,false),StructField(page_is_new,IntegerType,false),StructField(page_random,DoubleType,false),StructField(page_touched,StringType,true),StructField(page_links_updated,StringType,true),StructField(page_latest,IntegerType,false),StructField(page_len,IntegerType,false),StructField(page_content_model,StringType,true),StructField(page_lang,StringType,true),StructField(_corrupt_record,StringType,true))
Then we read it back in with the schema we just created:
val readFromCSV = spark.read
.options(Map("quote" -> "'", "mode" -> "PERMISSIVE", "columnNameOfCorruptRecord" -> "_corrupt_record"))
.schema(pageSchema)
.csv("/WikipediaData/enwiki-page.csv")
readFromCSV: org.apache.spark.sql.DataFrame = [page_id: int, page_namespace: int ... 11 more fields]
Let's have a look at what we just created:
display(readFromCSV)
| page_id | page_namespace | page_title | page_is_redirect | page_is_new | page_random | page_touched | page_links_updated | page_latest | page_len | page_content_model | page_lang | _corrupt_record |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6.8860822e7 | 3.0 | Akshata_Tiwari | 0.0 | 0.0 | 0.433054031876 | 20220528183322 | 20221003060232 | 1.051999765e9 | 7037.0 | wikitext | NULL | null |
| 6.8860823e7 | 3.0 | 2401:3C00:18E:19AA:5973:5CC7:B49D:C258 | 0.0 | 1.0 | 0.400991271811 | 20220520213044 | 20221008154248 | 1.047557002e9 | 1083.0 | wikitext | NULL | null |
| 6.8860824e7 | 3.0 | 2401:4900:4A95:A727:1:1:5182:FA63 | 0.0 | 1.0 | 0.30129749129 | 20220520213114 | 20221008154248 | 1.047557066e9 | 1381.0 | wikitext | NULL | null |
| 6.8860825e7 | 3.0 | SUPROCKS | 0.0 | 0.0 | 0.725841331722 | 20220821081144 | 20221003060232 | 1.052000304e9 | 4924.0 | wikitext | NULL | null |
| 6.8860826e7 | 7.0 | WEASEL.JPG | 0.0 | 1.0 | 0.281630812613 | 20221021144754 | 20220829095138 | 1.047557093e9 | 50.0 | wikitext | NULL | null |
| 6.8860827e7 | 3.0 | 130.193.221.44 | 0.0 | 1.0 | 0.487532485502 | 20220803125143 | 20220803125142 | 1.047557095e9 | 741.0 | wikitext | NULL | null |
| 6.8860828e7 | 3.0 | Kspencer2 | 0.0 | 1.0 | 0.9383753637 | 20220803125143 | 20220803125142 | 1.047557107e9 | 4568.0 | wikitext | NULL | null |
| 6.8860829e7 | 10.0 | Schrader-Porsche-924-944-968 | 0.0 | 0.0 | 0.14985841197 | 20221023074722 | 20221101064157 | 1.048533127e9 | 529.0 | wikitext | NULL | null |
| 6.886083e7 | 0.0 | William_Alexander_(architect) | 0.0 | 0.0 | 4.3310289959e-2 | 20221026145423 | 20221021064111 | 1.050465598e9 | 4098.0 | wikitext | NULL | null |
| 6.8860831e7 | 3.0 | 2405:201:4012:5093:A846:8D6A:DECE:1C33 | 0.0 | 1.0 | 0.539577992208 | 20220520213438 | 20221008154248 | 1.047557191e9 | 936.0 | wikitext | NULL | null |
| 6.8860832e7 | 6.0 | David_Graves.jpg | 0.0 | 0.0 | 0.404485287144 | 20221101064048 | 20221101064040 | 1.049134032e9 | 497.0 | wikitext | NULL | null |
| 6.8860833e7 | 0.0 | 1911_South_Sydney_season | 0.0 | 0.0 | 0.827142123494 | 20221023074722 | 20221012090340 | 1.091550923e9 | 9240.0 | wikitext | NULL | null |
| 6.8860834e7 | 11.0 | Schrader-Porsche-924-944-968 | 0.0 | 1.0 | 0.594639863808 | 20221023135015 | 20221026140056 | 1.047557236e9 | 27.0 | wikitext | NULL | null |
| 6.8860835e7 | 7.0 | David_Graves.jpg | 0.0 | 1.0 | 0.121902250077 | 20221023135015 | 20221026140056 | 1.047557247e9 | 88.0 | wikitext | NULL | null |
| 6.8860836e7 | 3.0 | Yashkulkarnixoxo | 0.0 | 1.0 | 0.215607243257 | 20220913101409 | 20220804014401 | 1.047557262e9 | 1272.0 | wikitext | NULL | null |
| 6.8860837e7 | 0.0 | Longtail_weasel | 1.0 | 1.0 | 4.1464632495e-2 | 20221021170204 | 20221018092305 | 1.047557371e9 | 32.0 | wikitext | NULL | null |
| 6.8860838e7 | 3.0 | 171.49.166.241 | 0.0 | 1.0 | 0.923431613927 | 20220520202801 | 20221008154248 | 1.047557394e9 | 1270.0 | wikitext | NULL | null |
| 6.8860839e7 | 1.0 | Longtail_weasel | 0.0 | 1.0 | 0.699760250093 | 20221021144754 | 20220829095115 | 1.047557418e9 | 54.0 | wikitext | NULL | null |
| 6.8860841e7 | 0.0 | RTL_Up | 1.0 | 1.0 | 0.980251414377 | 20221031141310 | 20221031141306 | 1.047557513e9 | 66.0 | wikitext | NULL | null |
| 6.8860842e7 | 6.0 | Raame_Aandalum_Raavane_Aandalum_poster.jpg | 0.0 | 0.0 | 0.294557725343 | 20221023093710 | 20221023171424 | 1.113556468e9 | 315.0 | wikitext | NULL | null |
| 6.8860843e7 | 3.0 | 123.231.123.237 | 0.0 | 1.0 | 0.190008213578 | 20220520200714 | 20221008154248 | 1.047557548e9 | 830.0 | wikitext | NULL | null |
| 6.8860844e7 | 3.0 | MattyShub | 0.0 | 1.0 | 0.587473902242 | 20221018143335 | 20221018143333 | 1.047557565e9 | 4246.0 | wikitext | NULL | null |
| 6.8860845e7 | 3.0 | Ocheaccounts | 0.0 | 1.0 | 0.697112716587 | 20220828115945 | 20220828115937 | 1.047557609e9 | 917.0 | wikitext | NULL | null |
| 6.8860846e7 | 10.0 | Schrader-Porsche-924-944-968/doc | 0.0 | 1.0 | 0.546049679537 | 20221101064048 | 20221101064046 | 1.047557655e9 | 1699.0 | wikitext | NULL | null |
| 6.8860847e7 | 0.0 | The_Sex_Side_of_Life | 1.0 | 1.0 | 0.411621099913 | 20221018180322 | 20221018180322 | 1.047557725e9 | 26.0 | wikitext | NULL | null |
| 6.8860848e7 | 3.0 | 2A02:C7F:7C57:9000:51C0:BB90:7DFA:A059 | 0.0 | 1.0 | 0.788242241036 | 20220520222441 | 20221008154249 | 1.047557726e9 | 977.0 | wikitext | NULL | null |
| 6.8860849e7 | 11.0 | Schrader-Porsche-924-944-968/doc | 1.0 | 1.0 | 0.494634656132 | 20220930232633 | 20220930232631 | 1.047557767e9 | 56.0 | wikitext | NULL | null |
| 6.886085e7 | 0.0 | Facemasks_during_the_Covid-19_pandemic | 1.0 | 1.0 | 0.301272045424 | 20221101031429 | 20221018072950 | 1.047557828e9 | 53.0 | wikitext | NULL | null |
| 6.8860851e7 | 3.0 | 2001:D08:D8:E609:6481:EB5A:4646:D743 | 0.0 | 1.0 | 0.685161615027 | 20220520210022 | 20221008154248 | 1.047557837e9 | 855.0 | wikitext | NULL | null |
| 6.8860853e7 | 2.0 | Rubymhel04/sandbox | 0.0 | 0.0 | 0.382185656223 | 20220728025144 | 20220728025143 | 1.047582817e9 | 1863.0 | wikitext | NULL | null |
| 6.8860854e7 | 7.0 | MRNA_vaccines_against_the_coronavirus.webm | 0.0 | 1.0 | 0.84661630027 | 20221021144754 | 20220829095139 | 1.047557886e9 | 177.0 | wikitext | NULL | null |
| 6.8860855e7 | 0.0 | List_of_awards_and_nominations_received_by_George_Lucas | 0.0 | 0.0 | 0.711784833384 | 20221030004103 | 20221030004449 | 1.10620484e9 | 12877.0 | wikitext | NULL | null |
| 6.8860856e7 | 3.0 | 77.96.168.101 | 0.0 | 0.0 | 0.885016413037 | 20220419035920 | 20221008154248 | 1.047559489e9 | 6509.0 | wikitext | NULL | null |
| 6.8860857e7 | 2.0 | Mrjwwd | 0.0 | 0.0 | 8.833056386e-3 | 20221023093710 | 20221013034855 | 1.047560555e9 | 434.0 | wikitext | NULL | null |
| 6.8860858e7 | 2.0 | Name6547/sandbox | 0.0 | 1.0 | 0.825950411417 | 20221023093710 | 20220803125341 | 1.047557986e9 | 74.0 | wikitext | NULL | null |
| 6.8860859e7 | 0.0 | Second_Chance_Motorsports | 0.0 | 0.0 | 0.972959691201 | 20221011054852 | 20220927082638 | 1.052480957e9 | 144.0 | wikitext | NULL | null |
| 6.886086e7 | 6.0 | CodexMendoza01.jpg | 0.0 | 0.0 | 0.367723667089 | 20221023093710 | 20220827190429 | 1.069935152e9 | 67.0 | wikitext | NULL | null |
| 6.8860861e7 | 0.0 | Lenny_Massey | 0.0 | 0.0 | 0.397275337479 | 20221023074722 | 20221023001612 | 1.111727583e9 | 5170.0 | wikitext | NULL | null |
| 6.8860862e7 | 0.0 | 2021–22_EHF_European_League | 0.0 | 0.0 | 0.970692318602 | 20221031232845 | 20221101014016 | 1.109832634e9 | 26821.0 | wikitext | NULL | null |
| 6.8860863e7 | 10.0 | Austen-Porsche-924-944-968 | 0.0 | 1.0 | 0.505228432583 | 20221023074722 | 20221101064157 | 1.047558268e9 | 491.0 | wikitext | NULL | null |
| 6.8860864e7 | 0.0 | 2021_Asian_Table_Tennis_Championships_–_Women's_team | 0.0 | 0.0 | 0.153117753767 | 20221024175505 | 20221022114857 | 1.063537781e9 | 10200.0 | wikitext | NULL | null |
| 6.8860865e7 | 2.0 | Shabraiz567 | 0.0 | 0.0 | 0.982321458825 | 20220728025144 | 20220728025143 | 1.047558563e9 | 394.0 | wikitext | NULL | null |
| 6.8860866e7 | 11.0 | Austen-Porsche-924-944-968 | 0.0 | 1.0 | 0.342443434686 | 20221023135015 | 20221026140056 | 1.04755834e9 | 27.0 | wikitext | NULL | null |
| 6.8860867e7 | 0.0 | Rina_Fukushi | 0.0 | 0.0 | 0.937815749526 | 20221023074722 | 20221002125141 | 1.068963921e9 | 2431.0 | wikitext | NULL | null |
| 6.8860868e7 | 14.0 | Adaptations_of_works_by_Georg_Büchner | 0.0 | 1.0 | 0.903088165199 | 20221004153754 | 20220912133804 | 1.047558431e9 | 95.0 | wikitext | NULL | null |
| 6.8860869e7 | 3.0 | Twofingered_Typist/Archives/2021/September | 0.0 | 1.0 | 0.687101836698 | 20220902081543 | 20221030074922 | 1.047558511e9 | 7074.0 | wikitext | NULL | null |
| 6.886087e7 | 14.0 | 2021_Asian_Table_Tennis_Championships | 0.0 | 1.0 | 0.206480787518 | 20221004174814 | 20221004174813 | 1.047558616e9 | 391.0 | wikitext | NULL | null |
| 6.8860871e7 | 0.0 | 1979_in_Finland | 0.0 | 0.0 | 0.866086508971 | 20221023074722 | 20221027141538 | 1.068272744e9 | 2619.0 | wikitext | NULL | null |
| 6.8860872e7 | 3.0 | Estefce | 0.0 | 0.0 | 0.202777871268 | 20220913101409 | 20220825203341 | 1.047558757e9 | 6283.0 | wikitext | NULL | null |
| 6.8860873e7 | 6.0 | I'm_the_Villainess,_So_I'm_Taming_the_Final_Boss_light_novel_volume_1_cover.jpg | 0.0 | 0.0 | 0.491724215508 | 20221023093710 | 20220724102005 | 1.049134598e9 | 853.0 | wikitext | NULL | null |
| 6.8860874e7 | 3.0 | Kennygoldprince | 0.0 | 1.0 | 0.796714685352 | 20220803125143 | 20220803125142 | 1.047558736e9 | 628.0 | wikitext | NULL | null |
| 6.8860875e7 | 10.0 | Austen-Porsche-924-944-968/doc | 0.0 | 1.0 | 0.805875194013 | 20221101064048 | 20221101064045 | 1.047558773e9 | 1682.0 | wikitext | NULL | null |
| 6.8860876e7 | 2.0 | Arnav_Bhate/vector.css | 0.0 | 1.0 | 0.457996502764 | 20220728025144 | 20220728025142 | 1.047558789e9 | 78.0 | css | NULL | null |
| 6.8860877e7 | 14.0 | Works_based_on_Woyzeck | 0.0 | 1.0 | 0.958023053289 | 20221004153754 | 20220907092914 | 1.047558809e9 | 184.0 | wikitext | NULL | null |
| 6.8860878e7 | 3.0 | SmartClinic | 0.0 | 0.0 | 0.392863797502 | 20221020153735 | 20221020153734 | 1.063201083e9 | 2648.0 | wikitext | NULL | null |
| 6.8860879e7 | 11.0 | Austen-Porsche-924-944-968/doc | 1.0 | 1.0 | 0.54459759364 | 20220930232633 | 20220930232630 | 1.047558939e9 | 54.0 | wikitext | NULL | null |
| 6.8860882e7 | 2.0 | Craft53 | 0.0 | 1.0 | 0.856275501275 | 20220728025144 | 20220728025143 | 1.047558962e9 | 0.0 | wikitext | NULL | null |
| 6.8860883e7 | 4.0 | Sockpuppet_investigations/CredEsTy | 0.0 | 0.0 | 0.296354585509 | 20221024143700 | 20221027013921 | 1.047558989e9 | 81.0 | wikitext | NULL | null |
| 6.8860884e7 | 0.0 | Charlie_Patino | 0.0 | 0.0 | 0.873178431548 | 20221031200508 | 20221028205743 | 1.118777946e9 | 11964.0 | wikitext | NULL | null |
| 6.8860885e7 | 3.0 | Salva_Reviews | 0.0 | 0.0 | 0.49000916154 | 20221020153735 | 20221020153734 | 1.063101111e9 | 2648.0 | wikitext | NULL | null |
| 6.8860886e7 | 3.0 | 117.20.69.159 | 0.0 | 1.0 | 0.688069343258 | 20220520195653 | 20221008154248 | 1.047559091e9 | 951.0 | wikitext | NULL | null |
| 6.8860887e7 | 3.0 | 2403:5800:7700:1300:3103:CD9D:6A2F:3153 | 0.0 | 1.0 | 8.3266595477e-2 | 20220803125143 | 20220803125142 | 1.047559106e9 | 2203.0 | wikitext | NULL | null |
| 6.8860889e7 | 2.0 | Eritque_arcus | 0.0 | 1.0 | 0.916796158262 | 20220728025144 | 20220728025143 | 1.047559146e9 | 68.0 | wikitext | NULL | null |
| 6.886089e7 | 2.0 | Botushali | 0.0 | 0.0 | 0.174368789891 | 20221019010418 | 20221019010418 | 1.116919316e9 | 998.0 | wikitext | NULL | null |
| 6.8860891e7 | 1.0 | Charlie_Patino | 0.0 | 0.0 | 0.250746277407 | 20221023093710 | 20220808033123 | 1.053690677e9 | 327.0 | wikitext | NULL | null |
| 6.8860893e7 | 0.0 | Thomas_Beven | 0.0 | 0.0 | 0.664714178705 | 20221030073953 | 20221030120120 | 1.055970968e9 | 3931.0 | wikitext | NULL | null |
| 6.8860894e7 | 1.0 | William_Alexander_(architect) | 0.0 | 0.0 | 0.949304483861 | 20221023093710 | 20220929112058 | 1.094935732e9 | 344.0 | wikitext | NULL | null |
| 6.8860896e7 | 1.0 | Thomas_Beven | 0.0 | 1.0 | 0.552309689933 | 20221021144754 | 20221016062122 | 1.047559432e9 | 106.0 | wikitext | NULL | null |
| 6.8860897e7 | 0.0 | Tomas_Serra_Olives | 0.0 | 0.0 | 3.5481266726e-2 | 20221023130113 | 20221006183220 | 1.069043029e9 | 2357.0 | wikitext | NULL | null |
| 6.8860898e7 | 0.0 | Charlie_Patiño | 1.0 | 1.0 | 0.356744216841 | 20221028205428 | 20221018064114 | 1.047559449e9 | 28.0 | wikitext | NULL | null |
| 6.8860899e7 | 2.0 | Eewilson/Work | 0.0 | 0.0 | 0.795391773552 | 20221023074722 | 20221005082017 | 1.111690035e9 | 36219.0 | wikitext | NULL | null |
| 6.88609e7 | 3.0 | 2409:4043:2D96:9570:366A:A20D:1CA3:D35B | 0.0 | 1.0 | 0.824473449436 | 20220520213808 | 20221008154249 | 1.047559471e9 | 1028.0 | wikitext | NULL | null |
| 6.8860901e7 | 1.0 | Tomas_Serra_Olives | 0.0 | 0.0 | 0.212171341464 | 20221023135015 | 20221024155536 | 1.100645198e9 | 238.0 | wikitext | NULL | null |
| 6.8860902e7 | 3.0 | Md_Saidul_Hasan_Adnan | 0.0 | 0.0 | 0.847609578148 | 20220809232000 | 20221010150718 | 1.049048019e9 | 9685.0 | wikitext | NULL | null |
| 6.8860903e7 | 0.0 | Tetilla_(sponge) | 0.0 | 0.0 | 0.928679518927 | 20221023074722 | 20221011101710 | 1.091133919e9 | 5630.0 | wikitext | NULL | null |
| 6.8860904e7 | 0.0 | Something_Real_(Phoebe_Snow_album) | 0.0 | 0.0 | 0.842222110265 | 20221029202802 | 20221029210048 | 1.118525442e9 | 8917.0 | wikitext | NULL | null |
| 6.8860905e7 | 0.0 | Luca_Pretolesi | 0.0 | 0.0 | 0.77092673445 | 20221023074722 | 20221009073326 | 1.114981309e9 | 6932.0 | wikitext | NULL | null |
| 6.8860907e7 | 3.0 | 78.80.16.63 | 0.0 | 1.0 | 1.5157985722e-2 | 20220521000539 | 20221008154248 | 1.047559784e9 | 912.0 | wikitext | NULL | null |
| 6.8860908e7 | 2.0 | Dishitha_Sathyaseelan/sandbox | 0.0 | 1.0 | 0.721020112919 | 20221023093710 | 20220803125341 | 1.047559788e9 | 350.0 | wikitext | NULL | null |
| 6.8860909e7 | 3.0 | 46.135.91.1 | 0.0 | 1.0 | 2.6013833417e-2 | 20220520223514 | 20221008154248 | 1.04755982e9 | 912.0 | wikitext | NULL | null |
| 6.886091e7 | 2.0 | Eewilson/Links | 0.0 | 0.0 | 0.603283862608 | 20221023093710 | 20221005082017 | 1.111690205e9 | 4887.0 | wikitext | NULL | null |
| 6.8860912e7 | 2.0 | Ethanix7 | 0.0 | 1.0 | 0.237041813811 | 20220728025144 | 20220728025143 | 1.047560008e9 | 120.0 | wikitext | NULL | null |
| 6.8860913e7 | 3.0 | 2A02:C7E:687:DF00:2937:2794:FB24:AA9E | 0.0 | 1.0 | 0.961504961061 | 20220520222302 | 20221008154248 | 1.047560011e9 | 1399.0 | wikitext | NULL | null |
| 6.8860914e7 | 0.0 | Jme_tire | 1.0 | 1.0 | 0.762124254894 | 20221018142316 | 20221018084611 | 1.047560035e9 | 23.0 | wikitext | NULL | null |
| 6.8860916e7 | 0.0 | Doolot_Sydykov | 0.0 | 0.0 | 0.985011776783 | 20221023074722 | 20221023032807 | 1.105642554e9 | 4332.0 | wikitext | NULL | null |
| 6.8860917e7 | 3.0 | 87.116.173.73 | 0.0 | 0.0 | 0.909803349467 | 20220521003541 | 20221008154248 | 1.047714392e9 | 2573.0 | wikitext | NULL | null |
| 6.8860918e7 | 2.0 | Eewilson/Editing_plant_articles | 0.0 | 0.0 | 0.560822079386 | 20221027044103 | 20221027044103 | 1.118467047e9 | 7147.0 | wikitext | NULL | null |
| 6.8860919e7 | 3.0 | 80.183.85.74 | 0.0 | 1.0 | 0.130867322555 | 20220803125143 | 20220803125142 | 1.04756028e9 | 641.0 | wikitext | NULL | null |
| 6.8860921e7 | 2.0 | SMAK/Editnotice | 0.0 | 0.0 | 0.150602441099 | 20220728025147 | 20220728025146 | 1.048008225e9 | 279.0 | wikitext | NULL | null |
| 6.8860923e7 | 2.0 | Randyinbonn | 0.0 | 0.0 | 0.727967064384 | 20221028052204 | 20221028052741 | 1.089775114e9 | 653.0 | wikitext | NULL | null |
| 6.8860924e7 | 3.0 | 196.15.207.227 | 0.0 | 1.0 | 0.412250851556 | 20220520205036 | 20221008154249 | 1.047560397e9 | 1080.0 | wikitext | NULL | null |
| 6.8860925e7 | 3.0 | 204.81.109.143 | 0.0 | 1.0 | 0.882543834418 | 20220911065958 | 20220911065957 | 1.047560411e9 | 615.0 | wikitext | NULL | null |
| 6.8860926e7 | 3.0 | Ranjha0083 | 0.0 | 1.0 | 0.615229553392 | 20221002143159 | 20221002143157 | 1.04756043e9 | 3272.0 | wikitext | NULL | null |
| 6.8860927e7 | 6.0 | Al_McCoy_baseball.jpg | 0.0 | 1.0 | 0.61827591825 | 20221101064048 | 20221101064038 | 1.04756049e9 | 524.0 | wikitext | NULL | null |
| 6.8860928e7 | 0.0 | Saterfrisian | 1.0 | 1.0 | 0.769987582065 | 20221018165829 | 20221018165828 | 1.047560498e9 | 40.0 | wikitext | NULL | null |
| 6.8860929e7 | 10.0 | Morgan-Porsche-924-944-968 | 0.0 | 1.0 | 0.557495026563 | 20221023074722 | 20221101064157 | 1.047560516e9 | 585.0 | wikitext | NULL | null |
| 6.886093e7 | 1.0 | Diffusion_gradient | 0.0 | 0.0 | 0.214232432513 | 20221021144754 | 20220913203008 | 1.060647447e9 | 49.0 | wikitext | NULL | null |
| 6.8860931e7 | 11.0 | Morgan-Porsche-924-944-968 | 0.0 | 1.0 | 0.955415040881 | 20221023135015 | 20221026140056 | 1.047560595e9 | 27.0 | wikitext | NULL | null |
| 6.8860932e7 | 3.0 | 98.229.237.70 | 0.0 | 1.0 | 0.618328241273 | 20220803125147 | 20220803125145 | 1.047560602e9 | 2244.0 | wikitext | NULL | null |
| 6.8860933e7 | 1.0 | 1973_Montana_State_Bobcats_football_team | 0.0 | 1.0 | 0.525878099586 | 20221027063132 | 20221027080018 | 1.047560626e9 | 105.0 | wikitext | NULL | null |
| 6.8860934e7 | 1.0 | 1974_Montana_State_Bobcats_football_team | 0.0 | 1.0 | 0.427495890992 | 20221027063132 | 20221027080048 | 1.047560636e9 | 105.0 | wikitext | NULL | null |
| 6.8860935e7 | 0.0 | Al_McCoy_(baseball) | 0.0 | 0.0 | 0.930369345899 | 20221101064050 | 20221101064156 | 1.071763991e9 | 2658.0 | wikitext | NULL | null |
| 6.8860936e7 | 0.0 | Ich_bin_weg_(Boro_boro) | 1.0 | 1.0 | 0.326664303833 | 20221021165642 | 20221018081349 | 1.047560665e9 | 36.0 | wikitext | NULL | null |
| 6.8860938e7 | 0.0 | Ich_bin_weg_(Boro_Boro) | 1.0 | 0.0 | 0.60755184816 | 20221020090731 | 20221020090731 | 1.07279941e9 | 47.0 | wikitext | NULL | null |
| 6.8860939e7 | 3.0 | AngelicCrusade | 0.0 | 1.0 | 0.53426683569 | 20220803125147 | 20220803125145 | 1.047560698e9 | 635.0 | wikitext | NULL | null |
| 6.886094e7 | 1.0 | Al_McCoy_(baseball) | 0.0 | 0.0 | 0.981241316884 | 20221021144754 | 20220916083249 | 1.092221011e9 | 150.0 | wikitext | NULL | null |
| 6.8860941e7 | 3.0 | Sakib108 | 0.0 | 1.0 | 0.368763431326 | 20221002143159 | 20221002143157 | 1.047560712e9 | 3268.0 | wikitext | NULL | null |
| 6.8860942e7 | 3.0 | Phawu | 0.0 | 1.0 | 0.152886132979 | 20221002143159 | 20221002143157 | 1.047560715e9 | 3262.0 | wikitext | NULL | null |
| 6.8860943e7 | 0.0 | Ich_bin_weg | 1.0 | 1.0 | 0.497140544602 | 20221021165642 | 20221018081349 | 1.047560722e9 | 36.0 | wikitext | NULL | null |
| 6.8860945e7 | 2.0 | EGGBUTTEATERLOL | 0.0 | 1.0 | 0.434198432559 | 20220728025147 | 20220728025145 | 1.047560806e9 | 2.0 | wikitext | NULL | null |
| 6.8860946e7 | 3.0 | 76.74.122.250 | 0.0 | 0.0 | 0.599551730357 | 20221025153032 | 20221008154248 | 1.089578323e9 | 7125.0 | wikitext | NULL | null |
| 6.8860947e7 | 10.0 | Morgan-Porsche-924-944-968/doc | 0.0 | 1.0 | 0.898751906908 | 20221101064048 | 20221101064046 | 1.047560829e9 | 1683.0 | wikitext | NULL | null |
| 6.8860949e7 | 3.0 | 2405:201:D00D:7012:40C0:BC4B:D61E:FBDB | 0.0 | 0.0 | 0.74870130798 | 20220917231312 | 20221010150718 | 1.078878783e9 | 4950.0 | wikitext | NULL | null |
| 6.886095e7 | 2.0 | Montag313 | 0.0 | 0.0 | 0.278251575766 | 20221023093710 | 20220804010330 | 1.051407297e9 | 5986.0 | wikitext | NULL | null |
| 6.8860951e7 | 3.0 | 112.209.164.160 | 0.0 | 0.0 | 0.493993193084 | 20220803125147 | 20220803125145 | 1.047673237e9 | 1044.0 | wikitext | NULL | null |
| 6.8860952e7 | 2.0 | Kayote_Music/sandbox | 0.0 | 1.0 | 0.339108753837 | 20221023093710 | 20220803125342 | 1.047560916e9 | 46.0 | wikitext | NULL | null |
| 6.8860953e7 | 0.0 | Yahya_Mahayni | 1.0 | 0.0 | 0.485066265148 | 20221030172535 | 20221030172534 | 1.064350445e9 | 39.0 | wikitext | NULL | null |
| 6.8860954e7 | 11.0 | Morgan-Porsche-924-944-968/doc | 1.0 | 1.0 | 0.553477519859 | 20220930232633 | 20220930232631 | 1.047560993e9 | 54.0 | wikitext | NULL | null |
| 6.8860955e7 | 3.0 | Omar_JOHN_234 | 0.0 | 1.0 | 0.690628191888 | 20221020153735 | 20221020153734 | 1.047561008e9 | 1096.0 | wikitext | NULL | null |
| 6.8860956e7 | 2.0 | Editeditit/common.js | 0.0 | 1.0 | 0.889054596992 | 20220728025147 | 20220728025146 | 1.047561118e9 | 66.0 | javascript | NULL | null |
| 6.8860957e7 | 0.0 | Barium_ethynediide | 1.0 | 1.0 | 0.351157398617 | 20221017151819 | 20221017151818 | 1.047561125e9 | 27.0 | wikitext | NULL | null |
| 6.8860958e7 | 3.0 | 5.30.24.187 | 0.0 | 1.0 | 0.231537075169 | 20220520224040 | 20221008154249 | 1.047561126e9 | 971.0 | wikitext | NULL | null |
| 6.886096e7 | 3.0 | OKKAMI | 0.0 | 0.0 | 0.466130757425 | 20221020153735 | 20221020153734 | 1.063101161e9 | 2681.0 | wikitext | NULL | null |
| 6.8860961e7 | 1.0 | Yahya_Mahayni | 0.0 | 0.0 | 0.130900065435 | 20221023093710 | 20220808033123 | 1.047682957e9 | 165.0 | wikitext | NULL | null |
| 6.8860962e7 | 3.0 | NHexcel47 | 0.0 | 1.0 | 0.526338186155 | 20220913101409 | 20220803125146 | 1.047561236e9 | 6275.0 | wikitext | NULL | null |
| 6.8860963e7 | 0.0 | P-synephrine | 1.0 | 1.0 | 0.738934139964 | 20221021053606 | 20221018101820 | 1.047561259e9 | 24.0 | wikitext | NULL | null |
| 6.8860964e7 | 6.0 | Ibac.jpg | 0.0 | 0.0 | 0.989366727154 | 20221101064048 | 20221101064044 | 1.049134611e9 | 450.0 | wikitext | NULL | null |
| 6.8860966e7 | 2.0 | ThatCollectibleDude/sandbox | 0.0 | 0.0 | 0.471178628925 | 20220728025147 | 20220728025146 | 1.048617729e9 | 90.0 | wikitext | NULL | null |
| 6.8860967e7 | 3.0 | Clare_Logan | 0.0 | 0.0 | 4.4957474871e-2 | 20220911065958 | 20220911065957 | 1.04771849e9 | 2628.0 | wikitext | NULL | null |
| 6.8860968e7 | 7.0 | Ibac.jpg | 0.0 | 1.0 | 0.474194091891 | 20221023135015 | 20221026140056 | 1.047561397e9 | 88.0 | wikitext | NULL | null |
| 6.8860969e7 | 3.0 | Dekoracje228 | 0.0 | 0.0 | 0.56539472642 | 20220825203343 | 20220825203341 | 1.051119198e9 | 3975.0 | wikitext | NULL | null |
| 6.886097e7 | 3.0 | 2A01:4C8:1075:90DE:50D5:8BDC:9130:F3F9 | 0.0 | 1.0 | 0.394188205673 | 20220520221915 | 20221008154249 | 1.047561428e9 | 953.0 | wikitext | NULL | null |
| 6.8860972e7 | 1.0 | Blessed_&_Free_(Kane_Brown_and_H.E.R._song) | 0.0 | 1.0 | 0.818181934338 | 20221021144754 | 20220827152103 | 1.047561556e9 | 8.0 | wikitext | NULL | null |
| 6.8860973e7 | 3.0 | Nisha_kanwar | 0.0 | 1.0 | 0.832091060827 | 20221002143159 | 20221002143157 | 1.047561605e9 | 3276.0 | wikitext | NULL | null |
| 6.8860974e7 | 3.0 | 125.209.162.73 | 0.0 | 1.0 | 0.853538291286 | 20220803125147 | 20220803125145 | 1.047561609e9 | 903.0 | wikitext | NULL | null |
| 6.8860975e7 | 3.0 | LowlySnake1 | 0.0 | 1.0 | 0.167491965007 | 20221020153735 | 20221020153734 | 1.047561625e9 | 1096.0 | wikitext | NULL | null |
| 6.8860976e7 | 3.0 | Enie_Meyer | 0.0 | 1.0 | 0.722047964643 | 20221020153735 | 20221020153733 | 1.047561664e9 | 1096.0 | wikitext | NULL | null |
| 6.8860977e7 | 0.0 | Blessed_&_Free | 1.0 | 1.0 | 7.4770833929e-2 | 20221023134808 | 20221017181120 | 1.047561667e9 | 24.0 | wikitext | NULL | null |
| 6.8860978e7 | 3.0 | 2601:8A:4002:620:383E:C558:EA22:D6DA | 0.0 | 1.0 | 0.372525884803 | 20220803125147 | 20220803125145 | 1.047561686e9 | 748.0 | wikitext | NULL | null |
| 6.8860979e7 | 3.0 | 81.234.44.249 | 0.0 | 1.0 | 0.300832812782 | 20220521001409 | 20221008154249 | 1.047561697e9 | 1110.0 | wikitext | NULL | null |
| 6.886098e7 | 0.0 | Early-May_1933_tornado_outbreak_sequence | 1.0 | 1.0 | 0.311521024485 | 20221027101433 | 20221027101432 | 1.0475617e9 | 106.0 | wikitext | NULL | null |
| 6.8860981e7 | 1.0 | Early-May_1933_tornado_outbreak_sequence | 1.0 | 1.0 | 0.404470664907 | 20221023093710 | 20221027101622 | 1.047561702e9 | 111.0 | wikitext | NULL | null |
| 6.8860982e7 | 118.0 | Flynn:_Son_of_Crimson | 1.0 | 1.0 | 0.328990800811 | 20221023093710 | 20220926111553 | 1.047561708e9 | 82.0 | wikitext | NULL | null |
| 6.8860983e7 | 119.0 | Flynn:_Son_of_Crimson | 1.0 | 1.0 | 0.60117549287 | 20221023093710 | 20220926111554 | 1.047561712e9 | 87.0 | wikitext | NULL | null |
| 6.8860984e7 | 3.0 | 118.103.253.90 | 0.0 | 1.0 | 0.684945054561 | 20221020153735 | 20221020153733 | 1.047561801e9 | 1360.0 | wikitext | NULL | null |
| 6.8860985e7 | 3.0 | 2A04:4A43:4D7E:BB11:0:0:ECED:D16 | 0.0 | 1.0 | 0.612589092258 | 20220803125147 | 20220803125145 | 1.047561827e9 | 924.0 | wikitext | NULL | null |
| 6.8860986e7 | 3.0 | Eaton_Community_Development_Specialist | 1.0 | 1.0 | 4.1052156935e-2 | 20221023093710 | 20220926111553 | 1.047561849e9 | 86.0 | wikitext | NULL | null |
| 6.8860987e7 | 1.0 | KaiserNeko | 0.0 | 1.0 | 0.58774716488 | 20221021144754 | 20221011075753 | 1.04756193e9 | 31.0 | wikitext | NULL | null |
| 6.8860988e7 | 3.0 | Surajkumar8453 | 0.0 | 0.0 | 0.773494478964 | 20221017141655 | 20221030074922 | 1.04759519e9 | 1441.0 | wikitext | NULL | null |
| 6.886099e7 | 3.0 | 2A01:4C8:62:E6FB:1:2:49E2:264F | 0.0 | 1.0 | 0.290155335269 | 20220911065958 | 20220911065957 | 1.047562121e9 | 444.0 | wikitext | NULL | null |
| 6.8860991e7 | 1.0 | Tell_the_Vision/GA1 | 0.0 | 0.0 | 0.904757931659 | 20221022145713 | 20221010223513 | 1.047768329e9 | 3188.0 | wikitext | NULL | null |
| 6.8860992e7 | 10.0 | User_Socialist_Guinea | 0.0 | 0.0 | 0.960672985447 | 20221028164502 | 20221028164502 | 1.118745246e9 | 496.0 | wikitext | NULL | null |
| 6.8860993e7 | 14.0 | Adaptations_of_works_by_Charles_Nodier | 0.0 | 1.0 | 0.821631248283 | 20221004153754 | 20220910131553 | 1.04756222e9 | 96.0 | wikitext | NULL | null |
| 6.8860994e7 | 10.0 | Cotton-Porsche-924-944-968 | 0.0 | 1.0 | 0.645743990372 | 20221023074722 | 20221101064157 | 1.047562242e9 | 538.0 | wikitext | NULL | null |
| 6.8860995e7 | 11.0 | User_Socialist_Guinea | 0.0 | 0.0 | 0.739109530208 | 20221027024549 | 20221027110843 | 1.047565275e9 | 69.0 | wikitext | NULL | null |
| 6.8860996e7 | 3.0 | Mariothegod | 0.0 | 1.0 | 0.709017181503 | 20221020153735 | 20221020153734 | 1.047562274e9 | 1096.0 | wikitext | NULL | null |
| 6.8860997e7 | 3.0 | 178.132.122.66 | 0.0 | 1.0 | 0.632715130639 | 20220520203748 | 20221008154249 | 1.047562276e9 | 1132.0 | wikitext | NULL | null |
| 6.8860999e7 | 3.0 | Xeo23 | 0.0 | 1.0 | 0.583293740672 | 20221020153735 | 20221020153734 | 1.047562305e9 | 1096.0 | wikitext | NULL | null |
| 6.8861001e7 | 0.0 | Date_of_birth_and_personality | 1.0 | 1.0 | 0.557424118229 | 20221024173837 | 20221018065835 | 1.047562313e9 | 35.0 | wikitext | NULL | null |
| 6.8861003e7 | 0.0 | Karl_Richard_Hanitsch | 1.0 | 1.0 | 0.889188304059 | 20221018085821 | 20221018085820 | 1.04756235e9 | 30.0 | wikitext | NULL | null |
| 6.8861004e7 | 14.0 | PinkPantheress_songs | 0.0 | 0.0 | 0.365549754865 | 20221023093710 | 20221003060232 | 1.081921122e9 | 114.0 | wikitext | NULL | null |
| 6.8861005e7 | 0.0 | Personality_and_date_of_birth | 1.0 | 1.0 | 0.373357342144 | 20221024173837 | 20221018102455 | 1.047562377e9 | 35.0 | wikitext | NULL | null |
| 6.8861007e7 | 0.0 | 2021–22_Serbian_Cup | 0.0 | 0.0 | 0.735104640879 | 20221023074722 | 20221011083330 | 1.11116143e9 | 26127.0 | wikitext | NULL | null |
| 6.8861008e7 | 2.0 | Mizux | 0.0 | 0.0 | 0.113582449457 | 20220728025147 | 20220728025146 | 1.048103235e9 | 240.0 | wikitext | NULL | null |
| 6.8861009e7 | 3.0 | The_Killarney_Park | 1.0 | 1.0 | 0.591876825826 | 20221023093710 | 20220926111553 | 1.047562447e9 | 88.0 | wikitext | NULL | null |
| 6.886101e7 | 3.0 | 103.95.167.173 | 0.0 | 0.0 | 0.628398902376 | 20220419035920 | 20221008154249 | 1.059994938e9 | 5919.0 | wikitext | NULL | null |
| 6.8861011e7 | 2.0 | Farhan087/sandbox | 0.0 | 1.0 | 0.343727005111 | 20220728025147 | 20220728025146 | 1.047562547e9 | 554.0 | wikitext | NULL | null |
| 6.8861013e7 | 0.0 | Siege_of_Kufa | 1.0 | 0.0 | 0.877521158747 | 20221031144712 | 20221031144709 | 1.047633899e9 | 145.0 | wikitext | NULL | null |
| 6.8861014e7 | 10.0 | Cotton-Porsche-924-944-968/doc | 0.0 | 1.0 | 0.391288619807 | 20221101064048 | 20221101064045 | 1.047562602e9 | 1685.0 | wikitext | NULL | null |
| 6.8861015e7 | 3.0 | 204.38.171.125 | 0.0 | 0.0 | 0.489317311422 | 20220419035920 | 20221008154249 | 1.073615064e9 | 10722.0 | wikitext | NULL | null |
| 6.8861017e7 | 3.0 | Paulfinebaum6789 | 0.0 | 0.0 | 0.330383173362 | 20221020153735 | 20221020153734 | 1.047709823e9 | 11522.0 | wikitext | NULL | null |
| 6.8861018e7 | 11.0 | Cotton-Porsche-924-944-968 | 0.0 | 1.0 | 0.334543728917 | 20221023135015 | 20221026115434 | 1.04756268e9 | 27.0 | wikitext | NULL | null |
| 6.886102e7 | 3.0 | 103.54.25.79 | 0.0 | 1.0 | 0.458591567167 | 20220913101409 | 20220803125145 | 1.047562697e9 | 8654.0 | wikitext | NULL | null |
| 6.8861021e7 | 1.0 | MetropolitaN | 0.0 | 1.0 | 0.629075388309 | 20221021144754 | 20221004174813 | 1.047562753e9 | 22.0 | wikitext | NULL | null |
| 6.8861022e7 | 3.0 | Azax_1147 | 0.0 | 1.0 | 0.87226933181 | 20220803125147 | 20220803125145 | 1.047562758e9 | 741.0 | wikitext | NULL | null |
| 6.8861023e7 | 11.0 | Cotton-Porsche-924-944-968/doc | 1.0 | 1.0 | 0.589742351974 | 20220930232633 | 20220930232631 | 1.047562789e9 | 54.0 | wikitext | NULL | null |
| 6.8861024e7 | 0.0 | Candy_Thuzar | 1.0 | 1.0 | 0.628799406728 | 20221018063413 | 20221018063412 | 1.047562806e9 | 30.0 | wikitext | NULL | null |
| 6.8861025e7 | 2.0 | Eewilson/Subpages | 0.0 | 0.0 | 1.177710859e-3 | 20221023093710 | 20221005041907 | 1.11329358e9 | 3555.0 | wikitext | NULL | null |
| 6.8861026e7 | 2.0 | Cagriyalcinkaya/Sample_page | 0.0 | 1.0 | 0.800111872346 | 20221023074722 | 20220820005754 | 1.047562873e9 | 2183.0 | wikitext | NULL | null |
| 6.8861027e7 | 2.0 | Filelakeshoe/shoot_on_sight | 1.0 | 1.0 | 0.906004238091 | 20221023093710 | 20220926111553 | 1.047562894e9 | 89.0 | wikitext | NULL | null |
| 6.8861028e7 | 3.0 | Filelakeshoe/shoot_on_sight | 1.0 | 1.0 | 0.919614293729 | 20221023093710 | 20220926111554 | 1.047562898e9 | 94.0 | wikitext | NULL | null |
| 6.8861029e7 | 2.0 | Slambo_312 | 0.0 | 0.0 | 0.808362003064 | 20220813143131 | 20220728111155 | 1.05434138e9 | 1408.0 | wikitext | NULL | null |
| 6.886103e7 | 2.0 | Surajkumar8453/sandbox | 0.0 | 1.0 | 0.841486423271 | 20221023093710 | 20220803125343 | 1.047562904e9 | 46.0 | wikitext | NULL | null |
| 6.8861031e7 | 6.0 | Andree_Millar.jpeg | 0.0 | 1.0 | 0.660084663932 | 20221028191549 | 20221028191534 | 1.047562921e9 | 574.0 | wikitext | NULL | null |
| 6.8861032e7 | 0.0 | Just_a_Waste_(PinkPantheress_song) | 1.0 | 0.0 | 0.593975111268 | 20221028192123 | 20221018085501 | 1.053857208e9 | 36.0 | wikitext | NULL | null |
| 6.8861033e7 | 3.0 | Mandsover_tose | 0.0 | 1.0 | 0.570651134131 | 20221020153735 | 20221020153734 | 1.047563e9 | 1096.0 | wikitext | NULL | null |
| 6.8861034e7 | 3.0 | 2600:1700:C1B0:8A0:608A:C24F:FC98:7E35 | 0.0 | 1.0 | 0.592983108317 | 20220803125147 | 20220803125145 | 1.047563077e9 | 1350.0 | wikitext | NULL | null |
| 6.8861035e7 | 3.0 | SentientObject | 0.0 | 0.0 | 0.118984446389 | 20220920171514 | 20221023171403 | 1.111371182e9 | 6216.0 | wikitext | NULL | null |
| 6.8861036e7 | 2.0 | Kierandi/sandbox | 0.0 | 0.0 | 8.7654050898e-2 | 20221023093710 | 20220803125342 | 1.047563447e9 | 128.0 | wikitext | NULL | null |
| 6.8861037e7 | 0.0 | La_Vie_d'artiste_(film) | 0.0 | 0.0 | 0.187811804904 | 20221026145423 | 20221028213000 | 1.06714546e9 | 3611.0 | wikitext | NULL | null |
| 6.8861038e7 | 3.0 | 194.228.129.62 | 0.0 | 1.0 | 0.691743996791 | 20220520204944 | 20221008154249 | 1.047563313e9 | 912.0 | wikitext | NULL | null |
| 6.8861039e7 | 3.0 | MusingSilence | 0.0 | 0.0 | 0.671761116097 | 20221025142144 | 20221010150718 | 1.097607833e9 | 18424.0 | wikitext | NULL | null |
| 6.8861042e7 | 3.0 | Rpdam | 0.0 | 1.0 | 0.392277193375 | 20221020153735 | 20221020153734 | 1.047563395e9 | 1096.0 | wikitext | NULL | null |
| 6.8861043e7 | 14.0 | People_from_Ribeira_Grande,_Azores | 0.0 | 1.0 | 0.825396556614 | 20220906030805 | 20220906032352 | 1.047563429e9 | 279.0 | wikitext | NULL | null |
| 6.8861044e7 | 10.0 | Did_you_know_nominations/Border_Violence_Monitoring_Network | 0.0 | 0.0 | 4.9008389936e-2 | 20221022145713 | 20221006131118 | 1.049983896e9 | 3476.0 | wikitext | NULL | null |
| 6.8861045e7 | 1.0 | Border_Violence_Monitoring_Network | 0.0 | 0.0 | 8.2274793982e-2 | 20221023135015 | 20221026140056 | 1.051420881e9 | 666.0 | wikitext | NULL | null |
| 6.8861046e7 | 2.0 | AloofBidoof/Lake_Balaton | 0.0 | 0.0 | 0.601086893403 | 20221023074722 | 20221003060232 | 1.058837589e9 | 8841.0 | wikitext | NULL | null |
| 6.8861047e7 | 0.0 | US_Embassy_in_Berlin | 1.0 | 1.0 | 0.980791395118 | 20221010020209 | 20221010020208 | 1.04756349e9 | 78.0 | wikitext | NULL | null |
| 6.8861048e7 | 1.0 | US_Embassy_in_Berlin | 0.0 | 1.0 | 0.942078004343 | 20221021144754 | 20220828115936 | 1.047563491e9 | 60.0 | wikitext | NULL | null |
| 6.8861049e7 | 118.0 | Le_Grêlé | 1.0 | 0.0 | 0.197899211497 | 20221005111337 | 20221005111336 | 1.047854844e9 | 48.0 | wikitext | NULL | null |
| 6.886105e7 | 3.0 | 149.170.83.198 | 0.0 | 1.0 | 0.528309849795 | 20220803125147 | 20220803125145 | 1.047563527e9 | 913.0 | wikitext | NULL | null |
| 6.8861051e7 | 2.0 | Jenben74 | 0.0 | 1.0 | 0.996996977197 | 20220728025147 | 20220728025146 | 1.047563552e9 | 0.0 | wikitext | NULL | null |
| 6.8861053e7 | 0.0 | Gaualofa | 0.0 | 0.0 | 0.712239232017 | 20221023074722 | 20220928130538 | 1.090602071e9 | 8798.0 | wikitext | NULL | null |
| 6.8861054e7 | 828.0 | Location_map/data/Metro_Cebu | 0.0 | 1.0 | 0.741044788824 | 20221022131052 | 20221017142125 | 1.047563608e9 | 143.0 | Scribunto | NULL | null |
| 6.8861055e7 | 0.0 | Gilberto_García_(chess_player) | 0.0 | 0.0 | 0.331635082339 | 20221023130113 | 20221006183845 | 1.113671223e9 | 2298.0 | wikitext | NULL | null |
| 6.8861056e7 | 3.0 | 88.241.42.109 | 0.0 | 1.0 | 0.352048758197 | 20220803125147 | 20220803125145 | 1.047563633e9 | 903.0 | wikitext | NULL | null |
| 6.8861057e7 | 3.0 | AIFS2020 | 0.0 | 1.0 | 0.455197346834 | 20220803125147 | 20220803125145 | 1.047563636e9 | 1423.0 | wikitext | NULL | null |
| 6.8861058e7 | 1.0 | Gilberto_García_(chess_player) | 0.0 | 0.0 | 0.47774836336 | 20221024144950 | 20221024145555 | 1.09718531e9 | 224.0 | wikitext | NULL | null |
| 6.8861059e7 | 1.0 | Gaualofa | 0.0 | 0.0 | 0.396666792232 | 20221023093710 | 20221011075753 | 1.054996589e9 | 236.0 | wikitext | NULL | null |
| 6.886106e7 | 828.0 | Location_map/data/Metro_Cebu/doc | 0.0 | 1.0 | 0.863560794396 | 20221022131052 | 20221017141654 | 1.047563692e9 | 117.0 | wikitext | NULL | null |
| 6.8861061e7 | 6.0 | Ian_Karkull.jpg | 0.0 | 0.0 | 0.353066024219 | 20221101064048 | 20221101064043 | 1.049134601e9 | 446.0 | wikitext | NULL | null |
| 6.8861062e7 | 10.0 | User_Zaire | 0.0 | 0.0 | 0.434146508841 | 20221028163533 | 20221028163533 | 1.118743755e9 | 956.0 | wikitext | NULL | null |
| 6.8861063e7 | 7.0 | Ian_Karkull.jpg | 0.0 | 1.0 | 0.867076865655 | 20221023135015 | 20221026140056 | 1.047563733e9 | 88.0 | wikitext | NULL | null |
| 6.8861064e7 | 0.0 | Andrée_Millar | 0.0 | 0.0 | 0.864725971777 | 20221028191551 | 20221028191753 | 1.11762431e9 | 7806.0 | wikitext | NULL | null |
| 6.8861066e7 | 0.0 | Parliamentary_Office_for_the_Evaluation_of_Scientific_and_Technological_Choices | 0.0 | 0.0 | 1.852428195e-3 | 20221028072423 | 20221023012504 | 1.096967357e9 | 23592.0 | wikitext | NULL | null |
| 6.8861067e7 | 3.0 | 2001:8003:26BE:1300:3092:FCD6:1021:165F | 0.0 | 0.0 | 0.450252497759 | 20220911065958 | 20220911065957 | 1.048218158e9 | 587.0 | wikitext | NULL | null |
| 6.8861068e7 | 0.0 | Attracted_to_You_(PinkPantheress_song) | 1.0 | 0.0 | 0.246034263149 | 20221028192123 | 20221017151045 | 1.053856972e9 | 36.0 | wikitext | NULL | null |
| 6.8861069e7 | 2.0 | SentientObject | 0.0 | 0.0 | 0.96430655643 | 20220701113733 | 20220929060310 | 1.08181685e9 | 228.0 | wikitext | NULL | null |
| 6.886107e7 | 11.0 | User_Zaire | 0.0 | 0.0 | 0.114709487891 | 20221027024549 | 20221027110843 | 1.070931877e9 | 85.0 | wikitext | NULL | null |
| 6.8861071e7 | 3.0 | Noddleloans | 0.0 | 1.0 | 0.87566863721 | 20220803125147 | 20220803125146 | 1.047563869e9 | 1425.0 | wikitext | NULL | null |
| 6.8861072e7 | 2.0 | MISSION_33/Talks | 0.0 | 0.0 | 0.855020648969 | 20220327234038 | 20221011101710 | 1.050904079e9 | 5213.0 | wikitext | NULL | null |
| 6.8861073e7 | 0.0 | Nam_Tok_Sai_Yok_Noi_railway_halt | 1.0 | 1.0 | 0.261635947116 | 20221019092324 | 20221018100023 | 1.047563974e9 | 66.0 | wikitext | NULL | null |
| 6.8861074e7 | 119.0 | Le_Grêlé | 0.0 | 0.0 | 0.967064373386 | 20221027214635 | 20221028144851 | 1.047854981e9 | 257.0 | wikitext | NULL | null |
| 6.8861075e7 | 3.0 | Aiden66362 | 0.0 | 1.0 | 0.725592975534 | 20221020153735 | 20221020153733 | 1.04756405e9 | 1096.0 | wikitext | NULL | null |
| 6.8861076e7 | 1.0 | La_Vie_d'artiste_(film) | 0.0 | 1.0 | 0.553412265316 | 20221023093710 | 20220802033548 | 1.047564116e9 | 279.0 | wikitext | NULL | null |
| 6.8861077e7 | 15.0 | February_1980_events_in_Africa | 0.0 | 1.0 | 0.631694656297 | 20221027024549 | 20221027110843 | 1.047564143e9 | 44.0 | wikitext | NULL | null |
| 6.8861078e7 | 0.0 | Sonterra,_Texas | 0.0 | 0.0 | 0.876032616141 | 20221023074722 | 20221002214204 | 1.113706292e9 | 3323.0 | wikitext | NULL | null |
| 6.8861079e7 | 0.0 | 2021–22_Zamalek_SC_(basketball)_season | 0.0 | 0.0 | 0.995119582976 | 20221031210758 | 20221031215131 | 1.108541779e9 | 60744.0 | wikitext | NULL | null |
| 6.886108e7 | 0.0 | The_Work_(album) | 0.0 | 0.0 | 0.902587268013 | 20221029202802 | 20221029221007 | 1.118081971e9 | 6095.0 | wikitext | NULL | null |
| 6.8861081e7 | 0.0 | The_Work_(Rivers_of_Nihil_album) | 1.0 | 1.0 | 0.73727670429 | 20221021050950 | 20221018180643 | 1.047564264e9 | 41.0 | wikitext | NULL | null |
| 6.8861082e7 | 0.0 | Sonterra | 1.0 | 1.0 | 0.430380507157 | 20221018170912 | 20221018170911 | 1.047564287e9 | 29.0 | wikitext | NULL | null |
| 6.8861083e7 | 3.0 | 64.25.209.17 | 0.0 | 0.0 | 6.0288165699e-2 | 20220601142848 | 20221008154249 | 1.090983032e9 | 3903.0 | wikitext | NULL | null |
| 6.8861084e7 | 0.0 | Rivers_of_Nihil_discography | 1.0 | 1.0 | 0.199407046268 | 20221021050950 | 20221018104650 | 1.047564309e9 | 41.0 | wikitext | NULL | null |
| 6.8861085e7 | 3.0 | 109.97.137.153 | 0.0 | 0.0 | 0.625455704213 | 20221026145423 | 20220913014600 | 1.047567055e9 | 784.0 | wikitext | NULL | null |
| 6.8861087e7 | 119.0 | Pocket_of_Lollipops | 0.0 | 0.0 | 0.598168388417 | 20221021144754 | 20220928033734 | 1.067380635e9 | 279.0 | wikitext | NULL | null |
| 6.8861088e7 | 3.0 | 42.201.249.84 | 0.0 | 1.0 | 0.569884454705 | 20220520223358 | 20221008154249 | 1.047564398e9 | 1000.0 | wikitext | NULL | null |
| 6.886109e7 | 3.0 | 1983littlemj | 0.0 | 0.0 | 0.513252441938 | 20220809232000 | 20221010150718 | 1.04757281e9 | 6971.0 | wikitext | NULL | null |
| 6.8861091e7 | 3.0 | 2405:205:C82D:553F:0:0:2629:68A4 | 0.0 | 1.0 | 0.951044663515 | 20220520213627 | 20221008154249 | 1.047564419e9 | 1213.0 | wikitext | NULL | null |
| 6.8861093e7 | 3.0 | Mobinabahari2007 | 0.0 | 1.0 | 0.183893817314 | 20220803125147 | 20220803125145 | 1.047564472e9 | 814.0 | wikitext | NULL | null |
| 6.8861094e7 | 2.0 | Slambo_312/sandbox | 0.0 | 1.0 | 0.31451408781 | 20221023093710 | 20220803125342 | 1.047564487e9 | 649.0 | wikitext | NULL | null |
| 6.8861095e7 | 11.0 | Charmap/sandbox | 1.0 | 1.0 | 0.461149556774 | 20221023093710 | 20220926111554 | 1.04756449e9 | 94.0 | wikitext | NULL | null |
| 6.8861096e7 | 6.0 | Arutz_24_logo.png | 0.0 | 1.0 | 0.587193919184 | 20221101064048 | 20221101064039 | 1.047564503e9 | 685.0 | wikitext | NULL | null |
| 6.8861097e7 | 4.0 | WikiProject_Spam/LinkReports/diplomi-ukr.com | 0.0 | 1.0 | 0.577406918431 | 20221023093710 | 20221024211651 | 1.047564514e9 | 882.0 | wikitext | NULL | null |
| 6.8861098e7 | 11.0 | Charmap/testcases | 1.0 | 1.0 | 0.568815933532 | 20221023093710 | 20220926111554 | 1.047564519e9 | 94.0 | wikitext | NULL | null |
| 6.8861099e7 | 2.0 | MauraWen/sandbox_Jens_Munk | 0.0 | 0.0 | 0.308689478004 | 20220728025147 | 20220728025146 | 1.047642099e9 | 0.0 | wikitext | NULL | null |
| 6.88611e7 | 2.0 | David0616 | 0.0 | 0.0 | 0.936660531026 | 20220728025147 | 20220728025145 | 1.048165054e9 | 0.0 | wikitext | NULL | null |
| 6.8861102e7 | 3.0 | 142.117.83.248 | 0.0 | 1.0 | 0.779504684666 | 20220520201706 | 20221008154249 | 1.047564722e9 | 980.0 | wikitext | NULL | null |
| 6.8861103e7 | 4.0 | WikiProject_Spam/LinkReports/chidiplomys.co | 0.0 | 1.0 | 0.170002422083 | 20221023093710 | 20221024211651 | 1.047564732e9 | 862.0 | wikitext | NULL | null |
| 6.8861104e7 | 2.0 | Moriiteusz/sandbox | 0.0 | 1.0 | 0.246552635691 | 20220728025147 | 20220728025146 | 1.047564738e9 | 126.0 | wikitext | NULL | null |
| 6.8861106e7 | 2.0 | UBX/Mammootty | 0.0 | 0.0 | 0.818161820266 | 20220728025147 | 20220728025146 | 1.048123754e9 | 656.0 | wikitext | NULL | null |
| 6.8861107e7 | 10.0 | Pitt-Porsche-924-944-968 | 0.0 | 0.0 | 0.35745623817 | 20221023074722 | 20221101064157 | 1.047603689e9 | 455.0 | wikitext | NULL | null |
| 6.8861108e7 | 3.0 | Yuriykolesn | 0.0 | 0.0 | 0.157824375333 | 20220825090443 | 20220825090441 | 1.047565565e9 | 1724.0 | wikitext | NULL | null |
| 6.8861109e7 | 3.0 | Maturescholar | 0.0 | 1.0 | 7.6482708854e-2 | 20220910174405 | 20221011101710 | 1.047564922e9 | 1694.0 | wikitext | NULL | null |
| 6.886111e7 | 4.0 | WikiProject_Spam/LinkReports/chidiplomys.com | 0.0 | 0.0 | 0.442736773133 | 20221023093710 | 20221024211651 | 1.068735569e9 | 1116.0 | wikitext | NULL | null |
| 6.8861111e7 | 3.0 | 2A02:C7E:16A3:6000:9D49:6279:873B:DADF | 0.0 | 1.0 | 0.849486279585 | 20220520222254 | 20221008154249 | 1.047564948e9 | 1003.0 | wikitext | NULL | null |
| 6.8861113e7 | 0.0 | Jean_Paul_Hobler | 1.0 | 1.0 | 0.289179563073 | 20221031121905 | 20221031121859 | 1.047565087e9 | 84.0 | wikitext | NULL | null |
| 6.8861114e7 | 1.0 | Andrée_Millar | 0.0 | 0.0 | 0.918885493177 | 20221021161300 | 20221012013039 | 1.047753235e9 | 335.0 | wikitext | NULL | null |
| 6.8861115e7 | 3.0 | 2607:FEA8:2B41:B570:923E:E36:D9C1:C371 | 0.0 | 1.0 | 0.1298600773 | 20220520221117 | 20221008154249 | 1.047565129e9 | 987.0 | wikitext | NULL | null |
| 6.8861116e7 | 3.0 | 2405:205:1281:D538:8C4D:2EBA:CA12:BB2F | 0.0 | 1.0 | 0.818269943236 | 20220803125150 | 20220803125148 | 1.047565144e9 | 754.0 | wikitext | NULL | null |
| 6.8861117e7 | 10.0 | 2021_World_Wrestling_Championships | 0.0 | 1.0 | 0.683163195517 | 20221021212824 | 20221021222504 | 1.047565145e9 | 3084.0 | wikitext | NULL | null |
| 6.8861119e7 | 4.0 | WikiProject_Spam/LinkReports/reeldrama.com | 0.0 | 1.0 | 0.968616957223 | 20221023093710 | 20221030150250 | 1.047565215e9 | 9323.0 | wikitext | NULL | null |
| 6.886112e7 | 3.0 | 174.88.48.194 | 0.0 | 1.0 | 0.616385814252 | 20220803125150 | 20220803125148 | 1.04756522e9 | 530.0 | wikitext | NULL | null |
| 6.8861121e7 | 0.0 | Listed_buildings_in_Barnsley_(Central_Ward) | 0.0 | 0.0 | 0.301613563899 | 20221025021906 | 20221025095607 | 1.109409216e9 | 54609.0 | wikitext | NULL | null |
| 6.8861122e7 | 10.0 | User_Central_African_Empire | 0.0 | 0.0 | 0.896534751239 | 20220930232633 | 20220930232630 | 1.047686644e9 | 618.0 | wikitext | NULL | null |
| 6.8861123e7 | 3.0 | Prayatnasoe123 | 0.0 | 1.0 | 0.738633505154 | 20220911065958 | 20220911065957 | 1.047565294e9 | 1166.0 | wikitext | NULL | null |
| 6.8861126e7 | 1.0 | Listed_buildings_in_Barnsley_(Central_Ward) | 0.0 | 0.0 | 0.765204076373 | 20221023093710 | 20220929112058 | 1.090601962e9 | 263.0 | wikitext | NULL | null |
| 6.8861128e7 | 0.0 | Tetilla_capillosa | 0.0 | 0.0 | 0.957410724573 | 20221023074722 | 20221010101746 | 1.091133939e9 | 2973.0 | wikitext | NULL | null |
| 6.8861129e7 | 14.0 | Adaptations_of_works_by_Charles_De_Coster | 0.0 | 1.0 | 0.79573803653 | 20221004153754 | 20220907025323 | 1.047565391e9 | 103.0 | wikitext | NULL | null |
| 6.886113e7 | 3.0 | KIttylover18916 | 0.0 | 1.0 | 0.612889521974 | 20221020153735 | 20221020153734 | 1.047565438e9 | 1096.0 | wikitext | NULL | null |
| 6.8861131e7 | 0.0 | (326732)_2003_HB6 | 1.0 | 0.0 | 0.465551005617 | 20221023093710 | 20221003060233 | 1.047565716e9 | 278.0 | wikitext | NULL | null |
| 6.8861132e7 | 11.0 | User_Central_African_Empire | 0.0 | 0.0 | 0.766677352447 | 20221027024549 | 20221027110843 | 1.047574978e9 | 105.0 | wikitext | NULL | null |
| 6.8861133e7 | 10.0 | Pitt-Porsche-924-944-968/doc | 0.0 | 1.0 | 0.650560051018 | 20221101064048 | 20221101064046 | 1.047565523e9 | 1524.0 | wikitext | NULL | null |
| 6.8861134e7 | 0.0 | 2021_Ecuadorian_prison_riot | 1.0 | 0.0 | 0.996969121116 | 20221017120853 | 20221017120852 | 1.055098058e9 | 50.0 | wikitext | NULL | null |
| 6.8861135e7 | 3.0 | 216.211.245.111 | 0.0 | 1.0 | 0.710983366049 | 20220520211512 | 20221008154249 | 1.047565613e9 | 1096.0 | wikitext | NULL | null |
| 6.8861136e7 | 14.0 | People_from_Angra_do_Heroísmo | 0.0 | 0.0 | 0.912121468154 | 20221024211515 | 20221024211619 | 1.048101052e9 | 240.0 | wikitext | NULL | null |
| 6.8861137e7 | 3.0 | 49.37.159.10 | 0.0 | 1.0 | 0.253278812142 | 20220803125150 | 20220803125149 | 1.047565687e9 | 676.0 | wikitext | NULL | null |
| 6.8861138e7 | 0.0 | Shanna_Swan | 0.0 | 0.0 | 0.890997308733 | 20221031211746 | 20221021055127 | 1.117341317e9 | 5518.0 | wikitext | NULL | null |
| 6.8861139e7 | 2.0 | Linanoisette/sandbox | 0.0 | 1.0 | 0.857319569261 | 20221023093710 | 20220803125343 | 1.047565724e9 | 579.0 | wikitext | NULL | null |
| 6.8861141e7 | 11.0 | Pitt-Porsche-924-944-968 | 0.0 | 1.0 | 0.567553974446 | 20221023135015 | 20221026140056 | 1.047565779e9 | 27.0 | wikitext | NULL | null |
| 6.8861142e7 | 3.0 | Slambo_312 | 0.0 | 0.0 | 0.391452734907 | 20221017141655 | 20221030074922 | 1.047594908e9 | 2387.0 | wikitext | NULL | null |
| 6.8861143e7 | 11.0 | Pitt-Porsche-924-944-968/doc | 1.0 | 1.0 | 0.443931678698 | 20220930232633 | 20220930232631 | 1.04756581e9 | 52.0 | wikitext | NULL | null |
| 6.8861144e7 | 3.0 | SmokinLikeWilKvng | 0.0 | 0.0 | 0.177138607698 | 20220913101409 | 20221003060232 | 1.082358249e9 | 6807.0 | wikitext | NULL | null |
| 6.8861145e7 | 3.0 | 2409:4040:E1E:828:0:0:CB8A:4106 | 0.0 | 1.0 | 0.794787493752 | 20220520213719 | 20221008154249 | 1.047565958e9 | 910.0 | wikitext | NULL | null |
| 6.8861146e7 | 2.0 | AloofBidoof/Lake_Balaton/Bibliography | 0.0 | 1.0 | 0.182562997735 | 20221023093710 | 20221003060232 | 1.047566049e9 | 639.0 | wikitext | NULL | null |
| 6.8861147e7 | 2.0 | Abtiw15218 | 0.0 | 1.0 | 0.824630748309 | 20220728025147 | 20220728025145 | 1.047566077e9 | 83.0 | wikitext | NULL | null |
| 6.8861148e7 | 3.0 | 213.162.73.206 | 0.0 | 1.0 | 0.166757245612 | 20220803125150 | 20220803125148 | 1.047566143e9 | 367.0 | wikitext | NULL | null |
| 6.8861149e7 | 3.0 | Cristianmusician | 0.0 | 1.0 | 0.925253650884 | 20220803125150 | 20220803125149 | 1.047566153e9 | 831.0 | wikitext | NULL | null |
| 6.886115e7 | 3.0 | 87.119.179.153 | 0.0 | 1.0 | 0.674256523774 | 20220803125150 | 20220803125149 | 1.047566257e9 | 567.0 | wikitext | NULL | null |
| 6.8861151e7 | 3.0 | 51.154.161.92 | 0.0 | 1.0 | 0.882245928298 | 20220520224220 | 20221008154249 | 1.047566303e9 | 1088.0 | wikitext | NULL | null |
| 6.8861153e7 | 14.0 | Self-contradictory_articles_from_April_2015 | 0.0 | 1.0 | 0.934802860689 | 20221023093710 | 20221005165456 | 1.047566352e9 | 29.0 | wikitext | NULL | null |
| 6.8861154e7 | 0.0 | Funeral_Ceremonies | 0.0 | 0.0 | 0.809580931206 | 20221026145423 | 20221016030457 | 1.111262145e9 | 5006.0 | wikitext | NULL | null |
| 6.8861155e7 | 3.0 | Atlantisandlemuria | 0.0 | 0.0 | 0.553803577295 | 20220904212513 | 20220904212513 | 1.108519782e9 | 4568.0 | wikitext | NULL | null |
| 6.8861156e7 | 0.0 | Abdorrasul_Zarrin | 0.0 | 0.0 | 0.515135012352 | 20221030120304 | 20221030120515 | 1.115686123e9 | 9600.0 | wikitext | NULL | null |
| 6.8861157e7 | 2.0 | Elixeral | 0.0 | 0.0 | 0.201855615703 | 20221023074722 | 20221010223513 | 1.047570167e9 | 506.0 | wikitext | NULL | null |
| 6.8861158e7 | 0.0 | Banque_du_Peuple | 1.0 | 0.0 | 8.5765610088e-2 | 20221024090256 | 20221024090254 | 1.051407049e9 | 78.0 | wikitext | NULL | null |
| 6.8861159e7 | 0.0 | National_Board_of_Student_Aid_(Sweden) | 1.0 | 1.0 | 0.998253717698 | 20221031211508 | 20221031133405 | 1.04756657e9 | 99.0 | wikitext | NULL | null |
| 6.886116e7 | 1.0 | National_Board_of_Student_Aid_(Sweden) | 1.0 | 1.0 | 8.6297795541e-2 | 20221023093710 | 20221031133441 | 1.047566574e9 | 104.0 | wikitext | NULL | null |
| 6.8861161e7 | 1.0 | RAWGraphs | 0.0 | 0.0 | 0.190756542716 | 20221029204943 | 20221029210449 | 1.074099999e9 | 493.0 | wikitext | NULL | null |
| 6.8861162e7 | 1.0 | I'm_the_Villainess,_So_I'm_Taming_the_Final_Boss | 0.0 | 0.0 | 0.284891589071 | 20221023001504 | 20221023001505 | 1.117672205e9 | 359.0 | wikitext | NULL | null |
| 6.8861163e7 | 0.0 | Kurdistan_Democratic_Independence_Party_(PASOK) | 0.0 | 0.0 | 0.873379599362 | 20221031093844 | 20221014074945 | 1.062473696e9 | 2611.0 | wikitext | NULL | null |
| 6.8861164e7 | 0.0 | (285571)_2000_PQ9 | 1.0 | 0.0 | 0.767397309544 | 20221023093710 | 20221003060233 | 1.047566929e9 | 278.0 | wikitext | NULL | null |
| 6.8861165e7 | 2.0 | Aggreybusiingeofficial | 0.0 | 1.0 | 0.528593417482 | 20220728025147 | 20220728025145 | 1.04756671e9 | 15.0 | wikitext | NULL | null |
| 6.8861166e7 | 1.0 | Sugar_Apple_Fairy_Tale | 0.0 | 1.0 | 0.4809985768 | 20221021144754 | 20221011075753 | 1.047566773e9 | 87.0 | wikitext | NULL | null |
| 6.8861167e7 | 0.0 | 1951_South_Sydney_season | 0.0 | 0.0 | 0.547547941329 | 20221023074722 | 20221012090356 | 1.091700784e9 | 12682.0 | wikitext | NULL | null |
| 6.8861168e7 | 3.0 | 194.230.103.220 | 0.0 | 0.0 | 0.147607203446 | 20220522110425 | 20221008154249 | 1.066650279e9 | 1926.0 | wikitext | NULL | null |
| 6.8861169e7 | 1.0 | Shanna_Swan | 0.0 | 0.0 | 0.395668260268 | 20221023093710 | 20220920165455 | 1.097331285e9 | 146.0 | wikitext | NULL | null |
| 6.886117e7 | 0.0 | Dea_Liane | 1.0 | 0.0 | 0.669896387246 | 20221018213727 | 20221018213726 | 1.064351223e9 | 39.0 | wikitext | NULL | null |
| 6.8861172e7 | 1.0 | Dea_Liane | 0.0 | 0.0 | 4.1747161777e-2 | 20221023093710 | 20220808033124 | 1.047682887e9 | 162.0 | wikitext | NULL | null |
| 6.8861173e7 | 3.0 | 121.200.26.188 | 0.0 | 0.0 | 0.192103902174 | 20220520200220 | 20221008154249 | 1.047568066e9 | 3198.0 | wikitext | NULL | null |
| 6.8861174e7 | 3.0 | Popsmoke2 | 0.0 | 1.0 | 0.516559962762 | 20221020153735 | 20221020153734 | 1.047567486e9 | 1096.0 | wikitext | NULL | null |
| 6.8861176e7 | 2.0 | Saradhanjana/sandbox | 0.0 | 0.0 | 0.338065007535 | 20221023074722 | 20221003152347 | 1.048122257e9 | 3167.0 | wikitext | NULL | null |
| 6.8861177e7 | 1.0 | These_Things_Happen_Too | 0.0 | 0.0 | 0.696546951809 | 20221021144754 | 20221004114451 | 1.048239607e9 | 85.0 | wikitext | NULL | null |
| 6.8861178e7 | 2.0 | Johnthegayman/sandbox | 0.0 | 1.0 | 0.658826963067 | 20221023093710 | 20220803125344 | 1.047567563e9 | 108.0 | wikitext | NULL | null |
| 6.8861181e7 | 2.0 | Abantesigmano/sandbox | 0.0 | 0.0 | 0.423609426997 | 20221023074722 | 20221003152347 | 1.049216135e9 | 429.0 | wikitext | NULL | null |
| 6.8861182e7 | 3.0 | 2A01:4C8:829:8713:A121:7E5F:1F81:C5B3 | 0.0 | 1.0 | 0.495858654361 | 20220803125150 | 20220803125148 | 1.047567754e9 | 641.0 | wikitext | NULL | null |
| 6.8861183e7 | 2.0 | O'Dea/Sandbox/Rathfarnham | 0.0 | 0.0 | 0.272839949618 | 20220728025147 | 20220728025146 | 1.047569197e9 | 0.0 | wikitext | NULL | null |
| 6.8861184e7 | 3.0 | 62.254.149.226 | 0.0 | 0.0 | 0.970432828794 | 20221007154646 | 20221007154646 | 1.114652569e9 | 12867.0 | wikitext | NULL | null |
| 6.8861185e7 | 3.0 | Drebullient21 | 0.0 | 1.0 | 0.925999831436 | 20220910174405 | 20221011101710 | 1.047567846e9 | 1528.0 | wikitext | NULL | null |
| 6.8861186e7 | 0.0 | Barnabáš_Lacík | 0.0 | 1.0 | 0.534405990275 | 20221031200508 | 20221023001722 | 1.04756785e9 | 2079.0 | wikitext | NULL | null |
| 6.8861187e7 | 3.0 | 136.158.42.168 | 0.0 | 1.0 | 0.284511319779 | 20220520201402 | 20221008154249 | 1.047567851e9 | 1372.0 | wikitext | NULL | null |
| 6.886119e7 | 3.0 | 49.156.99.118 | 0.0 | 1.0 | 0.576359413822 | 20220520223802 | 20221008154249 | 1.047567968e9 | 1318.0 | wikitext | NULL | null |
| 6.8861191e7 | 3.0 | Fatimah2222.x | 0.0 | 0.0 | 0.424520559629 | 20220917231312 | 20221010150718 | 1.047568846e9 | 6121.0 | wikitext | NULL | null |
| 6.8861192e7 | 3.0 | Official_Rakib | 0.0 | 1.0 | 0.483211031178 | 20220803125150 | 20220803125149 | 1.047568074e9 | 699.0 | wikitext | NULL | null |
| 6.8861193e7 | 3.0 | Snooker_coordinator | 0.0 | 1.0 | 0.660832736988 | 20221020153739 | 20221020153738 | 1.047568137e9 | 2850.0 | wikitext | NULL | null |
| 6.8861194e7 | 3.0 | 203.177.252.230 | 0.0 | 1.0 | 0.260818790199 | 20220520210344 | 20221008154249 | 1.047568327e9 | 1413.0 | wikitext | NULL | null |
| 6.8861195e7 | 3.0 | 2405:201:5500:B1DC:A427:DB01:C70C:95FF | 0.0 | 1.0 | 0.413955142467 | 20220520213446 | 20221008154249 | 1.047568344e9 | 1004.0 | wikitext | NULL | null |
| 6.8861196e7 | 3.0 | 2A01:4C8:C8D:784E:395B:15C6:503F:6AA8 | 0.0 | 1.0 | 0.88818020391 | 20220520222004 | 20221008154249 | 1.047568349e9 | 1035.0 | wikitext | NULL | null |
| 6.8861197e7 | 3.0 | 142.255.40.152 | 0.0 | 1.0 | 0.880738020072 | 20220520201755 | 20221008154249 | 1.04756842e9 | 896.0 | wikitext | NULL | null |
| 6.8861199e7 | 3.0 | DBYZ | 0.0 | 0.0 | 0.67393525601 | 20221020153739 | 20221020153737 | 1.063201252e9 | 2587.0 | wikitext | NULL | null |
| 6.88612e7 | 3.0 | My.bh1307 | 0.0 | 1.0 | 7.7617059823e-2 | 20221018143335 | 20221018143334 | 1.047568445e9 | 4672.0 | wikitext | NULL | null |
| 6.8861201e7 | 0.0 | Alqabas | 1.0 | 1.0 | 0.137252332236 | 20221026202856 | 20221026202855 | 1.047568456e9 | 69.0 | wikitext | NULL | null |
| 6.8861202e7 | 1.0 | Alqabas | 1.0 | 1.0 | 0.592292562805 | 20221023093710 | 20221026204734 | 1.047568458e9 | 74.0 | wikitext | NULL | null |
| 6.8861204e7 | 1.0 | Mackenzie_Evangelical_College_of_Paraná | 0.0 | 0.0 | 0.926969710693 | 20221021144754 | 20220829095130 | 1.052178294e9 | 757.0 | wikitext | NULL | null |
| 6.8861205e7 | 3.0 | Oshansandipa | 0.0 | 1.0 | 0.366734327553 | 20220803125150 | 20220803125149 | 1.047568511e9 | 4888.0 | wikitext | NULL | null |
| 6.8861206e7 | 3.0 | 106.193.129.171 | 0.0 | 1.0 | 0.337492356489 | 20220520194535 | 20221008154249 | 1.047568595e9 | 1047.0 | wikitext | NULL | null |
| 6.8861209e7 | 0.0 | Blauw-Wit_Beursbengels | 1.0 | 0.0 | 7.2446305381e-2 | 20221026224339 | 20221026224338 | 1.047743497e9 | 74.0 | wikitext | NULL | null |
| 6.886121e7 | 1.0 | Blauw-Wit_Beursbengels | 1.0 | 0.0 | 0.188104267146 | 20221004085508 | 20221004085506 | 1.047748355e9 | 32.0 | wikitext | NULL | null |
| 6.8861211e7 | 0.0 | Petrol_panic | 1.0 | 0.0 | 0.186349512546 | 20221101042741 | 20221018102534 | 1.050348684e9 | 52.0 | wikitext | NULL | null |
| 6.8861212e7 | 1.0 | Informationism | 0.0 | 0.0 | 9.8279050912e-2 | 20221023093710 | 20221008124051 | 1.04758989e9 | 252.0 | wikitext | NULL | null |
| 6.8861213e7 | 3.0 | Cyrusvidallo | 0.0 | 0.0 | 0.104725212006 | 20220917231312 | 20221010150718 | 1.078878861e9 | 5370.0 | wikitext | NULL | null |
| 6.8861214e7 | 3.0 | Dj_Caboo | 0.0 | 1.0 | 0.726681395395 | 20220803125150 | 20220803125149 | 1.047568803e9 | 4864.0 | wikitext | NULL | null |
| 6.8861215e7 | 1.0 | Paka_(River_of_Blood) | 0.0 | 0.0 | 0.744205260423 | 20221021144754 | 20221011073130 | 1.061308086e9 | 113.0 | wikitext | NULL | null |
| 6.8861216e7 | 3.0 | RothariumCF | 0.0 | 0.0 | 4.7149303519e-2 | 20221020153739 | 20221020153738 | 1.063201258e9 | 2587.0 | wikitext | NULL | null |
| 6.8861218e7 | 1.0 | Dataism | 0.0 | 0.0 | 0.625787272897 | 20221027214034 | 20221028015825 | 1.047619671e9 | 592.0 | wikitext | NULL | null |
| 6.8861219e7 | 3.0 | Çıtır_Kuruyemiş | 0.0 | 0.0 | 2.3337589586e-2 | 20220803125150 | 20220803125149 | 1.047592238e9 | 1991.0 | wikitext | NULL | null |
| 6.886122e7 | 1.0 | Sanctus_(species) | 0.0 | 0.0 | 0.195594263183 | 20221021144754 | 20220808033124 | 1.047661482e9 | 207.0 | wikitext | NULL | null |
| 6.8861221e7 | 0.0 | Nick_McCloud | 0.0 | 0.0 | 0.535065289001 | 20221031185426 | 20221031201120 | 1.119234093e9 | 5266.0 | wikitext | NULL | null |
| 6.8861222e7 | 6.0 | Jimmy_Dean_baseball.jpg | 0.0 | 0.0 | 0.398933181182 | 20221101064103 | 20221101064054 | 1.049134722e9 | 523.0 | wikitext | NULL | null |
| 6.8861223e7 | 118.0 | List_of_American_Samoa_international_footballers | 1.0 | 1.0 | 0.959454885324 | 20221023093710 | 20220926111554 | 1.047569077e9 | 109.0 | wikitext | NULL | null |
| 6.8861224e7 | 6.0 | Sugar_Apple_Fairy_Tale_light_novel_volume_1_cover.jpg | 0.0 | 0.0 | 0.622304182553 | 20221023093710 | 20220724114808 | 1.049135992e9 | 823.0 | wikitext | NULL | null |
| 6.8861225e7 | 3.0 | Muneeriaha | 0.0 | 1.0 | 0.284548724445 | 20221018143335 | 20221018143334 | 1.047569127e9 | 4573.0 | wikitext | NULL | null |
| 6.8861226e7 | 3.0 | 2001:4451:711:FF00:B9BF:1E57:3391:8354 | 0.0 | 1.0 | 9.0238491785e-2 | 20221101064103 | 20221101064050 | 1.047569167e9 | 1120.0 | wikitext | NULL | null |
| 6.8861227e7 | 0.0 | Unification_of_Germany_(1871) | 1.0 | 0.0 | 0.23100564693 | 20221025054139 | 20221006071040 | 1.047665935e9 | 74.0 | wikitext | NULL | null |
| 6.8861228e7 | 1.0 | Unification_of_Germany_(1871) | 0.0 | 1.0 | 0.801136422725 | 20221021144754 | 20220828115937 | 1.047569174e9 | 60.0 | wikitext | NULL | null |
| 6.8861229e7 | 4.0 | WikiProject_Opera/SotM/October2021 | 0.0 | 1.0 | 0.139475203152 | 20220712205541 | 20221001064658 | 1.047569175e9 | 629.0 | wikitext | NULL | null |
| 6.886123e7 | 1.0 | Nick_McCloud | 0.0 | 0.0 | 0.993796185815 | 20221027063132 | 20221027154624 | 1.107821221e9 | 496.0 | wikitext | NULL | null |
| 6.8861231e7 | 0.0 | Santa_Rita_Ranch,_Texas | 0.0 | 0.0 | 0.339660693995 | 20221023074722 | 20221002214219 | 1.113706383e9 | 3286.0 | wikitext | NULL | null |
| 6.8861233e7 | 0.0 | Santa_Rita_Ranch | 1.0 | 1.0 | 0.527985029601 | 20221018165754 | 20221018165753 | 1.047569316e9 | 37.0 | wikitext | NULL | null |
| 6.8861234e7 | 0.0 | Jimmy_Dean_(baseball) | 0.0 | 0.0 | 0.197508780412 | 20221101064104 | 20221101064213 | 1.071935189e9 | 2685.0 | wikitext | NULL | null |
| 6.8861235e7 | 1.0 | Jimmy_Dean_(baseball) | 0.0 | 0.0 | 0.857538996596 | 20221021144754 | 20220921193718 | 1.092541953e9 | 152.0 | wikitext | NULL | null |
| 6.8861236e7 | 4.0 | WikiProject_Opera/OotM/October2021 | 0.0 | 0.0 | 0.742397000448 | 20220825090443 | 20220825090441 | 1.074547987e9 | 527.0 | wikitext | NULL | null |
| 6.8861237e7 | 3.0 | 212.139.107.222 | 0.0 | 0.0 | 0.520387612315 | 20220522112336 | 20221008154249 | 1.067907895e9 | 1092.0 | wikitext | NULL | null |
| 6.8861238e7 | 3.0 | 2600:1700:8E70:1C60:98E4:847:6709:2392 | 0.0 | 1.0 | 0.653851227756 | 20221020153739 | 20221020153737 | 1.047569538e9 | 1358.0 | wikitext | NULL | null |
| 6.886124e7 | 3.0 | 49.144.101.200 | 0.0 | 1.0 | 0.123016462189 | 20220520223724 | 20221008154249 | 1.047569754e9 | 1142.0 | wikitext | NULL | null |
| 6.8861241e7 | 1.0 | Aperregi | 0.0 | 1.0 | 0.341637360838 | 20221023093710 | 20221017050852 | 1.04756978e9 | 75.0 | wikitext | NULL | null |
| 6.8861242e7 | 3.0 | 106.206.200.245 | 0.0 | 0.0 | 7.9455857638e-2 | 20220520194556 | 20221008154249 | 1.047570048e9 | 1921.0 | wikitext | NULL | null |
| 6.8861244e7 | 0.0 | Watthana_Nakhon_railway_station | 0.0 | 0.0 | 0.768673755786 | 20221023074722 | 20221006195758 | 1.062250408e9 | 1397.0 | wikitext | NULL | null |
| 6.8861245e7 | 0.0 | Border_abolitionism | 1.0 | 1.0 | 0.51347671272 | 20221017181354 | 20221017181353 | 1.047569901e9 | 25.0 | wikitext | NULL | null |
| 6.8861246e7 | 3.0 | 67.9.33.134 | 0.0 | 1.0 | 0.640919888816 | 20220924045256 | 20221008154249 | 1.047569904e9 | 1101.0 | wikitext | NULL | null |
| 6.8861247e7 | 3.0 | Lukejohnb | 0.0 | 0.0 | 0.966817565856 | 20221018143335 | 20221018143333 | 1.04812191e9 | 5607.0 | wikitext | NULL | null |
| 6.8861248e7 | 0.0 | Boyfriend_(EP) | 1.0 | 1.0 | 0.617102514738 | 20221025185104 | 20221017181458 | 1.047569919e9 | 18.0 | wikitext | NULL | null |
| 6.8861249e7 | 0.0 | Border_abolition | 1.0 | 1.0 | 0.484774287438 | 20221017181354 | 20221017181353 | 1.047569933e9 | 25.0 | wikitext | NULL | null |
| 6.886125e7 | 0.0 | Boyfriend_(CKay_EP) | 1.0 | 1.0 | 0.751719313186 | 20221025185104 | 20221017181458 | 1.047569956e9 | 18.0 | wikitext | NULL | null |
| 6.8861251e7 | 6.0 | Rodney_Franklin_The_Groove.jpg | 0.0 | 0.0 | 0.775837687295 | 20221101064103 | 20221101064058 | 1.049135777e9 | 261.0 | wikitext | NULL | null |
| 6.8861253e7 | 3.0 | 2.51.100.71 | 0.0 | 0.0 | 0.386120262513 | 20220913101409 | 20221008154249 | 1.049034209e9 | 4099.0 | wikitext | NULL | null |
| 6.8861254e7 | 3.0 | Zenkai.vis | 0.0 | 1.0 | 0.716432042645 | 20220803125150 | 20220803125149 | 1.047570092e9 | 795.0 | wikitext | NULL | null |
| 6.8861255e7 | 0.0 | Dickie_Moltisanti | 1.0 | 1.0 | 8.5936212898e-2 | 20221031204923 | 20221018070338 | 1.047570098e9 | 64.0 | wikitext | NULL | null |
| 6.8861256e7 | 1.0 | Mucus_fishing_syndrome | 0.0 | 0.0 | 0.135459342002 | 20221021144754 | 20220829095139 | 1.048126693e9 | 370.0 | wikitext | NULL | null |
| 6.8861257e7 | 2.0 | Cone.exe | 0.0 | 1.0 | 0.20280217003 | 20220728025147 | 20220728025145 | 1.047570232e9 | 200.0 | wikitext | NULL | null |
| 6.8861258e7 | 0.0 | Faustina_Rehuher-Marugg | 1.0 | 0.0 | 0.785963788889 | 20221018214957 | 20221018214955 | 1.048951836e9 | 57.0 | wikitext | NULL | null |
| 6.8861259e7 | 119.0 | Scott_Seiss | 0.0 | 0.0 | 0.268538015405 | 20221023135015 | 20221026140056 | 1.079750477e9 | 177.0 | wikitext | NULL | null |
| 6.8861261e7 | 0.0 | Erkin_Tuniyaz | 0.0 | 0.0 | 0.314431987325 | 20221028134415 | 20221028134742 | 1.117709857e9 | 6115.0 | wikitext | NULL | null |
| 6.8861262e7 | 0.0 | Nong_Sang_railway_station | 0.0 | 0.0 | 0.671846718707 | 20221023074722 | 20221006195758 | 1.062250098e9 | 1364.0 | wikitext | NULL | null |
| 6.8861263e7 | 0.0 | Matthew_Smith | 0.0 | 0.0 | 0.275415680888 | 20221011054852 | 20221006152641 | 1.109493971e9 | 3273.0 | wikitext | NULL | null |
| 6.8861264e7 | 3.0 | Allthisnmore | 0.0 | 1.0 | 0.507221481034 | 20220803125150 | 20220803125149 | 1.047570484e9 | 2114.0 | wikitext | NULL | null |
| 6.8861265e7 | 4.0 | WikiProject_Opera/OotM/November2021 | 0.0 | 0.0 | 5.1560827547e-2 | 20220930232633 | 20220930232630 | 1.05392793e9 | 501.0 | wikitext | NULL | null |
| 6.8861266e7 | 2.0 | MD380/Peer_review_response | 0.0 | 1.0 | 1.5915246255e-2 | 20220728025147 | 20220728025146 | 1.047570586e9 | 1320.0 | wikitext | NULL | null |
| 6.8861267e7 | 0.0 | Caravaggio_(song) | 1.0 | 1.0 | 5.8513223111e-2 | 20221018063525 | 20221018063524 | 1.047570654e9 | 41.0 | wikitext | NULL | null |
| 6.8861268e7 | 0.0 | Matt_Smith_(disambiguation) | 1.0 | 0.0 | 0.779985103825 | 20221031131412 | 20221031131406 | 1.072194209e9 | 74.0 | wikitext | NULL | null |
| 6.8861269e7 | 0.0 | Government_College_of_Education,_Komarapalayam | 0.0 | 0.0 | 0.792413523322 | 20221023074722 | 20221015083512 | 1.092178413e9 | 3784.0 | wikitext | NULL | null |
| 6.886127e7 | 0.0 | Thomas_Burton_(16th_century_MP) | 0.0 | 0.0 | 0.470640426691 | 20221028074243 | 20221028174027 | 1.081858699e9 | 4883.0 | wikitext | NULL | null |
| 6.8861271e7 | 4.0 | WikiProject_Opera/OotM/December2021 | 0.0 | 0.0 | 0.859540386621 | 20220930232633 | 20220930232630 | 1.058929499e9 | 501.0 | wikitext | NULL | null |
| 6.8861272e7 | 3.0 | 4glorybound | 0.0 | 1.0 | 1.7730772921e-2 | 20220803125150 | 20220803125149 | 1.047570683e9 | 848.0 | wikitext | NULL | null |
| 6.8861273e7 | 3.0 | MariyaKapadia | 0.0 | 0.0 | 0.803715223697 | 20221023093710 | 20221018143338 | 1.047570826e9 | 5071.0 | wikitext | NULL | null |
| 6.8861274e7 | 0.0 | Caravaggio_(1.Cuz_song) | 1.0 | 1.0 | 0.143515280666 | 20221018063525 | 20221018063524 | 1.047570698e9 | 19.0 | wikitext | NULL | null |
| 6.8861275e7 | 4.0 | WikiProject_Women_in_Red/Metrics/October_2021 | 0.0 | 0.0 | 7.9451577497e-2 | 20221027132800 | 20221031132704 | 1.118523997e9 | 44109.0 | wikitext | NULL | null |
| 6.8861276e7 | 0.0 | Berlinia_grandiflora | 0.0 | 0.0 | 0.711850619294 | 20221031201239 | 20221031202143 | 1.071049562e9 | 2781.0 | wikitext | NULL | null |
| 6.8861278e7 | 0.0 | Ban_Dong_Bang_railway_station | 0.0 | 0.0 | 0.706852665157 | 20221023074722 | 20221009224542 | 1.115118647e9 | 1431.0 | wikitext | NULL | null |
| 6.8861279e7 | 4.0 | WikiProject_Opera/OotM/January2022 | 0.0 | 0.0 | 0.435848781725 | 20220930232633 | 20220930232630 | 1.062465836e9 | 500.0 | wikitext | NULL | null |
| 6.886128e7 | 0.0 | Zhang_Jianmin | 0.0 | 0.0 | 0.301344026944 | 20221027214104 | 20221027235739 | 1.073372152e9 | 4178.0 | wikitext | NULL | null |
| 6.8861281e7 | 2.0 | Joeytje50/JWB.js/i18n-it.js | 0.0 | 1.0 | 0.670529959486 | 20220728025147 | 20220728025146 | 1.047570865e9 | 12220.0 | javascript | NULL | null |
| 6.8861282e7 | 1.0 | Thomas_Burton_(16th_century_MP) | 0.0 | 0.0 | 9.350098045e-3 | 20221021144754 | 20220921193720 | 1.048924766e9 | 172.0 | wikitext | NULL | null |
| 6.8861283e7 | 0.0 | Deng_Jianjun | 0.0 | 0.0 | 0.267661179324 | 20221027214104 | 20221027235739 | 1.093463681e9 | 3647.0 | wikitext | NULL | null |
| 6.8861284e7 | 4.0 | WikiProject_Opera/SotM/November2021 | 0.0 | 0.0 | 0.502803439878 | 20220712205541 | 20221001064658 | 1.053210325e9 | 630.0 | wikitext | NULL | null |
| 6.8861285e7 | 3.0 | 43.245.249.1 | 0.0 | 0.0 | 4.9054269556e-2 | 20221019154858 | 20221019154858 | 1.117033247e9 | 11654.0 | wikitext | NULL | null |
| 6.8861286e7 | 0.0 | Michela_De_Rossi | 0.0 | 0.0 | 8.2959157203e-2 | 20221023074722 | 20221009074034 | 1.110917445e9 | 4008.0 | wikitext | NULL | null |
| 6.8861287e7 | 3.0 | 49.195.224.203 | 0.0 | 0.0 | 0.307749642374 | 20220911065958 | 20220911065957 | 1.048218164e9 | 596.0 | wikitext | NULL | null |
| 6.8861288e7 | 3.0 | 87.252.98.132 | 0.0 | 1.0 | 0.689187194443 | 20220521003633 | 20221008154249 | 1.047571024e9 | 982.0 | wikitext | NULL | null |
| 6.8861289e7 | 14.0 | 1909_Western_(genre)_films | 0.0 | 0.0 | 0.553211356372 | 20221023093710 | 20221004143823 | 1.048131261e9 | 167.0 | wikitext | NULL | null |
| 6.886129e7 | 0.0 | Mao_Jingwen | 0.0 | 0.0 | 0.946978053248 | 20221023074722 | 20221029034229 | 1.073372185e9 | 3579.0 | wikitext | NULL | null |
| 6.8861291e7 | 15.0 | 1909_Western_(genre)_films | 0.0 | 1.0 | 7.0420955403e-2 | 20221021144754 | 20221011073130 | 1.047571088e9 | 145.0 | wikitext | NULL | null |
| 6.8861292e7 | 2.0 | Jjfap02 | 0.0 | 0.0 | 0.21021837675 | 20220728025147 | 20220728025146 | 1.047572108e9 | 0.0 | wikitext | NULL | null |
| 6.8861293e7 | 0.0 | Prachantakham_railway_station | 0.0 | 0.0 | 2.9569522751e-2 | 20221023074722 | 20221006164258 | 1.062250108e9 | 1397.0 | wikitext | NULL | null |
| 6.8861294e7 | 0.0 | Mennekes_connector | 1.0 | 1.0 | 0.480868260488 | 20221018094340 | 20221018094339 | 1.047571125e9 | 30.0 | wikitext | NULL | null |
| 6.8861295e7 | 14.0 | Hebei_GEO_University_alumni | 0.0 | 1.0 | 0.184394101198 | 20221004174814 | 20221004174814 | 1.047571177e9 | 77.0 | wikitext | NULL | null |
| 6.8861297e7 | 0.0 | Koreatwon,_Flushing | 1.0 | 1.0 | 0.182418631803 | 20221018090411 | 20221018090411 | 1.04757122e9 | 31.0 | wikitext | NULL | null |
| 6.8861298e7 | 2.0 | JonasfromDublin | 0.0 | 0.0 | 0.575161025232 | 20220701113736 | 20220929053435 | 1.04847656e9 | 680.0 | wikitext | NULL | null |
| 6.8861299e7 | 14.0 | Hebei_GEO_University | 0.0 | 0.0 | 0.776072979072 | 20221004153754 | 20220906115717 | 1.048248632e9 | 81.0 | wikitext | NULL | null |
| 6.88613e7 | 2.0 | Daniel_Phantom/be_bold | 0.0 | 0.0 | 6.55843e-4 | 20220728025147 | 20220728025145 | 1.047571428e9 | 102.0 | wikitext | NULL | null |
| 6.8861301e7 | 3.0 | Udaibkhattak47 | 0.0 | 1.0 | 0.863627172946 | 20220911065958 | 20220911065957 | 1.047571253e9 | 1332.0 | wikitext | NULL | null |
| 6.8861302e7 | 3.0 | 2601:CF:8480:4FE0:0:0:0:ADA0 | 0.0 | 1.0 | 0.54934820989 | 20220803125150 | 20220803125148 | 1.047571266e9 | 2042.0 | wikitext | NULL | null |
| 6.8861303e7 | 4.0 | WikiProject_Opera/SotM/December2021 | 0.0 | 0.0 | 0.205011840495 | 20220712205541 | 20221001064658 | 1.058929644e9 | 630.0 | wikitext | NULL | null |
| 6.8861304e7 | 14.0 | Xi'an_University_of_Technology_faculty | 0.0 | 1.0 | 0.924229140718 | 20221004204156 | 20221004204155 | 1.047571315e9 | 51.0 | wikitext | NULL | null |
| 6.8861305e7 | 2.0 | Victuallers/sandboxada | 0.0 | 0.0 | 0.866299870286 | 20221027194953 | 20221027195135 | 1.047944226e9 | 5130.0 | wikitext | NULL | null |
| 6.8861306e7 | 14.0 | Works_based_on_The_Blue_Bird_(play) | 0.0 | 1.0 | 0.976285512658 | 20221004153754 | 20220909160223 | 1.047571337e9 | 217.0 | wikitext | NULL | null |
| 6.8861307e7 | 3.0 | The_aliens12 | 0.0 | 1.0 | 0.639515287834 | 20221020153739 | 20221020153738 | 1.047571349e9 | 1096.0 | wikitext | NULL | null |
| 6.8861308e7 | 3.0 | 139.216.147.152 | 0.0 | 0.0 | 0.973054802866 | 20220809020059 | 20221008154249 | 1.04757193e9 | 1851.0 | wikitext | NULL | null |
| 6.886131e7 | 0.0 | Alexandra_Intrator | 1.0 | 1.0 | 0.852416805851 | 20221017123344 | 20221017123344 | 1.047571389e9 | 39.0 | wikitext | NULL | null |
| 6.8861311e7 | 3.0 | 12yellow34 | 0.0 | 1.0 | 0.430878618675 | 20220909152227 | 20220803125148 | 1.047571394e9 | 2122.0 | wikitext | NULL | null |
| 6.8861312e7 | 0.0 | Khok_Makok_railway_station | 0.0 | 0.0 | 0.825873564314 | 20221023074722 | 20221006195758 | 1.062250089e9 | 1388.0 | wikitext | NULL | null |
| 6.8861313e7 | 3.0 | Barbarelarino | 0.0 | 0.0 | 0.200094277387 | 20221020153739 | 20221020153737 | 1.093575832e9 | 6196.0 | wikitext | NULL | null |
| 6.8861314e7 | 0.0 | Lauren_DiMario | 1.0 | 1.0 | 0.582812103222 | 20221018090904 | 20221018090903 | 1.047571453e9 | 39.0 | wikitext | NULL | null |
| 6.8861316e7 | 1.0 | 2021_Georgian_local_elections | 0.0 | 1.0 | 4.4789110454e-2 | 20221021144754 | 20221002174735 | 1.047571475e9 | 48.0 | wikitext | NULL | null |
| 6.8861317e7 | 0.0 | Johnny_Soprano | 1.0 | 1.0 | 0.972120480791 | 20221031204923 | 20221018085002 | 1.04757149e9 | 56.0 | wikitext | NULL | null |
| 6.886132e7 | 3.0 | 2A04:4A43:4B0F:CFEE:F504:F269:8BBD:BB24 | 0.0 | 1.0 | 0.638094985879 | 20220809020059 | 20220803125149 | 1.047571538e9 | 822.0 | wikitext | NULL | null |
| 6.8861321e7 | 4.0 | WikiProject_Opera/SotM/January2022 | 0.0 | 0.0 | 0.510326792293 | 20220712205541 | 20221001064658 | 1.062465983e9 | 629.0 | wikitext | NULL | null |
| 6.8861322e7 | 14.0 | 1908_Western_(genre)_films | 0.0 | 0.0 | 0.312987041072 | 20221023093710 | 20221004143823 | 1.048131214e9 | 167.0 | wikitext | NULL | null |
| 6.8861324e7 | 15.0 | 1908_Western_(genre)_films | 0.0 | 1.0 | 0.832204982152 | 20221021144754 | 20221011073130 | 1.047571622e9 | 145.0 | wikitext | NULL | null |
| 6.8861325e7 | 0.0 | Asclepiadeae | 1.0 | 1.0 | 0.223444737545 | 20221017150610 | 20221017150610 | 1.047571639e9 | 41.0 | wikitext | NULL | null |
| 6.8861328e7 | 0.0 | Suicide_of_Etika | 1.0 | 0.0 | 0.968860183039 | 20221101084058 | 20221018172514 | 1.04757209e9 | 19.0 | wikitext | NULL | null |
| 6.8861329e7 | 0.0 | Death_and_the_Maiden_(novel) | 0.0 | 0.0 | 1.7996125508e-2 | 20221101064104 | 20221101064213 | 1.106522755e9 | 1738.0 | wikitext | NULL | null |
| 6.886133e7 | 2.0 | Bokoharamwatch/Nigeria_had_15_mill_in_early_20cen | 0.0 | 1.0 | 0.932434301803 | 20221023093710 | 20220828202635 | 1.047571748e9 | 404.0 | wikitext | NULL | null |
| 6.8861333e7 | 1.0 | Death_and_the_Maiden_(novel) | 0.0 | 1.0 | 0.214889866833 | 20221021144754 | 20220828055326 | 1.047571849e9 | 51.0 | wikitext | NULL | null |
| 6.8861334e7 | 0.0 | Sterphus_auricaudatus | 0.0 | 0.0 | 5.9138294976e-2 | 20221023074722 | 20221010101746 | 1.048040165e9 | 1526.0 | wikitext | NULL | null |
| 6.8861335e7 | 0.0 | Peter_Heering | 1.0 | 1.0 | 0.766682327608 | 20221031135847 | 20221031135844 | 1.047572021e9 | 75.0 | wikitext | NULL | null |
| 6.8861336e7 | 3.0 | Will111111 | 0.0 | 1.0 | 0.450302477105 | 20220803125150 | 20220803125149 | 1.047572022e9 | 636.0 | wikitext | NULL | null |
| 6.8861337e7 | 1.0 | Peter_Heering | 1.0 | 1.0 | 0.31537608064 | 20221023093710 | 20221031135916 | 1.047572028e9 | 80.0 | wikitext | NULL | null |
| 6.8861339e7 | 3.0 | 157.49.165.171 | 0.0 | 0.0 | 0.285910574487 | 20220911065958 | 20220911065957 | 1.047572188e9 | 991.0 | wikitext | NULL | null |
| 6.886134e7 | 2.0 | Pyaarkarona | 0.0 | 0.0 | 0.56225487949 | 20221026145423 | 20220924172831 | 1.048526631e9 | 27.0 | wikitext | NULL | null |
| 6.8861341e7 | 3.0 | Annamaria.dmrt | 0.0 | 0.0 | 0.552257586967 | 20220909152227 | 20221010150718 | 1.077109947e9 | 10216.0 | wikitext | NULL | null |
| 6.8861343e7 | 0.0 | Build-up_(association_football) | 1.0 | 0.0 | 0.8913515291 | 20221018062603 | 20221018062601 | 1.047689819e9 | 97.0 | wikitext | NULL | null |
| 6.8861344e7 | 0.0 | Ban_Pak_Phli_railway_station | 0.0 | 0.0 | 0.471663896049 | 20221023074722 | 20221009225148 | 1.115119891e9 | 1697.0 | wikitext | NULL | null |
| 6.8861346e7 | 3.0 | Jjfap02 | 0.0 | 0.0 | 0.615230519033 | 20220917231312 | 20221010150718 | 1.078878931e9 | 7906.0 | wikitext | NULL | null |
| 6.8861347e7 | 3.0 | ContributorFromSpace | 0.0 | 0.0 | 0.274525705749 | 20220917231312 | 20221010150718 | 1.083486419e9 | 15110.0 | wikitext | NULL | null |
| 6.8861348e7 | 3.0 | 95.93.142.252 | 0.0 | 0.0 | 0.456753677188 | 20220809020059 | 20221008154250 | 1.052177915e9 | 7018.0 | wikitext | NULL | null |
| 6.8861349e7 | 0.0 | Diocese_of_the_Romanian_Army | 0.0 | 0.0 | 0.338084096938 | 20221029150903 | 20221029151500 | 1.11051681e9 | 5009.0 | wikitext | NULL | null |
| 6.886135e7 | 0.0 | Michael_and_Alice_Halkias | 1.0 | 1.0 | 0.607025844452 | 20221018094541 | 20221018094540 | 1.047572351e9 | 33.0 | wikitext | NULL | null |
| 6.8861351e7 | 2.0 | Bhanu_Pratap_Mirjapur | 0.0 | 1.0 | 0.296926693738 | 20220728025147 | 20220728025145 | 1.047572358e9 | 151.0 | wikitext | NULL | null |
| 6.8861352e7 | 4.0 | WikiProject_Spam/LinkReports/michelsogny.net | 0.0 | 0.0 | 0.864672250368 | 20221023093710 | 20221030150250 | 1.047648113e9 | 3282.0 | wikitext | NULL | null |
| 6.8861353e7 | 0.0 | Auguste_Gérôme | 0.0 | 0.0 | 0.976816109928 | 20221023074722 | 20221009182742 | 1.08435709e9 | 5689.0 | wikitext | NULL | null |
| 6.8861354e7 | 2.0 | Bluecrayon13/citing_sources | 0.0 | 0.0 | 0.18062361222 | 20221023074722 | 20221003152347 | 1.047573776e9 | 1118.0 | wikitext | NULL | null |
| 6.8861356e7 | 6.0 | Death_and_the_Maiden_(novel).jpg | 0.0 | 0.0 | 0.106007785046 | 20221101064103 | 20221101064051 | 1.068979417e9 | 710.0 | wikitext | NULL | null |
| 6.8861357e7 | 0.0 | Ban_Sang_railway_station | 0.0 | 0.0 | 0.680014719492 | 20221023074722 | 20221006195758 | 1.06225008e9 | 1364.0 | wikitext | NULL | null |
| 6.8861358e7 | 4.0 | WikiProject_Spam/UserReports/Lieangent | 0.0 | 1.0 | 0.569387701016 | 20221023093710 | 20221024211651 | 1.047572496e9 | 378.0 | wikitext | NULL | null |
| 6.8861359e7 | 3.0 | 49.244.74.112 | 0.0 | 1.0 | 0.257846683425 | 20220520223932 | 20221008154249 | 1.047572589e9 | 1482.0 | wikitext | NULL | null |
| 6.886136e7 | 0.0 | Jehiel_Beman | 0.0 | 0.0 | 0.735264228261 | 20221023074722 | 20220930144050 | 1.113243235e9 | 5038.0 | wikitext | NULL | null |
| 6.8861362e7 | 14.0 | Deaths_from_pneumonia_in_Campania | 0.0 | 0.0 | 0.794313282536 | 20220910045527 | 20220910045526 | 1.087774601e9 | 244.0 | wikitext | NULL | null |
| 6.8861364e7 | 0.0 | Underbart_i_all_misär | 1.0 | 1.0 | 0.951053716936 | 20221027235721 | 20221019065824 | 1.04757277e9 | 21.0 | wikitext | NULL | null |
| 6.8861365e7 | 0.0 | Bolshoy_Yeravna | 0.0 | 0.0 | 0.162189157614 | 20221023120021 | 20221023164516 | 1.051296588e9 | 4534.0 | wikitext | NULL | null |
| 6.8861367e7 | 0.0 | Prachinburi_railway_station | 0.0 | 0.0 | 0.572125372474 | 20221023074722 | 20221012043022 | 1.062250115e9 | 2222.0 | wikitext | NULL | null |
| 6.8861368e7 | 0.0 | Tanja_Gellenthien | 0.0 | 0.0 | 0.789369174473 | 20221023074722 | 20221015102032 | 1.098462228e9 | 5679.0 | wikitext | NULL | null |
| 6.8861369e7 | 0.0 | Bolshoy_Yeravna_Lake | 1.0 | 1.0 | 0.898150715588 | 20221017181315 | 20221017181314 | 1.047572956e9 | 29.0 | wikitext | NULL | null |
| 6.886137e7 | 0.0 | Melissa_Malzkuhn | 0.0 | 0.0 | 4.1070808107e-2 | 20221026145423 | 20221016122515 | 1.097656646e9 | 7541.0 | wikitext | NULL | null |
| 6.8861373e7 | 0.0 | Tanja_Jensen | 1.0 | 1.0 | 0.949419346791 | 20221018173817 | 20221018173816 | 1.04757307e9 | 54.0 | wikitext | NULL | null |
| 6.8861374e7 | 3.0 | 2A00:23C8:9624:F401:F07C:EBD9:6069:904 | 0.0 | 1.0 | 0.623825464426 | 20221020153739 | 20221020153737 | 1.047573096e9 | 1360.0 | wikitext | NULL | null |
| 6.8861376e7 | 0.0 | Mohamed_Shamas | 1.0 | 1.0 | 0.50537919632 | 20221031132629 | 20221031132626 | 1.047573124e9 | 77.0 | wikitext | NULL | null |
| 6.8861377e7 | 1.0 | Mohamed_Shamas | 1.0 | 1.0 | 0.367530596644 | 20221023093710 | 20221031132645 | 1.047573126e9 | 82.0 | wikitext | NULL | null |
| 6.8861378e7 | 1.0 | Bjarmian_languages | 0.0 | 1.0 | 0.908266042111 | 20221021074520 | 20221021074514 | 1.047573191e9 | 116.0 | wikitext | NULL | null |
| 6.8861379e7 | 3.0 | Jimmiboi69420 | 0.0 | 1.0 | 0.244803729315 | 20221020153739 | 20221020153737 | 1.047573193e9 | 1096.0 | wikitext | NULL | null |
| 6.8861381e7 | 3.0 | Trulymematelol | 0.0 | 1.0 | 0.392660534458 | 20221020153739 | 20221020153738 | 1.047573305e9 | 1096.0 | wikitext | NULL | null |
| 6.8861382e7 | 0.0 | Bukit_Merah_double_murders | 1.0 | 1.0 | 0.932067365049 | 20221018062605 | 20221018062605 | 1.047573341e9 | 33.0 | wikitext | NULL | null |
| 6.8861383e7 | 6.0 | Manga_Khan.jpg | 0.0 | 0.0 | 0.551662473219 | 20221101064103 | 20221101064055 | 1.049134943e9 | 441.0 | wikitext | NULL | null |
| 6.8861384e7 | 0.0 | Fourth_Son_South | 0.0 | 0.0 | 0.749225267969 | 20221023074722 | 20221017111347 | 1.064351511e9 | 3839.0 | wikitext | NULL | null |
| 6.8861385e7 | 7.0 | Manga_Khan.jpg | 0.0 | 1.0 | 0.267952500832 | 20221023135015 | 20221026140056 | 1.047573409e9 | 88.0 | wikitext | NULL | null |
| 6.8861386e7 | 15.0 | Bands_of_the_Royal_Canadian_Navy | 0.0 | 0.0 | 0.807686817247 | 20221027214635 | 20221028023705 | 1.047573862e9 | 46.0 | wikitext | NULL | null |
| 6.8861387e7 | 0.0 | Two_Point_Campus | 0.0 | 0.0 | 0.96805960018 | 20221028164354 | 20221028164718 | 1.110866562e9 | 9340.0 | wikitext | NULL | null |
| 6.8861388e7 | 1.0 | HMCS_Carleton_Band | 0.0 | 0.0 | 4.9547009558e-2 | 20221027214635 | 20221028023705 | 1.048636566e9 | 82.0 | wikitext | NULL | null |
| 6.8861389e7 | 0.0 | Angie_Ng_(murder_victim) | 1.0 | 1.0 | 0.80979520392 | 20221017124356 | 20221017124355 | 1.047573473e9 | 33.0 | wikitext | NULL | null |
| 6.886139e7 | 0.0 | Naoki_Ishikawa_(photographer) | 0.0 | 0.0 | 0.500847608481 | 20221023074722 | 20221022133156 | 1.086631936e9 | 20610.0 | wikitext | NULL | null |
| 6.8861391e7 | 0.0 | Jacaeber_Kastor | 0.0 | 0.0 | 0.693137053498 | 20221023074722 | 20221005095816 | 1.083921543e9 | 10114.0 | wikitext | NULL | null |
| 6.8861392e7 | 3.0 | 72.50.4.152 | 0.0 | 0.0 | 0.436061513625 | 20220520234022 | 20221008154249 | 1.051885194e9 | 2112.0 | wikitext | NULL | null |
| 6.8861393e7 | 0.0 | Crystal_Poh | 1.0 | 1.0 | 0.23265413571 | 20221018065333 | 20221018065332 | 1.047573522e9 | 33.0 | wikitext | NULL | null |
| 6.8861394e7 | 0.0 | List_of_English_football_transfers_winter_2021–22 | 0.0 | 0.0 | 9.0613722231e-2 | 20221031232845 | 20221101070401 | 1.106908388e9 | 168980.0 | wikitext | NULL | null |
| 6.8861396e7 | 3.0 | 66.249.83.15 | 0.0 | 1.0 | 0.971188916909 | 20220803125152 | 20220803125151 | 1.047573654e9 | 139.0 | wikitext | NULL | null |
| 6.8861397e7 | 1.0 | HMCS_York_Band | 0.0 | 1.0 | 0.885724841657 | 20221027214635 | 20221028023651 | 1.047573694e9 | 54.0 | wikitext | NULL | null |
| 6.8861398e7 | 1.0 | Naden_Band_of_Maritime_Forces_Pacific | 0.0 | 0.0 | 0.306488161949 | 20221027214635 | 20221028023705 | 1.048021873e9 | 135.0 | wikitext | NULL | null |
| 6.8861399e7 | 0.0 | James_Winston | 0.0 | 0.0 | 0.943883795624 | 20221101014137 | 20221101014135 | 1.053415443e9 | 425.0 | wikitext | NULL | null |
| 6.88614e7 | 1.0 | James_Winston | 0.0 | 0.0 | 0.467815129709 | 20220828124315 | 20221101014411 | 1.047696522e9 | 30.0 | wikitext | NULL | null |
| 6.8861401e7 | 1.0 | Navy_bands_in_Canada | 0.0 | 0.0 | 0.19484469679 | 20221027214635 | 20221028023651 | 1.048635336e9 | 81.0 | wikitext | NULL | null |
| 6.8861402e7 | 1.0 | Kurdistan_Democratic_Independence_Party_(PASOK) | 0.0 | 1.0 | 0.408845241848 | 20221004085508 | 20221004085506 | 1.047573838e9 | 240.0 | wikitext | NULL | null |
| 6.8861404e7 | 0.0 | Khlong_Bang_Phra_railway_station | 0.0 | 0.0 | 0.990281211749 | 20221023074722 | 20221006195758 | 1.06224683e9 | 1475.0 | wikitext | NULL | null |
| 6.8861405e7 | 0.0 | Hans_Nylund | 0.0 | 1.0 | 0.834262069977 | 20221031200508 | 20221026112506 | 1.047573889e9 | 1364.0 | wikitext | NULL | null |
| 6.8861406e7 | 1.0 | Tanja_Gellenthien | 0.0 | 1.0 | 0.164331170589 | 20221023093710 | 20220925082020 | 1.047573904e9 | 307.0 | wikitext | NULL | null |
| 6.8861407e7 | 2.0 | The448/citing_sources | 0.0 | 0.0 | 0.247574736219 | 20221023074722 | 20221002044719 | 1.047574802e9 | 1120.0 | wikitext | NULL | null |
| 6.8861408e7 | 1.0 | Hans_Nylund | 0.0 | 1.0 | 0.515792370116 | 20221021144754 | 20220808033124 | 1.047573937e9 | 263.0 | wikitext | NULL | null |
| 6.8861409e7 | 0.0 | Anton_Edler_von_Schmid | 0.0 | 0.0 | 0.176814380366 | 20221023074722 | 20221001064658 | 1.062330915e9 | 7688.0 | wikitext | NULL | null |
| 6.886141e7 | 1.0 | Band_of_the_Ceremonial_Guard | 0.0 | 0.0 | 0.508124846892 | 20221027214635 | 20221028023651 | 1.048635009e9 | 68.0 | wikitext | NULL | null |
| 6.8861411e7 | 3.0 | 108.30.123.67 | 0.0 | 1.0 | 0.573314255944 | 20220911065958 | 20220911065957 | 1.047573996e9 | 1105.0 | wikitext | NULL | null |
| 6.8861412e7 | 1.0 | Canadian_Forces_School_of_Music | 0.0 | 0.0 | 0.571533463874 | 20221027214635 | 20221028023705 | 1.048632918e9 | 89.0 | wikitext | NULL | null |
| 6.8861414e7 | 10.0 | Taxonomy/Echinosteliales | 0.0 | 0.0 | 0.198838577428 | 20221022045344 | 20220825074945 | 1.063127685e9 | 127.0 | wikitext | NULL | null |
| 6.8861415e7 | 3.0 | Ineedthisaccountforschool1 | 0.0 | 1.0 | 0.364789801479 | 20220809020059 | 20220803125152 | 1.047574081e9 | 844.0 | wikitext | NULL | null |
| 6.8861416e7 | 2.0 | Lisalex954 | 0.0 | 1.0 | 7.8400704439e-2 | 20220728025149 | 20220728025148 | 1.047574086e9 | 32.0 | wikitext | NULL | null |
| 6.8861417e7 | 10.0 | Taxonomy/Echinosteliaceae | 0.0 | 1.0 | 0.702956295611 | 20221022045344 | 20220825074946 | 1.047574101e9 | 172.0 | wikitext | NULL | null |
| 6.8861418e7 | 6.0 | Two_Point_Campus_cover_art.jpg | 0.0 | 0.0 | 0.522980906477 | 20221026174447 | 20220927153754 | 1.049136302e9 | 768.0 | wikitext | NULL | null |
| 6.8861419e7 | 10.0 | Taxonomy/Echinostelium | 0.0 | 1.0 | 0.956420406943 | 20221022045344 | 20220825074945 | 1.047574146e9 | 167.0 | wikitext | NULL | null |
| 6.886142e7 | 2.0 | Phoebewolf/1811–1812_New_Madrid_earthquakes/Bibliography | 0.0 | 0.0 | 0.594246278311 | 20221023074722 | 20221003060233 | 1.04762819e9 | 2681.0 | wikitext | NULL | null |
| 6.8861421e7 | 0.0 | Preng_railway_station | 0.0 | 0.0 | 0.880041487629 | 20221023074722 | 20221006195758 | 1.06224684e9 | 1541.0 | wikitext | NULL | null |
| 6.8861422e7 | 0.0 | Khlong_Udom_Chonlajorn_Halt_railway_station | 1.0 | 1.0 | 0.865059118132 | 20221018090140 | 20221018090139 | 1.047574221e9 | 49.0 | wikitext | NULL | null |
| 6.8861424e7 | 3.0 | Mossley_Music_&_Arts_Society | 0.0 | 0.0 | 0.661424077935 | 20221020153739 | 20221020153737 | 1.063101543e9 | 2696.0 | wikitext | NULL | null |
| 6.8861426e7 | 1.0 | Gordon_Bradt | 0.0 | 0.0 | 0.957536303136 | 20221029204943 | 20221029210356 | 1.054024909e9 | 569.0 | wikitext | NULL | null |
| 6.8861427e7 | 2.0 | Zofthej/Jean-Michel_Kibushi | 0.0 | 1.0 | 0.990369865089 | 20220728081650 | 20220728081649 | 1.047574374e9 | 1078.0 | wikitext | NULL | null |
| 6.8861428e7 | 0.0 | Jan_Ørke | 0.0 | 1.0 | 0.461347584239 | 20221031200508 | 20221026112552 | 1.047574375e9 | 1318.0 | wikitext | NULL | null |
| 6.8861429e7 | 1.0 | Yaduvanshi | 0.0 | 1.0 | 0.961398806928 | 20221004114453 | 20221004114452 | 1.04757438e9 | 262.0 | wikitext | NULL | null |
| 6.886143e7 | 1.0 | Royal_Roads_Military_College_Band | 0.0 | 0.0 | 0.35453297474 | 20221027214635 | 20221028023705 | 1.048635441e9 | 68.0 | wikitext | NULL | null |
| 6.8861431e7 | 1.0 | Two_Point_Campus | 0.0 | 1.0 | 0.408887073857 | 20221101070921 | 20221031235110 | 1.047574412e9 | 56.0 | wikitext | NULL | null |
| 6.8861433e7 | 1.0 | Toronto_Signals_Band | 0.0 | 0.0 | 0.293931929932 | 20221027214635 | 20221028023739 | 1.048635509e9 | 69.0 | wikitext | NULL | null |
| 6.8861434e7 | 2.0 | Asterbal/Ida_Bagus_Putra_Manuaba_Ida_Bagus_Putra_Manuaba | 0.0 | 0.0 | 0.983332311928 | 20221028074243 | 20221028174027 | 1.107157233e9 | 4003.0 | wikitext | NULL | null |
| 6.8861435e7 | 1.0 | Jan_Ørke | 0.0 | 1.0 | 0.596353789275 | 20221023093710 | 20220808033125 | 1.047574449e9 | 262.0 | wikitext | NULL | null |
| 6.8861436e7 | 0.0 | Jan_Orke | 1.0 | 1.0 | 0.358049974409 | 20221018084214 | 20221018084213 | 1.047574486e9 | 22.0 | wikitext | NULL | null |
| 6.8861437e7 | 1.0 | ADM_Capital_Foundation | 0.0 | 0.0 | 8.5939807084e-2 | 20221021144754 | 20220929065214 | 1.076810391e9 | 210.0 | wikitext | NULL | null |
| 6.8861438e7 | 0.0 | Rathana_Club | 1.0 | 1.0 | 0.223155929611 | 20221018103950 | 20221018103948 | 1.04757456e9 | 29.0 | wikitext | NULL | null |
| 6.8861439e7 | 1.0 | Chen_Yet-Sen_Family_Foundation | 0.0 | 0.0 | 0.334808324663 | 20221021144754 | 20220929065214 | 1.072370892e9 | 52.0 | wikitext | NULL | null |
| 6.886144e7 | 0.0 | 1923_West_Tennessee_State_Normal_football_team | 0.0 | 0.0 | 0.946479789312 | 20221029222957 | 20221030052910 | 1.079637328e9 | 3809.0 | wikitext | NULL | null |
| 6.8861441e7 | 0.0 | Rakhagarhi | 1.0 | 1.0 | 0.362216691923 | 20221018103837 | 20221018103834 | 1.04757469e9 | 24.0 | wikitext | NULL | null |
| 6.8861442e7 | 0.0 | Manlio_De_Domenico | 0.0 | 0.0 | 7.7860122806e-2 | 20221023074722 | 20221010224437 | 1.097202268e9 | 11894.0 | wikitext | NULL | null |
| 6.8861443e7 | 2.0 | Purlspearls/citing_sources | 0.0 | 0.0 | 0.875540256806 | 20221023074722 | 20221003152348 | 1.047575282e9 | 1092.0 | wikitext | NULL | null |
| 6.8861444e7 | 15.0 | Ships_transferred_from_the_United_States_Coast_Guard_to_the_Estonian_Border_Guard | 0.0 | 0.0 | 0.956394601084 | 20221027214635 | 20221028023651 | 1.10341493e9 | 149.0 | wikitext | NULL | null |
| 6.8861445e7 | 0.0 | Daniela_Rathana_discography | 1.0 | 1.0 | 0.279482947472 | 20221018065754 | 20221018065752 | 1.047574726e9 | 41.0 | wikitext | NULL | null |
| 6.8861446e7 | 0.0 | Hans_Saksvik | 0.0 | 1.0 | 0.690117156549 | 20221031200508 | 20221026112723 | 1.047574761e9 | 1374.0 | wikitext | NULL | null |
| 6.8861447e7 | 2.0 | Mohammadjunaidkhan.phd | 0.0 | 0.0 | 0.832856065381 | 20220728025149 | 20220728025148 | 1.04757549e9 | 0.0 | wikitext | NULL | null |
| 6.8861448e7 | 0.0 | Sarah_Story | 0.0 | 0.0 | 0.531698951545 | 20221023074722 | 20221010145952 | 1.104060753e9 | 4272.0 | wikitext | NULL | null |
| 6.8861449e7 | 1.0 | Hans_Saksvik | 0.0 | 1.0 | 0.531836971905 | 20221021144754 | 20220808033124 | 1.047574805e9 | 264.0 | wikitext | NULL | null |
| 6.886145e7 | 3.0 | Userperson1234 | 0.0 | 0.0 | 0.63767210743 | 20220917231312 | 20221010150718 | 1.07887896e9 | 8703.0 | wikitext | NULL | null |
| 6.8861451e7 | 3.0 | Nonproliferation_Policy_Education_Center | 0.0 | 1.0 | 0.69501894072 | 20221020153739 | 20221020153738 | 1.047574824e9 | 3244.0 | wikitext | NULL | null |
| 6.8861452e7 | 3.0 | Squam_Lizard | 0.0 | 1.0 | 0.928357104094 | 20220913101409 | 20220803125152 | 1.047574827e9 | 7101.0 | wikitext | NULL | null |
| 6.8861453e7 | 4.0 | Articles_for_deletion/Gosine | 0.0 | 0.0 | 8.946256082e-3 | 20220821072540 | 20220930094855 | 1.050149774e9 | 4463.0 | wikitext | NULL | null |
| 6.8861454e7 | 0.0 | Recursion_in_natural_languages | 1.0 | 1.0 | 0.450072889052 | 20221031141636 | 20221031141633 | 1.047574838e9 | 82.0 | wikitext | NULL | null |
| 6.8861455e7 | 1.0 | Sarah_Story | 0.0 | 0.0 | 0.377251601981 | 20221023093710 | 20220905193115 | 1.050898058e9 | 356.0 | wikitext | NULL | null |
| 6.8861457e7 | 101.0 | Current_events/October_2021 | 0.0 | 1.0 | 0.625686154178 | 20221021144754 | 20220808033124 | 1.047574883e9 | 119.0 | wikitext | NULL | null |
| 6.8861458e7 | 3.0 | 2A02:C7F:8E3D:8A00:20BE:4A95:C3D3:9466 | 0.0 | 1.0 | 0.967094321396 | 20220913101409 | 20220803215305 | 1.047574887e9 | 1559.0 | wikitext | NULL | null |
| 6.8861459e7 | 0.0 | Anton_von_Schmid | 1.0 | 1.0 | 4.7907270015e-2 | 20221017124751 | 20221017124750 | 1.047574913e9 | 36.0 | wikitext | NULL | null |
| 6.886146e7 | 0.0 | Sean_Rhyan | 0.0 | 0.0 | 0.431785935971 | 20221031185426 | 20221031200926 | 1.117906364e9 | 7144.0 | wikitext | NULL | null |
| 6.8861461e7 | 6.0 | William_Breda.png | 0.0 | 0.0 | 0.890394697256 | 20221101064103 | 20221101064100 | 1.049136407e9 | 528.0 | wikitext | NULL | null |
| 6.8861462e7 | 1.0 | Medium_Support_Vehicle_System | 0.0 | 0.0 | 0.81325499521 | 20221027214635 | 20221028023705 | 1.04863531e9 | 68.0 | wikitext | NULL | null |
| 6.8861463e7 | 0.0 | Epistlar | 1.0 | 1.0 | 0.474092779035 | 20221028111314 | 20221018071936 | 1.047575069e9 | 30.0 | wikitext | NULL | null |
| 6.8861464e7 | 6.0 | Pentatonix_The_Lucky_Ones_Album_Art.jpeg | 0.0 | 0.0 | 0.426200252786 | 20221101064103 | 20221101064056 | 1.049135363e9 | 651.0 | wikitext | NULL | null |
| 6.8861465e7 | 6.0 | Rama_Khan.png | 0.0 | 0.0 | 0.734086696952 | 20221101064103 | 20221101064057 | 1.049135695e9 | 433.0 | wikitext | NULL | null |
| 6.8861466e7 | 0.0 | Kåre_Bjørnsen | 0.0 | 0.0 | 1.462314077e-2 | 20221031200508 | 20221026104446 | 1.111599351e9 | 1462.0 | wikitext | NULL | null |
| 6.8861467e7 | 0.0 | Epistlar_(EP) | 1.0 | 1.0 | 0.519263226207 | 20221028111314 | 20221018071936 | 1.047575112e9 | 30.0 | wikitext | NULL | null |
| 6.8861468e7 | 1.0 | Anton_Edler_von_Schmid | 0.0 | 0.0 | 0.444257704902 | 20221021144754 | 20221001064658 | 1.053871344e9 | 331.0 | wikitext | NULL | null |
| 6.8861469e7 | 7.0 | Rama_Khan.png | 0.0 | 1.0 | 7.3324712703e-2 | 20221023135015 | 20221026140056 | 1.047575148e9 | 88.0 | wikitext | NULL | null |
| 6.8861471e7 | 1.0 | Kåre_Bjørnsen | 0.0 | 1.0 | 0.25925871008 | 20221021144754 | 20220808033125 | 1.047575166e9 | 265.0 | wikitext | NULL | null |
| 6.8861472e7 | 0.0 | Kare_Bjornsen | 1.0 | 1.0 | 0.204720382591 | 20221018085804 | 20221018085801 | 1.047575205e9 | 28.0 | wikitext | NULL | null |
| 6.8861473e7 | 2.0 | Javad5351/Sample_page | 0.0 | 1.0 | 0.279989810259 | 20221023074722 | 20220820005754 | 1.047575233e9 | 2914.0 | wikitext | NULL | null |
| 6.8861474e7 | 3.0 | SWFLucknow | 0.0 | 0.0 | 0.467292303819 | 20220522054029 | 20221003060233 | 1.051985056e9 | 2566.0 | wikitext | NULL | null |
| 6.8861476e7 | 3.0 | 2A02:C7F:6080:4400:89A3:FCE4:D32:E468 | 0.0 | 1.0 | 0.523401492936 | 20220803125152 | 20220803125151 | 1.04757527e9 | 803.0 | wikitext | NULL | null |
| 6.8861477e7 | 3.0 | Ismailismo | 0.0 | 0.0 | 0.587531600576 | 20221023142513 | 20221003060233 | 1.085417666e9 | 9696.0 | wikitext | NULL | null |
| 6.8861478e7 | 3.0 | 114.122.41.123 | 0.0 | 1.0 | 9.3870138167e-2 | 20220520195336 | 20221008154249 | 1.047575365e9 | 1429.0 | wikitext | NULL | null |
| 6.8861479e7 | 2.0 | Droid248 | 0.0 | 1.0 | 0.310798261387 | 20220728025149 | 20220728025148 | 1.047575375e9 | 1080.0 | wikitext | NULL | null |
| 6.886148e7 | 1.0 | Alexander_Madsen | 0.0 | 0.0 | 0.540968099849 | 20221023093710 | 20220808033125 | 1.047585226e9 | 240.0 | wikitext | NULL | null |
| 6.8861481e7 | 2.0 | Droid248/sandbox | 0.0 | 1.0 | 0.18825135305 | 20221023093710 | 20220803125344 | 1.047575472e9 | 1103.0 | wikitext | NULL | null |
| 6.8861482e7 | 0.0 | Don_Si_Non_railway_station | 0.0 | 0.0 | 0.969199138449 | 20221023074722 | 20221006195759 | 1.062246825e9 | 3011.0 | wikitext | NULL | null |
| 6.8861483e7 | 0.0 | Phil_L._Hudson_Municipal_Airport | 1.0 | 1.0 | 0.720833694848 | 20221031140001 | 20221031135956 | 1.047575574e9 | 74.0 | wikitext | NULL | null |
| 6.8861485e7 | 1.0 | Middle_European_Class | 0.0 | 1.0 | 0.247475979508 | 20221021144754 | 20220903095647 | 1.047575589e9 | 49.0 | wikitext | NULL | null |
| 6.8861486e7 | 100.0 | Current_events/September_2021/Sidebar | 0.0 | 0.0 | 0.282004274702 | 20221028000118 | 20221028000215 | 1.111672171e9 | 20181.0 | wikitext | NULL | null |
| 6.8861487e7 | 1.0 | Results_of_the_2021_German_federal_election | 0.0 | 0.0 | 0.787946801893 | 20221027214034 | 20221028015922 | 1.048662836e9 | 97.0 | wikitext | NULL | null |
| 6.8861488e7 | 3.0 | 49.144.3.64 | 0.0 | 1.0 | 0.361861032064 | 20220520223729 | 20221008154249 | 1.04757564e9 | 1184.0 | wikitext | NULL | null |
| 6.8861489e7 | 10.0 | Southern_Brave_squad | 0.0 | 0.0 | 0.481267317548 | 20220822174002 | 20220822174002 | 1.105983346e9 | 1790.0 | wikitext | NULL | null |
| 6.886149e7 | 3.0 | RhonJean | 0.0 | 1.0 | 0.652303954524 | 20220803125152 | 20220803125152 | 1.047575659e9 | 147.0 | wikitext | NULL | null |
| 6.8861491e7 | 3.0 | Allison_SoCHC | 0.0 | 0.0 | 0.608747775355 | 20221020153739 | 20221020153737 | 1.079876563e9 | 6395.0 | wikitext | NULL | null |
| 6.8861495e7 | 6.0 | Campari_bottle.jpg | 0.0 | 0.0 | 0.373193395334 | 20221023093710 | 20221022095034 | 1.049133858e9 | 733.0 | wikitext | NULL | null |
| 6.8861496e7 | 0.0 | James_Winston_(thespian) | 0.0 | 0.0 | 0.842387330941 | 20221023074722 | 20221018084330 | 1.075871926e9 | 765.0 | wikitext | NULL | null |
| 6.8861497e7 | 3.0 | 2600:100F:B001:316C:4C25:F730:1D30:B62B | 0.0 | 1.0 | 0.596354889418 | 20220825090443 | 20220825090439 | 1.047575766e9 | 6356.0 | wikitext | NULL | null |
| 6.8861498e7 | 0.0 | 2021-22_Serbian_Cup | 1.0 | 1.0 | 0.166172006199 | 20221024003111 | 20221024144846 | 1.047575769e9 | 178.0 | wikitext | NULL | null |
| 6.8861499e7 | 3.0 | Wonyounghoon | 0.0 | 0.0 | 9.9381170001e-2 | 20220914210635 | 20220914212118 | 1.049068885e9 | 15654.0 | wikitext | NULL | null |
| 6.88615e7 | 1.0 | 2014-15_ISTAF_SuperSeries | 1.0 | 1.0 | 0.956051031476 | 20221024003111 | 20221024144846 | 1.047575793e9 | 211.0 | wikitext | NULL | null |
| 6.8861501e7 | 1.0 | 4_Field_Ambulance_(Canada) | 0.0 | 1.0 | 0.904763052959 | 20221027214635 | 20221028023705 | 1.047575805e9 | 41.0 | wikitext | NULL | null |
| 6.8861502e7 | 0.0 | 2021-22_EHF_European_League | 1.0 | 1.0 | 0.705241294012 | 20221024003111 | 20221024144846 | 1.047575842e9 | 202.0 | wikitext | NULL | null |
| 6.8861503e7 | 0.0 | Phan_Thong_railway_station | 0.0 | 0.0 | 0.791486566217 | 20221023074722 | 20221006195759 | 1.062247208e9 | 2992.0 | wikitext | NULL | null |
| 6.8861504e7 | 0.0 | Anton_Von_Schmid | 1.0 | 1.0 | 0.712684783764 | 20221017124751 | 20221017124750 | 1.047575847e9 | 36.0 | wikitext | NULL | null |
| 6.8861505e7 | 2.0 | Sergej319 | 0.0 | 0.0 | 0.633951446825 | 20221028052204 | 20221028053417 | 1.04757633e9 | 658.0 | wikitext | NULL | null |
| 6.8861506e7 | 1.0 | 4_Health_Services_Group | 0.0 | 1.0 | 0.279722095376 | 20221027214635 | 20221028023739 | 1.047575851e9 | 41.0 | wikitext | NULL | null |
| 6.8861507e7 | 0.0 | 2021_World_Wrestling_Championships_–_Men's_freestyle_61_kg | 0.0 | 0.0 | 0.763995298751 | 20221031224549 | 20221031224603 | 1.11885109e9 | 8763.0 | wikitext | NULL | null |
| 6.8861508e7 | 0.0 | Listed_buildings_in_Cudworth,_South_Yorkshire | 0.0 | 0.0 | 0.386215955183 | 20221025021906 | 20221025060121 | 1.081558101e9 | 3246.0 | wikitext | NULL | null |
| 6.886151e7 | 0.0 | Heropanti_2_(2022_film) | 1.0 | 0.0 | 0.583099660144 | 20221019200123 | 20221004121157 | 1.050306308e9 | 63.0 | wikitext | NULL | null |
| 6.8861511e7 | 0.0 | List_of_English_football_transfers_winter_2021-22 | 1.0 | 1.0 | 0.813798611037 | 20221024003111 | 20221024144846 | 1.047575921e9 | 268.0 | wikitext | NULL | null |
| 6.8861512e7 | 3.0 | QPEdson | 0.0 | 0.0 | 0.374551694661 | 20220911065958 | 20220911065957 | 1.047580158e9 | 1800.0 | wikitext | NULL | null |
| 6.8861513e7 | 1.0 | Listed_buildings_in_Cudworth,_South_Yorkshire | 0.0 | 0.0 | 0.457781165398 | 20221023093710 | 20220929112058 | 1.090601968e9 | 263.0 | wikitext | NULL | null |
| 6.8861514e7 | 0.0 | 2021-22_Liga_IV_Galați | 1.0 | 0.0 | 0.744574310445 | 20221101060548 | 20221101060645 | 1.057590759e9 | 272.0 | wikitext | NULL | null |
| 6.8861515e7 | 11.0 | Southern_Brave_squad | 0.0 | 1.0 | 0.845539608806 | 20221023135015 | 20221025195513 | 1.047575944e9 | 23.0 | wikitext | NULL | null |
| 6.8861516e7 | 0.0 | Wilhelm_Eliassen | 0.0 | 0.0 | 0.951302242849 | 20221031200508 | 20221030153506 | 1.098801875e9 | 1437.0 | wikitext | NULL | null |
| 6.8861517e7 | 1.0 | No._4_Casualty_Clearing_Station_(Canada) | 0.0 | 1.0 | 0.373859467709 | 20221027214635 | 20221028023651 | 1.047575981e9 | 41.0 | wikitext | NULL | null |
| 6.8861518e7 | 0.0 | Tornado_outbreak_sequence_of_May_4-10,_1933 | 1.0 | 1.0 | 0.752944651488 | 20221024003111 | 20221024144846 | 1.047575996e9 | 250.0 | wikitext | NULL | null |
| 6.8861519e7 | 1.0 | Wilhelm_Eliassen | 0.0 | 1.0 | 0.772792459872 | 20221021144754 | 20220808033125 | 1.047576012e9 | 268.0 | wikitext | NULL | null |
| 6.8861521e7 | 1.0 | 2021-22_Liga_IV_Galați | 1.0 | 1.0 | 0.366768119492 | 20221024003111 | 20221024144846 | 1.047576039e9 | 205.0 | wikitext | NULL | null |
| 6.8861522e7 | 1.0 | Tornado_outbreak_sequence_of_May_4-10,_1933 | 1.0 | 1.0 | 0.992471269296 | 20221024003111 | 20221024144846 | 1.047576085e9 | 265.0 | wikitext | NULL | null |
| 6.8861523e7 | 0.0 | Servant_of_the_Mind | 0.0 | 0.0 | 0.734067114006 | 20221029202802 | 20221029204109 | 1.08806323e9 | 21859.0 | wikitext | NULL | null |
| 6.8861524e7 | 15.0 | N-Train_members | 0.0 | 1.0 | 0.671184852634 | 20221021144754 | 20220913203012 | 1.047576113e9 | 55.0 | wikitext | NULL | null |
| 6.8861525e7 | 0.0 | 2021_Asian_Table_Tennis_Championships_-_Women's_team | 1.0 | 1.0 | 0.886355683538 | 20221024003111 | 20221024144847 | 1.047576118e9 | 277.0 | wikitext | NULL | null |
| 6.8861526e7 | 0.0 | Servant_of_the_Mind_(album) | 1.0 | 0.0 | 0.919372430568 | 20221021041923 | 20221021041922 | 1.058526987e9 | 33.0 | wikitext | NULL | null |
| 6.8861527e7 | 14.0 | The_Hundred_(cricket)_navigational_boxes | 0.0 | 0.0 | 0.82015890849 | 20221023093710 | 20220912212504 | 1.050346429e9 | 83.0 | wikitext | NULL | null |
| 6.8861528e7 | 15.0 | The_Hundred_(cricket)_navigational_boxes | 0.0 | 1.0 | 0.386911736267 | 20221023135015 | 20221026140056 | 1.047576157e9 | 23.0 | wikitext | NULL | null |
| 6.8861529e7 | 0.0 | Servant_of_the_Mind_(Volbeat_album) | 1.0 | 0.0 | 0.75130791004 | 20221021041923 | 20221021041922 | 1.058527029e9 | 33.0 | wikitext | NULL | null |
| 6.886153e7 | 0.0 | +-=÷x_Tour | 1.0 | 1.0 | 0.413178095315 | 20221029075509 | 20221024144847 | 1.047576171e9 | 154.0 | wikitext | NULL | null |
| 6.8861531e7 | 3.0 | 202.53.6.50 | 0.0 | 1.0 | 0.468829287196 | 20220803125152 | 20220803125151 | 1.047576177e9 | 780.0 | wikitext | NULL | null |
| 6.8861532e7 | 0.0 | Bang_Phra_railway_station | 0.0 | 0.0 | 0.225654598504 | 20221023074722 | 20221009225432 | 1.115120615e9 | 3003.0 | wikitext | NULL | null |
| 6.8861533e7 | 0.0 | Pucheng-Meizhou_railway | 1.0 | 1.0 | 0.527611556057 | 20221024003111 | 20221024144846 | 1.047576246e9 | 190.0 | wikitext | NULL | null |
| 6.8861534e7 | 6.0 | HKU23_Logo.png | 0.0 | 0.0 | 0.312190070195 | 20221101064103 | 20221101064053 | 1.049134554e9 | 715.0 | wikitext | NULL | null |
| 6.8861535e7 | 1.0 | Non-Public_Property | 0.0 | 0.0 | 0.103550822165 | 20221027214635 | 20221028023738 | 1.048022726e9 | 56.0 | wikitext | NULL | null |
| 6.8861536e7 | 0.0 | 2021_World_Wrestling_Championships_-_Men's_freestyle_61_kg | 1.0 | 1.0 | 0.118874152145 | 20221029081732 | 20221024144846 | 1.047576321e9 | 295.0 | wikitext | NULL | null |
| 6.8861537e7 | 1.0 | 2021-22_European_winter_storm_season | 1.0 | 0.0 | 0.847009798845 | 20221024003111 | 20221024144847 | 1.072785624e9 | 315.0 | wikitext | NULL | null |
| 6.8861539e7 | 2.0 | Gng1999/sandbox | 0.0 | 1.0 | 0.556154596235 | 20220728025149 | 20220728025148 | 1.047576368e9 | 23.0 | wikitext | NULL | null |
| 6.886154e7 | 1.0 | Pucheng-Meizhou_railway | 1.0 | 1.0 | 0.566333012756 | 20221024003111 | 20221024144847 | 1.047576371e9 | 205.0 | wikitext | NULL | null |
| 6.8861541e7 | 2.0 | Sammysterns/citing_sources | 0.0 | 0.0 | 0.287798894536 | 20221023074722 | 20221003152348 | 1.047576961e9 | 1119.0 | wikitext | NULL | null |
| 6.8861542e7 | 2.0 | Johnmilton2/sandbox | 0.0 | 1.0 | 0.897323524058 | 20220728025149 | 20220728025148 | 1.047576433e9 | 19.0 | wikitext | NULL | null |
| 6.8861543e7 | 3.0 | Sijark | 0.0 | 1.0 | 0.801482013454 | 20220803125152 | 20220803125152 | 1.047576495e9 | 1107.0 | wikitext | NULL | null |
| 6.8861544e7 | 2.0 | Fmei00/Non-rapid_eye_movement_sleep | 0.0 | 0.0 | 0.932286206975 | 20221023074722 | 20221003060233 | 1.047683031e9 | 6302.0 | wikitext | NULL | null |
| 6.8861545e7 | 0.0 | Kåre_Aasgaard | 0.0 | 0.0 | 0.966844812768 | 20221031200508 | 20221026104438 | 1.10898553e9 | 1415.0 | wikitext | NULL | null |
| 6.8861546e7 | 2.0 | Vitsuha/notes_(i) | 0.0 | 0.0 | 0.250838806716 | 20220906030707 | 20220906030707 | 1.108753823e9 | 3.0 | wikitext | NULL | null |
| 6.8861547e7 | 2.0 | Flabrador/be_bold | 0.0 | 0.0 | 0.82306296176 | 20220728025149 | 20220728025148 | 1.04759136e9 | 189.0 | wikitext | NULL | null |
| 6.8861549e7 | 1.0 | Kåre_Aasgaard | 0.0 | 1.0 | 0.28314638423 | 20221023093710 | 20220808033125 | 1.047576603e9 | 267.0 | wikitext | NULL | null |
| 6.886155e7 | 3.0 | Eggsareslimy | 0.0 | 0.0 | 0.650776568678 | 20221020153739 | 20221020153737 | 1.04757734e9 | 1766.0 | wikitext | NULL | null |
| 6.8861551e7 | 0.0 | Kare_Aasgaard | 1.0 | 1.0 | 7.5414990947e-2 | 20221018085804 | 20221018085801 | 1.047576654e9 | 27.0 | wikitext | NULL | null |
| 6.8861552e7 | 0.0 | Ban_Huai_Khwang_railway_station | 0.0 | 0.0 | 0.286773424358 | 20221023074722 | 20221009224825 | 1.115119202e9 | 3119.0 | wikitext | NULL | null |
| 6.8861553e7 | 0.0 | Jaume_Masiá | 1.0 | 1.0 | 0.27830484156 | 20221031121826 | 20221031121822 | 1.04757679e9 | 73.0 | wikitext | NULL | null |
| 6.8861554e7 | 1.0 | Jaume_Masiá | 1.0 | 1.0 | 0.581284200718 | 20221023093710 | 20221031121857 | 1.047576792e9 | 78.0 | wikitext | NULL | null |
| 6.8861555e7 | 3.0 | Gcverberkmoespstcc | 0.0 | 1.0 | 0.193024734681 | 20220803125152 | 20220803125151 | 1.047576812e9 | 407.0 | wikitext | NULL | null |
| 6.8861556e7 | 4.0 | Featured_picture_candidates/Australian_Cattle_Dog_with_injured_leg | 0.0 | 0.0 | 0.844833567862 | 20211011204245 | 20221005182147 | 1.049437822e9 | 1884.0 | wikitext | NULL | null |
| 6.8861557e7 | 118.0 | Cornal_Tower | 1.0 | 1.0 | 0.919870007496 | 20221023093710 | 20220926111554 | 1.047576883e9 | 73.0 | wikitext | NULL | null |
| 6.8861558e7 | 119.0 | Cornal_Tower | 1.0 | 1.0 | 0.672623057336 | 20221023093710 | 20220926111554 | 1.047576888e9 | 78.0 | wikitext | NULL | null |
| 6.8861559e7 | 4.0 | Meetup/DC/Vaccine_Safety_Wikipedia_Edit-a-thon_WCNA | 0.0 | 0.0 | 0.461617922043 | 20221021213221 | 20221006152628 | 1.063337222e9 | 18386.0 | wikitext | NULL | null |
| 6.8861561e7 | 0.0 | Roald_Paulsen | 0.0 | 1.0 | 0.8363145374 | 20221031200508 | 20221028223847 | 1.047576964e9 | 1343.0 | wikitext | NULL | null |
| 6.8861563e7 | 2.0 | Acomplex/sandbox | 1.0 | 0.0 | 0.553532459364 | 20221023093710 | 20220926111554 | 1.054901509e9 | 93.0 | wikitext | NULL | null |
| 6.8861564e7 | 3.0 | Acomplex | 0.0 | 0.0 | 0.493133900092 | 20220913101409 | 20220803215305 | 1.053687135e9 | 4330.0 | wikitext | NULL | null |
| 6.8861565e7 | 1.0 | Roald_Paulsen | 0.0 | 1.0 | 0.614880810445 | 20221023093710 | 20220808033125 | 1.047577026e9 | 267.0 | wikitext | NULL | null |
| 6.8861566e7 | 3.0 | 114.10.11.192 | 0.0 | 1.0 | 0.648033010974 | 20220520195330 | 20221008154249 | 1.047577044e9 | 912.0 | wikitext | NULL | null |
| 6.8861568e7 | 0.0 | Anthonio_Sanjairag | 0.0 | 0.0 | 0.798989476049 | 20221031200508 | 20221022171332 | 1.099084629e9 | 4105.0 | wikitext | NULL | null |
| 6.886157e7 | 0.0 | Tor_Wæhler | 0.0 | 1.0 | 0.736069985338 | 20221031200508 | 20221030043917 | 1.047577283e9 | 1327.0 | wikitext | NULL | null |
| 6.8861571e7 | 6.0 | Cynar_bottles.jpg | 0.0 | 0.0 | 0.653375547803 | 20221023093710 | 20221022095041 | 1.10190775e9 | 507.0 | wikitext | NULL | null |
| 6.8861573e7 | 3.0 | Efraín.gms1981 | 0.0 | 1.0 | 0.214802695027 | 20220803125152 | 20220803125151 | 1.047577334e9 | 870.0 | wikitext | NULL | null |
| 6.8861574e7 | 3.0 | Lili_Strumf | 0.0 | 0.0 | 0.282928251397 | 20220913101409 | 20221010150718 | 1.080542122e9 | 9141.0 | wikitext | NULL | null |
| 6.8861575e7 | 0.0 | Mulholland_Drive_(album) | 0.0 | 0.0 | 0.371721268812 | 20221101084220 | 20221101084317 | 1.105943769e9 | 8628.0 | wikitext | NULL | null |
| 6.8861576e7 | 1.0 | Tor_Wæhler | 0.0 | 1.0 | 0.913285632129 | 20221023093710 | 20220808033125 | 1.047577364e9 | 265.0 | wikitext | NULL | null |
| 6.8861577e7 | 0.0 | Chonburi_railway_station | 0.0 | 0.0 | 0.561010395256 | 20221023074722 | 20221012043025 | 1.062247189e9 | 3598.0 | wikitext | NULL | null |
| 6.8861578e7 | 0.0 | Tor_Waehler | 1.0 | 1.0 | 0.590057216079 | 20221018182303 | 20221018182303 | 1.047577423e9 | 24.0 | wikitext | NULL | null |
| 6.8861579e7 | 3.0 | Luk0121 | 0.0 | 1.0 | 0.445646170257 | 20221018143339 | 20221018143338 | 1.047577457e9 | 4517.0 | wikitext | NULL | null |
| 6.886158e7 | 3.0 | Hamzahalloubi | 0.0 | 0.0 | 0.545223979699 | 20220809232000 | 20221010150718 | 1.04758432e9 | 8671.0 | wikitext | NULL | null |
| 6.8861581e7 | 3.0 | Itssanjeet | 0.0 | 1.0 | 0.798677938355 | 20221002143159 | 20221002143157 | 1.0475775e9 | 3206.0 | wikitext | NULL | null |
| 6.8861582e7 | 0.0 | 1898_Nebraska_gubernatorial_election | 0.0 | 0.0 | 0.721812551772 | 20221031203138 | 20221031232413 | 1.088064221e9 | 6402.0 | wikitext | NULL | null |
| 6.8861583e7 | 0.0 | Circuit_Laundry | 1.0 | 1.0 | 3.6330973482e-2 | 20221018064543 | 20221018064542 | 1.04757751e9 | 27.0 | wikitext | NULL | null |
| 6.8861584e7 | 3.0 | Dannybai2020 | 0.0 | 0.0 | 0.611355520962 | 20221020153739 | 20221020153737 | 1.047578603e9 | 1666.0 | wikitext | NULL | null |
| 6.8861585e7 | 0.0 | Cynanchum_pulchellum | 0.0 | 0.0 | 0.287379829217 | 20221031201239 | 20221031202144 | 1.047651649e9 | 1238.0 | wikitext | NULL | null |
| 6.8861587e7 | 14.0 | Grijalva_River | 0.0 | 0.0 | 0.914342930927 | 20221004153754 | 20220909225100 | 1.052140725e9 | 204.0 | wikitext | NULL | null |
| 6.8861588e7 | 119.0 | A_Blue_Flower | 0.0 | 0.0 | 6.4172306652e-2 | 20221023135015 | 20221025131059 | 1.104259038e9 | 143.0 | wikitext | NULL | null |
| 6.8861589e7 | 3.0 | Leonida-hr | 0.0 | 0.0 | 0.911740787173 | 20220821074719 | 20221008133447 | 1.104259033e9 | 5701.0 | wikitext | NULL | null |
| 6.886159e7 | 0.0 | Svein_Hammerø | 0.0 | 1.0 | 0.947982816025 | 20221031200508 | 20221029190021 | 1.047577717e9 | 1350.0 | wikitext | NULL | null |
| 6.8861591e7 | 15.0 | Grijalva_River | 0.0 | 1.0 | 0.107262643926 | 20221021144754 | 20220829095139 | 1.04757772e9 | 45.0 | wikitext | NULL | null |
| 6.8861592e7 | 1.0 | Svein_Hammerø | 0.0 | 1.0 | 0.381694237043 | 20221023093710 | 20220808033125 | 1.047577784e9 | 267.0 | wikitext | NULL | null |
| 6.8861593e7 | 0.0 | Svein_Hammero | 1.0 | 1.0 | 0.627646051687 | 20221018172923 | 20221018172921 | 1.047577826e9 | 27.0 | wikitext | NULL | null |
| 6.8861594e7 | 3.0 | Gerard_Alferez | 0.0 | 0.0 | 0.497730577097 | 20220917231312 | 20221010150718 | 1.078879036e9 | 5238.0 | wikitext | NULL | null |
| 6.8861596e7 | 0.0 | Savage_River_(TV_series) | 0.0 | 0.0 | 0.832486029373 | 20221026155522 | 20221026161217 | 1.116432979e9 | 11382.0 | wikitext | NULL | null |
| 6.8861597e7 | 3.0 | 2A02:2F08:200B:6900:A57E:D148:8BD7:787C | 0.0 | 0.0 | 0.721580578781 | 20221026145423 | 20221020153737 | 1.047581199e9 | 2076.0 | wikitext | NULL | null |
| 6.8861598e7 | 0.0 | Børge_Josefsen | 0.0 | 1.0 | 0.755900706387 | 20221031200508 | 20221026112112 | 1.047578e9 | 1358.0 | wikitext | NULL | null |
| 6.8861599e7 | 1.0 | Børge_Josefsen | 0.0 | 1.0 | 0.845328233809 | 20221023093710 | 20220808033126 | 1.047578045e9 | 268.0 | wikitext | NULL | null |
| 6.88616e7 | 0.0 | Borge_Josefsen | 1.0 | 1.0 | 0.242849578734 | 20221017181359 | 20221017181358 | 1.047578088e9 | 28.0 | wikitext | NULL | null |
| 6.8861601e7 | 3.0 | Visiontopgs | 0.0 | 0.0 | 0.565732359902 | 20221002143159 | 20221002143157 | 1.048130301e9 | 4902.0 | wikitext | NULL | null |
| 6.8861602e7 | 0.0 | The_Dancing_Druids | 0.0 | 0.0 | 0.186123261878 | 20221101080448 | 20221101080646 | 1.106522837e9 | 1868.0 | wikitext | NULL | null |
| 6.8861603e7 | 6.0 | Aperol_bottle.jpeg | 0.0 | 0.0 | 0.551915296732 | 20221023093710 | 20221022095041 | 1.049133697e9 | 469.0 | wikitext | NULL | null |
| 6.8861605e7 | 3.0 | Newmalayalam | 0.0 | 0.0 | 0.44932761902 | 20220917231312 | 20221010150719 | 1.05243175e9 | 7883.0 | wikitext | NULL | null |
| 6.8861606e7 | 3.0 | 2409:4043:2C9A:8945:0:0:B4B:1208 | 0.0 | 1.0 | 0.337823910212 | 20220520213805 | 20221008154250 | 1.047578209e9 | 1088.0 | wikitext | NULL | null |
| 6.8861607e7 | 0.0 | Finn_Vådahl | 0.0 | 1.0 | 0.221588568266 | 20221031200508 | 20221026113035 | 1.047578288e9 | 1333.0 | wikitext | NULL | null |
| 6.8861608e7 | 3.0 | Mohammed12313893/TWA | 0.0 | 0.0 | 0.20839706644 | 20211001142312 | 20220929165626 | 1.047578587e9 | 1245.0 | wikitext | NULL | null |
| 6.8861609e7 | 3.0 | Alpha23212 | 0.0 | 1.0 | 0.413322656595 | 20220803125152 | 20220803125151 | 1.047578388e9 | 753.0 | wikitext | NULL | null |
| 6.886161e7 | 1.0 | The_Dancing_Druids | 0.0 | 1.0 | 0.757476913698 | 20221021144754 | 20220828055325 | 1.047578418e9 | 51.0 | wikitext | NULL | null |
| 6.8861611e7 | 0.0 | Rutherford_B._Hayes_Presidential_Library_&_Museums | 1.0 | 1.0 | 5.8720509783e-2 | 20221018165227 | 20221018165226 | 1.04757844e9 | 53.0 | wikitext | NULL | null |
| 6.8861612e7 | 118.0 | St._Stefan_Serbian_Orthodox_Church_(Ottawa) | 1.0 | 1.0 | 0.380131768953 | 20221023093710 | 20220926111555 | 1.047578454e9 | 104.0 | wikitext | NULL | null |
| 6.8861613e7 | 1.0 | Finn_Vådahl | 0.0 | 1.0 | 0.156301444028 | 20221023093710 | 20220808033126 | 1.047578459e9 | 265.0 | wikitext | NULL | null |
| 6.8861614e7 | 1.0 | St._Stefan_Serbian_Orthodox_Church_(Ottawa) | 0.0 | 0.0 | 0.834079599272 | 20221023135015 | 20221026140056 | 1.072580834e9 | 311.0 | wikitext | NULL | null |
| 6.8861615e7 | 0.0 | Finn_Vadahl | 1.0 | 1.0 | 0.693225527805 | 20221018073416 | 20221018073415 | 1.047578504e9 | 25.0 | wikitext | NULL | null |
| 6.8861616e7 | 2.0 | Johnmilton2 | 0.0 | 0.0 | 0.920629466627 | 20220728025149 | 20220728025148 | 1.047759069e9 | 0.0 | wikitext | NULL | null |
| 6.8861617e7 | 0.0 | Oxalis_bifida | 0.0 | 0.0 | 0.148172456274 | 20221031201239 | 20221031202144 | 1.090526284e9 | 2900.0 | wikitext | NULL | null |
| 6.8861618e7 | 14.0 | 2022_song_contests | 0.0 | 1.0 | 3.5658509896e-2 | 20221028205914 | 20221005232416 | 1.047578547e9 | 211.0 | wikitext | NULL | null |
| 6.8861619e7 | 3.0 | 2402:8100:3A0A:4DF2:D645:A8B5:8E9E:CF73 | 0.0 | 1.0 | 0.633554224694 | 20220803125152 | 20220803125151 | 1.047578555e9 | 694.0 | wikitext | NULL | null |
| 6.886162e7 | 14.0 | 2022_in_British_motorsport | 0.0 | 1.0 | 0.292467983233 | 20221016125205 | 20220919202302 | 1.04757859e9 | 50.0 | wikitext | NULL | null |
| 6.8861623e7 | 15.0 | 2022_in_British_motorsport | 0.0 | 1.0 | 0.236316371919 | 20221021144754 | 20220808033126 | 1.047578618e9 | 164.0 | wikitext | NULL | null |
| 6.8861624e7 | 3.0 | 84.52.185.65 | 0.0 | 1.0 | 0.176158335205 | 20220521002319 | 20221008154250 | 1.047578625e9 | 1190.0 | wikitext | NULL | null |
| 6.8861626e7 | 3.0 | Syedabdulrehmantariq | 0.0 | 0.0 | 0.213534112906 | 20221020153739 | 20221020153738 | 1.047580445e9 | 2255.0 | wikitext | NULL | null |
| 6.8861627e7 | 0.0 | 2021_AFL_Sydney | 1.0 | 1.0 | 0.308018153373 | 20221018150426 | 20221018150425 | 1.047578894e9 | 156.0 | wikitext | NULL | null |
| 6.8861628e7 | 3.0 | MrWilson-2012 | 0.0 | 0.0 | 0.798583822693 | 20221017141655 | 20221030074922 | 1.047595265e9 | 1826.0 | wikitext | NULL | null |
| 6.8861629e7 | 3.0 | Vnlands | 0.0 | 0.0 | 0.522266584921 | 20221018143339 | 20221018143338 | 1.063101686e9 | 7511.0 | wikitext | NULL | null |
| 6.886163e7 | 0.0 | Dancing_on_My_Knees | 1.0 | 0.0 | 0.230558095314 | 20221018065718 | 20221018065717 | 1.047578949e9 | 28.0 | wikitext | NULL | null |
| 6.8861631e7 | 1.0 | Cynanchum_pulchellum | 0.0 | 1.0 | 0.121498072865 | 20221021144754 | 20221012013039 | 1.047578928e9 | 49.0 | wikitext | NULL | null |
| 6.8861632e7 | 0.0 | Melbourne_Welsh_Church | 0.0 | 0.0 | 0.207103875771 | 20221023074722 | 20221018035613 | 1.116748846e9 | 4587.0 | wikitext | NULL | null |
| 6.8861633e7 | 15.0 | Military_airbases_in_Prince_Edward_Island | 0.0 | 1.0 | 0.739978833852 | 20221027214635 | 20221028023705 | 1.047578938e9 | 46.0 | wikitext | NULL | null |
| 6.8861635e7 | 1.0 | OR-Tools | 0.0 | 0.0 | 0.157341108097 | 20221023093710 | 20220928234944 | 1.049381583e9 | 327.0 | wikitext | NULL | null |
| 6.8861636e7 | 0.0 | Ole_Kristian_Olsen | 0.0 | 1.0 | 0.978883627695 | 20221031200508 | 20221028061944 | 1.047578978e9 | 1366.0 | wikitext | NULL | null |
| 6.8861637e7 | 3.0 | GeorgiPergelov | 0.0 | 0.0 | 6.858411693e-3 | 20220809232000 | 20221010150718 | 1.059928632e9 | 25901.0 | wikitext | NULL | null |
| 6.8861638e7 | 0.0 | Jarle_Bernhoft_discography | 1.0 | 1.0 | 0.71790941436 | 20221018084255 | 20221018084254 | 1.047579007e9 | 40.0 | wikitext | NULL | null |
| 6.8861639e7 | 3.0 | PondStibbons | 0.0 | 0.0 | 0.582698852727 | 20220803125152 | 20220803125152 | 1.047579054e9 | 6271.0 | wikitext | NULL | null |
| 6.886164e7 | 3.0 | Imbadatthinkingofnames | 0.0 | 1.0 | 0.741327292098 | 20220803125152 | 20220803125152 | 1.047579042e9 | 1159.0 | wikitext | NULL | null |
| 6.8861641e7 | 1.0 | Ole_Kristian_Olsen | 0.0 | 1.0 | 0.255037177462 | 20221023093710 | 20220808033125 | 1.047579047e9 | 272.0 | wikitext | NULL | null |
| 6.8861643e7 | 3.0 | Glitt006 | 0.0 | 0.0 | 0.160600637554 | 20221017050855 | 20221017050854 | 1.069356321e9 | 3340.0 | wikitext | NULL | null |
| 6.8861644e7 | 0.0 | SF_Mono | 1.0 | 1.0 | 0.816669958605 | 20221018165333 | 20221018165332 | 1.047579121e9 | 57.0 | wikitext | NULL | null |
| 6.8861645e7 | 3.0 | 2600:8807:9A05:4800:1880:4CCA:18BB:8C1 | 0.0 | 1.0 | 0.284172731076 | 20220520215326 | 20221008154250 | 1.047579155e9 | 1470.0 | wikitext | NULL | null |
| 6.8861646e7 | 1.0 | Melbourne_Welsh_Church | 0.0 | 0.0 | 0.479874917931 | 20221023135015 | 20221026140056 | 1.047605704e9 | 197.0 | wikitext | NULL | null |
| 6.8861647e7 | 1.0 | RCAF_Station_North_Battleford | 0.0 | 1.0 | 0.459713561948 | 20221027214635 | 20221028023651 | 1.047579205e9 | 54.0 | wikitext | NULL | null |
| 6.8861648e7 | 0.0 | Impact_of_the_COVID-19_pandemic_on_gridiron_football | 0.0 | 0.0 | 0.667928214675 | 20221101031402 | 20221101021108 | 1.115534667e9 | 61782.0 | wikitext | NULL | null |
| 6.8861649e7 | 14.0 | July_2012_events_in_Turkey | 0.0 | 1.0 | 0.757621003179 | 20220918235337 | 20220924004206 | 1.047579283e9 | 466.0 | wikitext | NULL | null |
| 6.886165e7 | 0.0 | Erik_Karlsen | 0.0 | 0.0 | 0.248231303911 | 20221031200508 | 20221026112123 | 1.090848233e9 | 1839.0 | wikitext | NULL | null |
| 6.8861651e7 | 3.0 | Imranjofficial | 0.0 | 0.0 | 0.630675208811 | 20221018143339 | 20221018143338 | 1.078913719e9 | 6235.0 | wikitext | NULL | null |
| 6.8861652e7 | 15.0 | July_2012_events_in_Turkey | 0.0 | 1.0 | 0.876785695685 | 20221021144754 | 20220921193721 | 1.047579315e9 | 44.0 | wikitext | NULL | null |
| 6.8861654e7 | 3.0 | Kanij_Fatima_Ammim_Ammani | 0.0 | 1.0 | 0.902408900649 | 20221002143159 | 20221002143157 | 1.047579351e9 | 3236.0 | wikitext | NULL | null |
| 6.8861655e7 | 1.0 | Erik_Karlsen | 0.0 | 1.0 | 0.309267132221 | 20221023093710 | 20220808033125 | 1.047579353e9 | 266.0 | wikitext | NULL | null |
| 6.8861656e7 | 3.0 | FishandChipoer | 0.0 | 1.0 | 0.346937826364 | 20221002143159 | 20221002143157 | 1.047579354e9 | 3214.0 | wikitext | NULL | null |
| 6.8861657e7 | 10.0 | Birmingham_Phoenix_squad | 0.0 | 0.0 | 0.224743416717 | 20220831185957 | 20220831185957 | 1.10776084e9 | 1815.0 | wikitext | NULL | null |
| 6.8861658e7 | 3.0 | 83.27.149.184 | 0.0 | 0.0 | 0.680591639605 | 20220521002109 | 20221008154249 | 1.047796329e9 | 1962.0 | wikitext | NULL | null |
| 6.886166e7 | 3.0 | Aggreybusiingeofficial | 0.0 | 0.0 | 0.130356332991 | 20221018143339 | 20221018143337 | 1.047593081e9 | 6083.0 | wikitext | NULL | null |
| 6.8861661e7 | 11.0 | Birmingham_Phoenix_squad | 0.0 | 1.0 | 0.372726350092 | 20221023135015 | 20221025052855 | 1.047579443e9 | 23.0 | wikitext | NULL | null |
| 6.8861662e7 | 3.0 | 203.128.29.147 | 0.0 | 1.0 | 0.653254773606 | 20220520210323 | 20221008154249 | 1.047579447e9 | 978.0 | wikitext | NULL | null |
| 6.8861663e7 | 0.0 | Angie_Ng_Wee_Peng | 1.0 | 1.0 | 0.678790056665 | 20221017124356 | 20221017124355 | 1.047579452e9 | 33.0 | wikitext | NULL | null |
| 6.8861664e7 | 3.0 | RosarioGilley | 0.0 | 1.0 | 0.680344579362 | 20221018143339 | 20221018143338 | 1.047579456e9 | 5065.0 | wikitext | NULL | null |
| 6.8861665e7 | 0.0 | The_Art_of_Disappearing | 1.0 | 1.0 | 0.18989396894 | 20221018175023 | 20221018175022 | 1.047579492e9 | 23.0 | wikitext | NULL | null |
| 6.8861666e7 | 3.0 | 2001:8003:60A8:2601:A52A:1622:9BB5:6B18 | 0.0 | 1.0 | 1.943363543e-2 | 20220520205821 | 20221008154249 | 1.0475795e9 | 1047.0 | wikitext | NULL | null |
| 6.8861667e7 | 0.0 | Crystal_Poh_Shi_Qi | 1.0 | 1.0 | 0.449060083749 | 20221018065333 | 20221018065332 | 1.047579523e9 | 33.0 | wikitext | NULL | null |
| 6.8861668e7 | 3.0 | Lucygirl03 | 0.0 | 0.0 | 0.343472742219 | 20221031134605 | 20221029212144 | 1.118942661e9 | 58892.0 | wikitext | NULL | null |
| 6.8861669e7 | 0.0 | Women's_Wrestling_Grand_Prize | 1.0 | 0.0 | 0.945515118365 | 20221021165957 | 20221021165954 | 1.047579633e9 | 123.0 | wikitext | NULL | null |
| 6.886167e7 | 0.0 | Rune_Hansen | 0.0 | 0.0 | 1.764101452e-2 | 20221031200508 | 20221029034930 | 1.090848236e9 | 1853.0 | wikitext | NULL | null |
| 6.8861671e7 | 0.0 | Murders_of_Angie_Ng_and_Crystal_Poh | 1.0 | 1.0 | 0.449544406206 | 20221018095536 | 20221018095534 | 1.04757961e9 | 33.0 | wikitext | NULL | null |
| 6.8861672e7 | 3.0 | Izaz_Shaikh | 0.0 | 1.0 | 0.864586181688 | 20220803125152 | 20220803125152 | 1.047579631e9 | 4372.0 | wikitext | NULL | null |
| 6.8861674e7 | 3.0 | Tuxtion | 0.0 | 0.0 | 1.830774519e-2 | 20221028052204 | 20221028053417 | 1.049035853e9 | 346.0 | wikitext | NULL | null |
| 6.8861676e7 | 3.0 | Blaady_bla | 0.0 | 1.0 | 0.143920265519 | 20220803125152 | 20220803125151 | 1.047579661e9 | 813.0 | wikitext | NULL | null |
| 6.8861677e7 | 1.0 | Rune_Hansen | 0.0 | 1.0 | 0.318146488019 | 20221023093710 | 20220808033125 | 1.047579663e9 | 265.0 | wikitext | NULL | null |
| 6.8861678e7 | 0.0 | Rachel_David | 0.0 | 0.0 | 0.463874093755 | 20221029184648 | 20221029184932 | 1.100547605e9 | 9366.0 | wikitext | NULL | null |
| 6.8861679e7 | 3.0 | Alamgirsislam10 | 0.0 | 1.0 | 0.175995307733 | 20221002143159 | 20221002143157 | 1.047579702e9 | 3216.0 | wikitext | NULL | null |
| 6.886168e7 | 3.0 | AmethystShell | 0.0 | 1.0 | 7.5346351735e-2 | 20221002143159 | 20221002143157 | 1.047579706e9 | 3212.0 | wikitext | NULL | null |
| 6.8861681e7 | 3.0 | Delaytelo | 0.0 | 1.0 | 0.200387008872 | 20221018143339 | 20221018143337 | 1.047579736e9 | 4522.0 | wikitext | NULL | null |
| 6.8861682e7 | 2.0 | Edeckard | 0.0 | 1.0 | 0.402867021878 | 20221023093710 | 20220728025148 | 1.047579749e9 | 171.0 | wikitext | NULL | null |
| 6.8861683e7 | 3.0 | Edeckard | 0.0 | 0.0 | 0.717875434092 | 20221023093710 | 20220825090440 | 1.075054292e9 | 2089.0 | wikitext | NULL | null |
| 6.8861684e7 | 2.0 | Edeckard/sandbox | 0.0 | 1.0 | 0.888282655355 | 20221023093710 | 20221019025458 | 1.047579758e9 | 33.0 | wikitext | NULL | null |
| 6.8861685e7 | 3.0 | 2A00:23C5:2204:5D01:3933:97EA:4CEB:DB4C | 0.0 | 1.0 | 0.636211007029 | 20220520221541 | 20221008154250 | 1.047579798e9 | 976.0 | wikitext | NULL | null |
| 6.8861686e7 | 0.0 | UFC_Fight_Night_199 | 1.0 | 0.0 | 0.91191131742 | 20221019065242 | 20221019065242 | 1.04996754e9 | 48.0 | wikitext | NULL | null |
| 6.8861687e7 | 1.0 | UFC_Fight_Night_199 | 1.0 | 0.0 | 0.869512044887 | 20221004114453 | 20221004114451 | 1.049976086e9 | 53.0 | wikitext | NULL | null |
| 6.8861688e7 | 3.0 | Mr.Muhmmad_Rizwan | 0.0 | 0.0 | 2.7070221201e-2 | 20220909152227 | 20220726160342 | 1.047774495e9 | 9430.0 | wikitext | NULL | null |
| 6.8861689e7 | 14.0 | 2022_music_festivals | 0.0 | 1.0 | 0.539723337011 | 20220930022739 | 20220919024712 | 1.047579913e9 | 31.0 | wikitext | NULL | null |
| 6.886169e7 | 118.0 | Gersh_v._Anglin | 1.0 | 1.0 | 0.343766485777 | 20221023093710 | 20220926111554 | 1.047579919e9 | 76.0 | wikitext | NULL | null |
| 6.8861691e7 | 0.0 | Pa_Sheehy_discography | 1.0 | 1.0 | 0.661032362047 | 20221018101907 | 20221018101907 | 1.047579928e9 | 35.0 | wikitext | NULL | null |
| 6.8861692e7 | 1.0 | Gersh_v._Anglin | 0.0 | 0.0 | 0.362317932123 | 20221021144754 | 20221016062122 | 1.072580869e9 | 226.0 | wikitext | NULL | null |
| 6.8861693e7 | 1.0 | RCAF_Langar | 0.0 | 1.0 | 0.835245453042 | 20221027214635 | 20221028023652 | 1.04757994e9 | 54.0 | wikitext | NULL | null |
| 6.8861694e7 | 1.0 | RCAF_Resolution_Island | 0.0 | 1.0 | 0.359892949298 | 20221027214635 | 20221028023651 | 1.047579991e9 | 54.0 | wikitext | NULL | null |
| 6.8861695e7 | 0.0 | Association_of_Polish_Electrical_Engineers | 0.0 | 0.0 | 1.3017694285e-2 | 20221023074722 | 20221014201625 | 1.116096265e9 | 6602.0 | wikitext | NULL | null |
| 6.8861696e7 | 1.0 | RCAF_Station_Baden-Soellingen | 0.0 | 1.0 | 0.596917846664 | 20221027214635 | 20221028023705 | 1.047580024e9 | 54.0 | wikitext | NULL | null |
| 6.8861697e7 | 0.0 | While_We_Live | 0.0 | 0.0 | 0.352387668117 | 20221026145423 | 20221029135409 | 1.112985117e9 | 4140.0 | wikitext | NULL | null |
| 6.8861698e7 | 3.0 | 123456ronan | 0.0 | 1.0 | 0.622099238964 | 20220803125152 | 20220803125151 | 1.047580047e9 | 678.0 | wikitext | NULL | null |
| 6.8861699e7 | 0.0 | The_Polymath | 0.0 | 0.0 | 0.31029265449 | 20221026145423 | 20221025065711 | 1.111200237e9 | 15716.0 | wikitext | NULL | null |
| 6.88617e7 | 0.0 | Joshi_Puroresu_Grand_Prize | 1.0 | 0.0 | 0.857849904434 | 20221018085201 | 20221018085200 | 1.047580131e9 | 123.0 | wikitext | NULL | null |
| 6.8861701e7 | 0.0 | Ground_Controlled_Approach_Squadron_RAF | 1.0 | 1.0 | 7.4606633983e-2 | 20221031125114 | 20221018075357 | 1.047580095e9 | 100.0 | wikitext | NULL | null |
| 6.8861702e7 | 0.0 | Ground_Controlled_Approach_Flight_RAF | 1.0 | 1.0 | 0.63373790948 | 20221031125114 | 20221018075357 | 1.047580106e9 | 100.0 | wikitext | NULL | null |
| 6.8861703e7 | 0.0 | Human_hermaphroditism | 1.0 | 0.0 | 0.100351509146 | 20221030000223 | 20221030000221 | 1.047580175e9 | 70.0 | wikitext | NULL | null |
| 6.8861704e7 | 1.0 | Association_of_Polish_Electrical_Engineers | 0.0 | 1.0 | 0.840889201971 | 20221023093710 | 20220828041353 | 1.047580198e9 | 56.0 | wikitext | NULL | null |
| 6.8861706e7 | 3.0 | Thebossblogger | 0.0 | 1.0 | 0.273042505425 | 20220803125152 | 20220803125152 | 1.04758026e9 | 257.0 | wikitext | NULL | null |
| 6.8861707e7 | 0.0 | Joshua_Vanneck | 0.0 | 1.0 | 7.7101199755e-2 | 20221011054852 | 20220927181812 | 1.047580297e9 | 275.0 | wikitext | NULL | null |
| 6.8861708e7 | 3.0 | Hunzaikashif49 | 0.0 | 0.0 | 0.319086531661 | 20221017141659 | 20221030074922 | 1.04759526e9 | 3587.0 | wikitext | NULL | null |
| 6.8861709e7 | 2.0 | Bokoharamwatch/Contra_trade | 0.0 | 1.0 | 0.196832859129 | 20220728025149 | 20220728025148 | 1.047580453e9 | 106.0 | wikitext | NULL | null |
| 6.8861711e7 | 0.0 | Franz_Schmidt_(serial_killer) | 0.0 | 0.0 | 0.411672900331 | 20221023074722 | 20221013102206 | 1.111423489e9 | 7866.0 | wikitext | NULL | null |
| 6.8861712e7 | 3.0 | AussieYTgrl | 0.0 | 0.0 | 0.103206945213 | 20220911065958 | 20220911065957 | 1.047625907e9 | 1054.0 | wikitext | NULL | null |
| 6.8861713e7 | 1.0 | Franz_Schmidt_(serial_killer) | 0.0 | 0.0 | 0.789008104812 | 20221023093710 | 20221006085557 | 1.04787779e9 | 414.0 | wikitext | NULL | null |
| 6.8861714e7 | 3.0 | Siofraferriter | 0.0 | 1.0 | 0.546016904058 | 20220803125152 | 20220803125152 | 1.047580654e9 | 803.0 | wikitext | NULL | null |
| 6.8861715e7 | 0.0 | PonJola_Coney | 0.0 | 0.0 | 0.39386415113 | 20221023074722 | 20221011211739 | 1.105013615e9 | 6349.0 | wikitext | NULL | null |
| 6.8861716e7 | 3.0 | 2A00:23C8:1901:F001:5D32:9DC3:D765:14C3 | 0.0 | 1.0 | 0.463345781963 | 20220520221816 | 20221008154249 | 1.047580761e9 | 1166.0 | wikitext | NULL | null |
| 6.8861717e7 | 3.0 | 92.184.96.235 | 0.0 | 1.0 | 0.733334083846 | 20220803125152 | 20220803125151 | 1.047580764e9 | 846.0 | wikitext | NULL | null |
| 6.8861718e7 | 3.0 | 78.1.199.16 | 0.0 | 1.0 | 0.671051757512 | 20220521000316 | 20221008154249 | 1.047580767e9 | 1203.0 | wikitext | NULL | null |
| 6.8861719e7 | 3.0 | 37.161.41.122 | 0.0 | 1.0 | 7.6172751058e-2 | 20220803125155 | 20220803125154 | 1.047580802e9 | 757.0 | wikitext | NULL | null |
| 6.886172e7 | 4.0 | Miscellany_for_deletion/User:Pomtarr82/List_of_cyclists_nicknames | 0.0 | 0.0 | 0.332515262719 | 20220821081144 | 20220724102001 | 1.049115936e9 | 5689.0 | wikitext | NULL | null |
| 6.8861721e7 | 3.0 | 217.155.32.184 | 0.0 | 1.0 | 0.970328707658 | 20221018143339 | 20221018143337 | 1.047580836e9 | 5629.0 | wikitext | NULL | null |
| 6.8861722e7 | 0.0 | The_Lathums_discography | 1.0 | 1.0 | 7.0618592386e-2 | 20221028153121 | 20221018175829 | 1.047580868e9 | 37.0 | wikitext | NULL | null |
| 6.8861723e7 | 0.0 | East_Basin,_Utah | 0.0 | 1.0 | 0.141818813628 | 20221028095959 | 20221028100118 | 1.047580997e9 | 3712.0 | wikitext | NULL | null |
| 6.8861724e7 | 3.0 | Pomroy24 | 0.0 | 0.0 | 0.387735741104 | 20220803125155 | 20220803125155 | 1.047626147e9 | 5904.0 | wikitext | NULL | null |
| 6.8861725e7 | 1.0 | While_We_Live | 0.0 | 0.0 | 0.292435202511 | 20221027024549 | 20221027110843 | 1.096312003e9 | 131.0 | wikitext | NULL | null |
| 6.8861726e7 | 3.0 | 170.51.180.115 | 0.0 | 1.0 | 2.2290709287e-2 | 20220520202753 | 20221008154249 | 1.047581087e9 | 1035.0 | wikitext | NULL | null |
| 6.8861727e7 | 3.0 | 174.215.200.36 | 0.0 | 1.0 | 0.899278947575 | 20220520203402 | 20221008154249 | 1.047581132e9 | 1041.0 | wikitext | NULL | null |
| 6.8861728e7 | 2.0 | Dhemmy234 | 0.0 | 1.0 | 0.364917944126 | 20221023093710 | 20221018143337 | 1.047581189e9 | 25.0 | wikitext | NULL | null |
| 6.8861729e7 | 3.0 | 156.110.149.15 | 0.0 | 0.0 | 2.721336808e-3 | 20220706160733 | 20221008154249 | 1.084617531e9 | 1600.0 | wikitext | NULL | null |
| 6.886173e7 | 2.0 | Abbigail01 | 0.0 | 1.0 | 0.142922321573 | 20221026145423 | 20221018143337 | 1.047581234e9 | 31.0 | wikitext | NULL | null |
| 6.8861731e7 | 3.0 | 180.151.89.168 | 0.0 | 1.0 | 0.737758692268 | 20220803125155 | 20220803125154 | 1.047581245e9 | 598.0 | wikitext | NULL | null |
| 6.8861732e7 | 2.0 | Freddy435 | 0.0 | 1.0 | 0.718269901805 | 20221026145423 | 20221018143337 | 1.047581254e9 | 31.0 | wikitext | NULL | null |
| 6.8861733e7 | 3.0 | 2409:4073:4E99:6AD1:A42C:532E:8BF7:EF43 | 0.0 | 1.0 | 5.3006528582e-2 | 20220520214222 | 20221008154249 | 1.047581281e9 | 1155.0 | wikitext | NULL | null |
| 6.8861734e7 | 2.0 | Acatalinaa/sandbox | 0.0 | 0.0 | 0.357636973489 | 20221023093710 | 20220803125345 | 1.049596984e9 | 3472.0 | wikitext | NULL | null |
| 6.8861735e7 | 3.0 | Vieites | 0.0 | 0.0 | 0.738307811644 | 20220917231312 | 20221010150718 | 1.078879137e9 | 5275.0 | wikitext | NULL | null |
| 6.8861736e7 | 14.0 | Wikipedia_sockpuppets_of_Dhemmy234 | 0.0 | 1.0 | 0.434483750992 | 20221023093710 | 20220807180730 | 1.047581304e9 | 23.0 | wikitext | NULL | null |
| 6.8861737e7 | 3.0 | Raunak1401 | 0.0 | 1.0 | 0.580320971627 | 20220803125155 | 20220803125155 | 1.047581331e9 | 295.0 | wikitext | NULL | null |
| 6.8861738e7 | 3.0 | Newsomj | 0.0 | 0.0 | 0.274491359421 | 20220911065958 | 20220911065957 | 1.047581847e9 | 1312.0 | wikitext | NULL | null |
| 6.8861739e7 | 0.0 | 2021_World_Wrestling_Championships_–_Men's_freestyle_125_kg | 0.0 | 0.0 | 0.496605890429 | 20221031130513 | 20221031130539 | 1.118338045e9 | 6785.0 | wikitext | NULL | null |
| 6.8861741e7 | 6.0 | Crossroads_Guitar_Festival_2019.jpg | 0.0 | 0.0 | 0.119779970409 | 20221023093710 | 20220828202426 | 1.049133993e9 | 659.0 | wikitext | NULL | null |
| 6.8861742e7 | 3.0 | 69.193.53.210 | 0.0 | 1.0 | 0.757593606517 | 20220520231342 | 20221008154249 | 1.047581527e9 | 1122.0 | wikitext | NULL | null |
| 6.8861743e7 | 2.0 | Alliekohl/Manatee | 0.0 | 0.0 | 0.523647112501 | 20221023074722 | 20221003060233 | 1.057322854e9 | 6100.0 | wikitext | NULL | null |
| 6.8861744e7 | 3.0 | 66.177.145.115 | 0.0 | 1.0 | 0.434904880862 | 20220520225434 | 20221008154249 | 1.047581537e9 | 1352.0 | wikitext | NULL | null |
| 6.8861745e7 | 3.0 | 2402:8100:24E6:BD42:0:0:437F:9BBA | 0.0 | 1.0 | 0.494716478361 | 20220520213335 | 20221008154249 | 1.047581616e9 | 1120.0 | wikitext | NULL | null |
| 6.8861747e7 | 1.0 | Music_at_the_University_of_Massachusetts_Lowell | 0.0 | 0.0 | 0.440188196974 | 20221021144754 | 20220908104247 | 1.064185405e9 | 171.0 | wikitext | NULL | null |
| 6.8861748e7 | 3.0 | 112.201.255.86 | 0.0 | 1.0 | 0.611161960646 | 20220520195223 | 20221008154249 | 1.047581702e9 | 1133.0 | wikitext | NULL | null |
| 6.886175e7 | 1.0 | Rachel_David | 0.0 | 0.0 | 0.454477336572 | 20221023093710 | 20220808033126 | 1.072674857e9 | 1042.0 | wikitext | NULL | null |
| 6.8861751e7 | 2.0 | Highdee24 | 0.0 | 1.0 | 0.467001553705 | 20220728025149 | 20220728025148 | 1.047581773e9 | 59.0 | wikitext | NULL | null |
| 6.8861752e7 | 2.0 | Doniefitz | 0.0 | 1.0 | 0.576368289221 | 20220728025149 | 20220728025148 | 1.047581778e9 | 86.0 | wikitext | NULL | null |
| 6.8861753e7 | 6.0 | Kid_Amazo.jpg | 0.0 | 0.0 | 3.5000067019e-2 | 20221101064103 | 20221101064055 | 1.049134801e9 | 447.0 | wikitext | NULL | null |
| 6.8861754e7 | 7.0 | Kid_Amazo.jpg | 0.0 | 1.0 | 2.7056837242e-2 | 20221023135015 | 20221026140056 | 1.047581881e9 | 88.0 | wikitext | NULL | null |
| 6.8861755e7 | 3.0 | Rizwan_Chopan | 0.0 | 1.0 | 0.33821325296 | 20220913101409 | 20220803125155 | 1.047581901e9 | 7067.0 | wikitext | NULL | null |
| 6.8861756e7 | 3.0 | 2401:4900:5082:3B62:FED3:211D:C79C:BC17 | 0.0 | 1.0 | 0.670463157926 | 20220520213136 | 20221008154249 | 1.047581919e9 | 1209.0 | wikitext | NULL | null |
| 6.8861757e7 | 3.0 | ElerAstaldo | 0.0 | 1.0 | 0.651419446544 | 20220913101409 | 20220803215305 | 1.047581923e9 | 1237.0 | wikitext | NULL | null |
| 6.8861758e7 | 6.0 | Winnipeg_Goldeyes_cap_insignia.jpg | 0.0 | 0.0 | 0.12662280051 | 20221101064103 | 20221101064100 | 1.049136426e9 | 742.0 | wikitext | NULL | null |
| 6.8861759e7 | 3.0 | 2601:646:8400:5750:1D56:EA28:77DC:ECB2 | 0.0 | 1.0 | 0.511939001131 | 20221020153739 | 20221020153737 | 1.047582019e9 | 1266.0 | wikitext | NULL | null |
| 6.886176e7 | 3.0 | Taking_Out_The_Trash/Archive_2 | 0.0 | 1.0 | 0.39605814858 | 20221023093710 | 20221004181835 | 1.047582021e9 | 10144.0 | wikitext | NULL | null |
| 6.8861761e7 | 4.0 | WikiProject_Spam/LinkReports/ourrangefinder.com | 0.0 | 1.0 | 2.3157795549e-2 | 20221023093710 | 20221030150250 | 1.047582024e9 | 1638.0 | wikitext | NULL | null |
| 6.8861762e7 | 3.0 | 2601:403:4380:130:E455:4089:7F8A:C43E | 0.0 | 1.0 | 5.9168522535e-2 | 20220520215814 | 20221008154249 | 1.047582046e9 | 1409.0 | wikitext | NULL | null |
| 6.8861764e7 | 3.0 | Vitaliy.Pipich | 0.0 | 1.0 | 0.357926496386 | 20221018143339 | 20221018143338 | 1.047582112e9 | 5406.0 | wikitext | NULL | null |
| 6.8861765e7 | 3.0 | Kollegal_nauman | 0.0 | 0.0 | 0.257665881021 | 20220803125155 | 20220803125154 | 1.047586439e9 | 3254.0 | wikitext | NULL | null |
| 6.8861766e7 | 3.0 | 192.41.128.1 | 0.0 | 0.0 | 0.357625620949 | 20221023093710 | 20221008154249 | 1.06942658e9 | 3453.0 | wikitext | NULL | null |
| 6.8861767e7 | 2.0 | DanielleNabor/citing_sources | 0.0 | 1.0 | 0.199786223805 | 20220728025149 | 20220728025148 | 1.047582147e9 | 569.0 | wikitext | NULL | null |
| 6.8861769e7 | 3.0 | 124.188.94.172 | 0.0 | 1.0 | 0.469298660535 | 20220520200921 | 20221008154249 | 1.04758218e9 | 970.0 | wikitext | NULL | null |
| 6.886177e7 | 2.0 | Spark_23 | 0.0 | 1.0 | 0.740738063712 | 20220728025149 | 20220728025149 | 1.047582186e9 | 46.0 | wikitext | NULL | null |
| 6.8861771e7 | 3.0 | 50.38.71.7 | 0.0 | 1.0 | 0.962142659684 | 20220520224151 | 20221008154249 | 1.047582266e9 | 1164.0 | wikitext | NULL | null |
| 6.8861772e7 | 3.0 | Ethan12343 | 0.0 | 1.0 | 0.901623879695 | 20221020153739 | 20221020153737 | 1.047582288e9 | 1096.0 | wikitext | NULL | null |
| 6.8861773e7 | 0.0 | Ida_Bagus_Putra_Manuaba | 0.0 | 0.0 | 0.179596175015 | 20221028074243 | 20221028171830 | 1.072429367e9 | 4073.0 | wikitext | NULL | null |
| 6.8861774e7 | 3.0 | 106.210.111.44 | 0.0 | 1.0 | 0.82661407957 | 20220520194604 | 20221008154249 | 1.047582375e9 | 1109.0 | wikitext | NULL | null |
| 6.8861775e7 | 10.0 | Did_you_know_nominations/Temagami_River | 0.0 | 0.0 | 8.5992991566e-2 | 20221022145713 | 20221005084203 | 1.050411648e9 | 4856.0 | wikitext | NULL | null |
| 6.8861776e7 | 3.0 | 2804:18:1030:5D9B:1:0:BBEA:CB0F | 0.0 | 0.0 | 0.460354565734 | 20220520221353 | 20221008154249 | 1.04758308e9 | 2042.0 | wikitext | NULL | null |
| 6.8861777e7 | 3.0 | Watchword22 | 0.0 | 1.0 | 0.644140814578 | 20220913101409 | 20220803125155 | 1.047582408e9 | 7812.0 | wikitext | NULL | null |
| 6.8861779e7 | 3.0 | 2600:1004:B035:8140:25DD:8C02:96AA:739B | 0.0 | 1.0 | 0.119643256648 | 20220520214308 | 20221008154249 | 1.047582433e9 | 1128.0 | wikitext | NULL | null |
| 6.886178e7 | 3.0 | 2409:4054:21D:ED22:0:0:16C7:8A0 | 0.0 | 1.0 | 8.8093467974e-2 | 20220520213911 | 20221008154250 | 1.047582465e9 | 1114.0 | wikitext | NULL | null |
| 6.8861781e7 | 3.0 | 2A01:4C8:824:BD37:1:1:3782:2EEE | 0.0 | 1.0 | 0.230604727012 | 20220520221949 | 20221008154250 | 1.047582489e9 | 1142.0 | wikitext | NULL | null |
| 6.8861783e7 | 0.0 | Drina_National_Park | 0.0 | 0.0 | 0.97249602974 | 20221029113445 | 20221029113616 | 1.118868998e9 | 4738.0 | wikitext | NULL | null |
| 6.8861784e7 | 0.0 | Ceriogaster_auricaudatus | 1.0 | 1.0 | 0.780797044139 | 20221018063921 | 20221018063920 | 1.047582506e9 | 35.0 | wikitext | NULL | null |
| 6.8861785e7 | 3.0 | 87.49.146.57 | 0.0 | 1.0 | 0.87093594294 | 20220521003644 | 20221008154250 | 1.047582546e9 | 1285.0 | wikitext | NULL | null |
| 6.8861786e7 | 3.0 | Harrison_Debbage-Price | 0.0 | 0.0 | 0.586234953028 | 20221018143339 | 20221018143338 | 1.047629635e9 | 6617.0 | wikitext | NULL | null |
| 6.8861787e7 | 3.0 | 86.5.23.74 | 0.0 | 1.0 | 0.300839433818 | 20220521003439 | 20221008154250 | 1.047582613e9 | 1101.0 | wikitext | NULL | null |
| 6.8861788e7 | 3.0 | Merosharesansar | 0.0 | 0.0 | 0.585822644305 | 20221026145423 | 20220926204458 | 1.063101805e9 | 4063.0 | wikitext | NULL | null |
| 6.8861789e7 | 2.0 | Jar07016/Bay_cat | 0.0 | 0.0 | 0.695544602478 | 20221023074722 | 20221002044719 | 1.056075745e9 | 9461.0 | wikitext | NULL | null |
| 6.8861791e7 | 3.0 | 98.199.148.184 | 0.0 | 1.0 | 0.551692139509 | 20220803125155 | 20220803125154 | 1.047582727e9 | 765.0 | wikitext | NULL | null |
| 6.8861792e7 | 3.0 | Michaelakasa | 0.0 | 1.0 | 0.998616434338 | 20220803125155 | 20220803125154 | 1.047582773e9 | 4237.0 | wikitext | NULL | null |
| 6.8861793e7 | 1.0 | Drina_National_Park | 0.0 | 1.0 | 0.695918977527 | 20221021144754 | 20220930051002 | 1.047582807e9 | 124.0 | wikitext | NULL | null |
| 6.8861794e7 | 3.0 | Mrizki99 | 0.0 | 1.0 | 0.569223652433 | 20220913101410 | 20220803125155 | 1.047582837e9 | 7057.0 | wikitext | NULL | null |
| 6.8861795e7 | 3.0 | 111.125.221.74 | 0.0 | 0.0 | 0.929996313723 | 20220520195114 | 20221008154250 | 1.049541697e9 | 1754.0 | wikitext | NULL | null |
| 6.8861796e7 | 2.0 | Itssanjeet/Sample_page | 0.0 | 0.0 | 0.696292106561 | 20221023074722 | 20220820005754 | 1.04758609e9 | 1413.0 | wikitext | NULL | null |
| 6.8861798e7 | 0.0 | Oleh_Synyehubov | 0.0 | 0.0 | 0.566756842883 | 20221029150903 | 20221101090857 | 1.112124149e9 | 9731.0 | wikitext | NULL | null |
| 6.8861799e7 | 2.0 | Newsomj | 0.0 | 0.0 | 0.918120724423 | 20220728025149 | 20220728025148 | 1.047583408e9 | 48.0 | wikitext | NULL | null |
| 6.88618e7 | 0.0 | No._1312_Mobile_Wing_RAF_Regiment | 1.0 | 1.0 | 0.906150380108 | 20221018100742 | 20221018100741 | 1.047583065e9 | 96.0 | wikitext | NULL | null |
| 6.8861801e7 | 0.0 | No._1315_Mobile_Wing_RAF_Regiment | 1.0 | 1.0 | 0.298123591379 | 20221018100742 | 20221018100741 | 1.047583081e9 | 96.0 | wikitext | NULL | null |
| 6.8861803e7 | 0.0 | Nijel_Pack | 0.0 | 0.0 | 4.6544136393e-2 | 20221031210758 | 20221031232314 | 1.085247506e9 | 8170.0 | wikitext | NULL | null |
| 6.8861804e7 | 3.0 | 67.84.96.201 | 0.0 | 1.0 | 0.967831947824 | 20220520230120 | 20221008154250 | 1.047583122e9 | 1331.0 | wikitext | NULL | null |
| 6.8861805e7 | 2.0 | Newsomj/sandbox | 0.0 | 1.0 | 0.39403599361 | 20221023093710 | 20220803125344 | 1.047583157e9 | 20686.0 | wikitext | NULL | null |
| 6.8861806e7 | 1.0 | Ilham_Aliyev/en.wikipedia.org/wiki/Wikipedia:Contact_us | 0.0 | 1.0 | 0.20574776888 | 20221004085508 | 20221004085506 | 1.047583179e9 | 550.0 | wikitext | NULL | null |
| 6.8861807e7 | 0.0 | Sienna_Mapelli_Mozzi | 1.0 | 0.0 | 0.336951694117 | 20221031144728 | 20221031144725 | 1.063193953e9 | 302.0 | wikitext | NULL | null |
| 6.8861808e7 | 3.0 | 96.250.225.94 | 0.0 | 1.0 | 0.24331932106 | 20220803125155 | 20220803125154 | 1.047583219e9 | 1441.0 | wikitext | NULL | null |
| 6.886181e7 | 1.0 | Sienna_Mapelli_Mozzi | 1.0 | 1.0 | 4.8955040707e-2 | 20221004114453 | 20221004114451 | 1.047583238e9 | 36.0 | wikitext | NULL | null |
| 6.8861812e7 | 2.0 | Vladrichi | 0.0 | 1.0 | 0.424546887019 | 20220728025149 | 20220728025149 | 1.047583297e9 | 260.0 | wikitext | NULL | null |
| 6.8861813e7 | 3.0 | 198.162.12.104 | 0.0 | 0.0 | 0.727789268992 | 20221010144831 | 20221010144831 | 1.115249655e9 | 2985.0 | wikitext | NULL | null |
| 6.8861814e7 | 14.0 | 82_mm_mortars | 0.0 | 0.0 | 0.108307763495 | 20221004174814 | 20221004174813 | 1.047583397e9 | 49.0 | wikitext | NULL | null |
| 6.8861815e7 | 3.0 | 2600:8800:2440:8800:3993:D3FB:A23F:DAB5 | 0.0 | 1.0 | 0.10102904064 | 20220520215201 | 20221008154250 | 1.047583411e9 | 1334.0 | wikitext | NULL | null |
| 6.8861816e7 | 2.0 | Govind_khiste/sandbox | 0.0 | 0.0 | 0.258211406435 | 20220728025149 | 20220728025148 | 1.047583826e9 | 19.0 | wikitext | NULL | null |
| 6.8861817e7 | 2.0 | PaulFourtySix | 0.0 | 0.0 | 0.605800648075 | 20220728025149 | 20220728025148 | 1.047583619e9 | 182.0 | wikitext | NULL | null |
| 6.8861818e7 | 3.0 | 24.51.244.117 | 0.0 | 0.0 | 0.893682734984 | 20221025184820 | 20221008154250 | 1.05845096e9 | 2613.0 | wikitext | NULL | null |
| 6.8861819e7 | 3.0 | 41.13.90.118 | 0.0 | 1.0 | 0.873893964498 | 20220520223210 | 20221008154250 | 1.047583662e9 | 1303.0 | wikitext | NULL | null |
| 6.886182e7 | 3.0 | Praguebass | 0.0 | 0.0 | 0.326821205446 | 20221017141659 | 20221030074922 | 1.047595254e9 | 2034.0 | wikitext | NULL | null |
| 6.8861821e7 | 3.0 | 198.162.12.103 | 0.0 | 1.0 | 0.259907034682 | 20220520205202 | 20221008154250 | 1.047583705e9 | 1299.0 | wikitext | NULL | null |
| 6.8861822e7 | 3.0 | Ashfaqanjum87866 | 0.0 | 1.0 | 0.562247849456 | 20220911065958 | 20220911065957 | 1.04758375e9 | 1154.0 | wikitext | NULL | null |
| 6.8861824e7 | 3.0 | 2406:3003:2001:2CB2:4766:5012:B7ED:6495 | 0.0 | 1.0 | 5.900386518e-2 | 20220520213652 | 20221008154250 | 1.047583797e9 | 1160.0 | wikitext | NULL | null |
| 6.8861825e7 | 3.0 | 217.181.22.34 | 0.0 | 1.0 | 0.246331105287 | 20220520211631 | 20221008154250 | 1.047583875e9 | 1164.0 | wikitext | NULL | null |
| 6.8861826e7 | 3.0 | Tereza_Rachinhas | 0.0 | 0.0 | 0.453983245454 | 20211001153209 | 20221023171403 | 1.047589501e9 | 2074.0 | wikitext | NULL | null |
| 6.8861827e7 | 6.0 | Arnold_Waites.png | 0.0 | 1.0 | 0.247635596695 | 20221101064103 | 20221101064050 | 1.047583953e9 | 562.0 | wikitext | NULL | null |
| 6.8861828e7 | 0.0 | No._2893_Squadron_RAF_Regiment | 1.0 | 1.0 | 0.689552314085 | 20221018100804 | 20221018100803 | 1.047584042e9 | 104.0 | wikitext | NULL | null |
| 6.8861829e7 | 3.0 | Manoharjha007 | 0.0 | 0.0 | 6.3489272777e-2 | 20220917231312 | 20221010150718 | 1.078879304e9 | 13601.0 | wikitext | NULL | null |
| 6.886183e7 | 0.0 | Parti_Libre_Canada | 1.0 | 0.0 | 0.717577517062 | 20221028225437 | 20221028225435 | 1.052406246e9 | 107.0 | wikitext | NULL | null |
| 6.8861831e7 | 3.0 | 77.143.4.161 | 0.0 | 0.0 | 7.696295473e-3 | 20220521000209 | 20221008154250 | 1.047588045e9 | 2231.0 | wikitext | NULL | null |
| 6.8861833e7 | 4.0 | Map_data/Buckingham_(UK_Parliament_constituency) | 0.0 | 1.0 | 0.805125839464 | 20220930232637 | 20220930232635 | 1.047584202e9 | 27662.0 | wikitext | NULL | null |
| 6.8861834e7 | 3.0 | 67.230.57.242 | 0.0 | 1.0 | 4.48050033e-3 | 20220520225931 | 20221008154250 | 1.047584224e9 | 1165.0 | wikitext | NULL | null |
| 6.8861835e7 | 3.0 | The_Ranger71 | 0.0 | 1.0 | 5.966677788e-3 | 20220825203343 | 20220825203342 | 1.047584247e9 | 1610.0 | wikitext | NULL | null |
| 6.8861836e7 | 3.0 | 27.5.41.250 | 0.0 | 1.0 | 3.1604290122e-2 | 20220803125155 | 20220803125154 | 1.047584319e9 | 2160.0 | wikitext | NULL | null |
| 6.8861837e7 | 3.0 | Pope_Atlas | 0.0 | 0.0 | 0.758090112304 | 20220803125155 | 20220803125155 | 1.047584496e9 | 0.0 | wikitext | NULL | null |
| 6.8861839e7 | 3.0 | 70.188.227.235 | 0.0 | 1.0 | 0.521936706791 | 20220520231958 | 20221008154250 | 1.047584359e9 | 1160.0 | wikitext | NULL | null |
| 6.886184e7 | 3.0 | 2405:9800:B920:BEE1:70F3:36EE:A05C:4B02 | 0.0 | 1.0 | 1.6355332409e-2 | 20220520213649 | 20221008154250 | 1.047584406e9 | 1161.0 | wikitext | NULL | null |
| 6.8861841e7 | 4.0 | Map_data/Broadland_(UK_Parliament_constituency) | 0.0 | 0.0 | 0.654343544499 | 20220930232637 | 20220930232635 | 1.047584606e9 | 30514.0 | wikitext | NULL | null |
| 6.8861842e7 | 3.0 | Uliberty | 0.0 | 0.0 | 0.855941773689 | 20221017141659 | 20221030074922 | 1.050055715e9 | 1538.0 | wikitext | NULL | null |
| 6.8861843e7 | 3.0 | 46.217.8.152 | 0.0 | 1.0 | 0.668734638243 | 20220803125155 | 20220803125154 | 1.047584514e9 | 951.0 | wikitext | NULL | null |
| 6.8861844e7 | 3.0 | 58.171.165.26 | 0.0 | 0.0 | 0.751571243845 | 20220913101410 | 20221008154250 | 1.071398583e9 | 3689.0 | wikitext | NULL | null |
| 6.8861845e7 | 0.0 | 1941_Spring_Hill_Badgers_football_team | 0.0 | 0.0 | 0.278381223903 | 20221029222957 | 20221030055702 | 1.075449158e9 | 5517.0 | wikitext | NULL | null |
| 6.8861846e7 | 3.0 | F14fixr | 0.0 | 0.0 | 0.739826030302 | 20220913101410 | 20220911065957 | 1.047701129e9 | 3803.0 | wikitext | NULL | null |
| 6.8861847e7 | 3.0 | 2600:1702:50:26A0:B9B2:D2C5:BB01:4DC0 | 0.0 | 1.0 | 0.416172998708 | 20220520214955 | 20221008154250 | 1.047584582e9 | 1136.0 | wikitext | NULL | null |
| 6.8861848e7 | 0.0 | Adolfo_Infante | 0.0 | 0.0 | 0.40812327842 | 20221026195056 | 20221026202059 | 1.112497988e9 | 8149.0 | wikitext | NULL | null |
| 6.8861849e7 | 0.0 | 2021_UCI_Road_World_Championships_–_Men's_under-23_time_trial | 0.0 | 0.0 | 0.122725132469 | 20221031232845 | 20221101014106 | 1.111168054e9 | 6687.0 | wikitext | NULL | null |
| 6.886185e7 | 1.0 | 1941_Spring_Hill_Badgers_football_team | 0.0 | 1.0 | 3.394632847e-2 | 20221027063132 | 20221027080018 | 1.047584622e9 | 58.0 | wikitext | NULL | null |
| 6.8861851e7 | 3.0 | 72.49.184.215 | 0.0 | 0.0 | 0.592386771717 | 20221023093710 | 20221008154250 | 1.052144377e9 | 12462.0 | wikitext | NULL | null |
| 6.8861852e7 | 6.0 | Kroloteans.jpg | 0.0 | 0.0 | 0.878866743388 | 20221028191549 | 20221028191542 | 1.049134812e9 | 426.0 | wikitext | NULL | null |
| 6.8861853e7 | 3.0 | 163.53.24.4 | 0.0 | 1.0 | 0.256537768397 | 20220803125155 | 20220803125154 | 1.047584745e9 | 759.0 | wikitext | NULL | null |
| 6.8861854e7 | 1.0 | Anthonio_Sanjairag | 0.0 | 0.0 | 0.616228320155 | 20221023093710 | 20221018222633 | 1.072334032e9 | 320.0 | wikitext | NULL | null |
| 6.8861855e7 | 7.0 | Kroloteans.jpg | 0.0 | 1.0 | 0.750467786145 | 20221023135015 | 20221026140056 | 1.047584777e9 | 88.0 | wikitext | NULL | null |
| 6.8861856e7 | 3.0 | 2A02:C7F:AEB8:3A00:4962:50F1:F845:CEE8 | 0.0 | 1.0 | 0.611340277264 | 20220520222530 | 20221008154250 | 1.047584809e9 | 1131.0 | wikitext | NULL | null |
| 6.8861857e7 | 3.0 | Karlpalencia1 | 0.0 | 1.0 | 0.950279345147 | 20220803125155 | 20220803125154 | 1.047584829e9 | 762.0 | wikitext | NULL | null |
| 6.8861858e7 | 3.0 | MJL | 0.0 | 0.0 | 0.975106054945 | 20221101080002 | 20221101080128 | 1.119180297e9 | 93473.0 | wikitext | NULL | null |
| 6.8861859e7 | 0.0 | Volevo_fare_la_Rockstar | 1.0 | 1.0 | 0.56488220082 | 20221031021207 | 20221019095107 | 1.047584928e9 | 28.0 | wikitext | NULL | null |
| 6.886186e7 | 3.0 | 86.187.235.140 | 0.0 | 0.0 | 0.325001882446 | 20220521003232 | 20221008154250 | 1.047585867e9 | 2006.0 | wikitext | NULL | null |
| 6.8861861e7 | 0.0 | Richard_Sseruwagi | 0.0 | 0.0 | 0.424117628639 | 20221026145423 | 20221018170201 | 1.116846399e9 | 4659.0 | wikitext | NULL | null |
| 6.8861862e7 | 3.0 | Talimaria | 0.0 | 0.0 | 0.880984816595 | 20220522063708 | 20221003060233 | 1.052000354e9 | 2499.0 | wikitext | NULL | null |
| 6.8861863e7 | 0.0 | Volevo_fare_la_rockstar | 1.0 | 1.0 | 0.657988224884 | 20221031021207 | 20221019095107 | 1.047584979e9 | 28.0 | wikitext | NULL | null |
| 6.8861864e7 | 14.0 | Self-contradictory_articles_from_July_2017 | 0.0 | 1.0 | 0.102905024446 | 20221023093710 | 20221005165456 | 1.047584995e9 | 29.0 | wikitext | NULL | null |
| 6.8861865e7 | 2.0 | Somurox | 0.0 | 0.0 | 0.830758339322 | 20220728111157 | 20220728111155 | 1.055091654e9 | 347.0 | wikitext | NULL | null |
| 6.8861866e7 | 14.0 | Self-contradictory_articles_from_October_2013 | 0.0 | 1.0 | 0.446698774899 | 20221023093710 | 20221005165456 | 1.047585013e9 | 29.0 | wikitext | NULL | null |
| 6.8861867e7 | 4.0 | Map_data/Brigg_and_Goole_(UK_Parliament_constituency) | 0.0 | 0.0 | 0.150736769017 | 20220930232637 | 20220930232635 | 1.047587368e9 | 22813.0 | wikitext | NULL | null |
| 6.8861868e7 | 0.0 | Volevo_fare_la_rockstar_(album) | 1.0 | 1.0 | 0.50370102472 | 20221031021207 | 20221019095107 | 1.047585017e9 | 28.0 | wikitext | NULL | null |
| 6.8861869e7 | 3.0 | BurakD53 | 0.0 | 0.0 | 0.218400747162 | 20220827012947 | 20220826170353 | 1.106823792e9 | 9869.0 | wikitext | NULL | null |
| 6.886187e7 | 3.0 | 106.207.133.117 | 0.0 | 1.0 | 0.109003474409 | 20220520194556 | 20221008154250 | 1.047585117e9 | 1104.0 | wikitext | NULL | null |
| 6.8861871e7 | 3.0 | 2600:1014:B10B:3837:91B8:6AE3:6529:DC6D | 0.0 | 1.0 | 0.498635366936 | 20220520214530 | 20221008154250 | 1.047585153e9 | 1580.0 | wikitext | NULL | null |
| 6.8861872e7 | 3.0 | Holabtbot | 0.0 | 1.0 | 0.569906338818 | 20220803125155 | 20220803125154 | 1.047585164e9 | 1174.0 | wikitext | NULL | null |
| 6.8861874e7 | 1.0 | Richard_Sseruwagi | 0.0 | 0.0 | 0.517435006028 | 20221027024549 | 20221027110843 | 1.096311878e9 | 248.0 | wikitext | NULL | null |
| 6.8861875e7 | 3.0 | 83.253.162.209 | 0.0 | 0.0 | 0.742474959398 | 20220521002106 | 20221008154250 | 1.049238116e9 | 2084.0 | wikitext | NULL | null |
| 6.8861876e7 | 3.0 | 66.44.6.113 | 0.0 | 1.0 | 0.162969264664 | 20220520225610 | 20221008154250 | 1.047585262e9 | 1161.0 | wikitext | NULL | null |
| 6.8861877e7 | 3.0 | Jitu_Bhardwaj | 0.0 | 1.0 | 0.581860852344 | 20220803125155 | 20220803125154 | 1.047585292e9 | 727.0 | wikitext | NULL | null |
| 6.8861878e7 | 10.0 | London_Spirit_squad | 0.0 | 0.0 | 0.667231224239 | 20220824164650 | 20220824164650 | 1.106444093e9 | 1780.0 | wikitext | NULL | null |
| 6.8861879e7 | 0.0 | Meredith_Calhoun | 0.0 | 0.0 | 7.6343032826e-2 | 20221023074722 | 20221010043324 | 1.100207818e9 | 1684.0 | wikitext | NULL | null |
| 6.886188e7 | 0.0 | Caproni_Transaero | 1.0 | 0.0 | 0.846645442553 | 20221027000854 | 20221027000854 | 1.047586123e9 | 87.0 | wikitext | NULL | null |
| 6.8861881e7 | 0.0 | Susil_Ranjan_Chattopadhyay | 0.0 | 0.0 | 0.419471357754 | 20221028074243 | 20221028174028 | 1.111419209e9 | 2211.0 | wikitext | NULL | null |
| 6.8861882e7 | 11.0 | London_Spirit_squad | 0.0 | 1.0 | 0.983301549027 | 20221023135015 | 20221025065153 | 1.047585389e9 | 23.0 | wikitext | NULL | null |
| 6.8861884e7 | 3.0 | 155.4.98.140 | 0.0 | 1.0 | 0.405219557885 | 20220520202254 | 20221008154250 | 1.047585425e9 | 1172.0 | wikitext | NULL | null |
| 6.8861887e7 | 1.0 | Susil_Ranjan_Chattopadhyay | 0.0 | 0.0 | 3.7404856018e-2 | 20221021144754 | 20220921193719 | 1.092374048e9 | 131.0 | wikitext | NULL | null |
| 6.8861888e7 | 3.0 | 2401:4900:599C:EC20:4FE8:D972:CEDF:2767 | 0.0 | 1.0 | 0.311511534435 | 20220520213156 | 20221008154250 | 1.047585576e9 | 1142.0 | wikitext | NULL | null |
| 6.8861889e7 | 4.0 | Sockpuppet_investigations/CreatorVRXAZ/Archive | 0.0 | 0.0 | 0.470124028531 | 20221024143701 | 20221030074923 | 1.053292145e9 | 8453.0 | wikitext | NULL | null |
| 6.8861891e7 | 0.0 | Meglio_del_cinema | 1.0 | 1.0 | 0.112435463605 | 20221021221816 | 20221018094248 | 1.047585685e9 | 19.0 | wikitext | NULL | null |
| 6.8861892e7 | 6.0 | Royole_logo.png | 0.0 | 1.0 | 0.844588284591 | 20221101064103 | 20221101064058 | 1.047585686e9 | 733.0 | wikitext | NULL | null |
| 6.8861893e7 | 3.0 | M_Noman_Akhtar_jutt | 0.0 | 1.0 | 0.335324713421 | 20221020153739 | 20221020153737 | 1.047585696e9 | 2358.0 | wikitext | NULL | null |
| 6.8861894e7 | 4.0 | Sockpuppet_investigations/Alaskayoung1/Archive | 0.0 | 1.0 | 0.613849213489 | 20221026145423 | 20221027013921 | 1.047585731e9 | 11736.0 | wikitext | NULL | null |
| 6.8861895e7 | 2.0 | Rubymhel_Lopez/sandbox | 0.0 | 0.0 | 0.978143661494 | 20220728025149 | 20220728025149 | 1.047586387e9 | 1862.0 | wikitext | NULL | null |
| 6.8861898e7 | 3.0 | 42.116.116.78 | 0.0 | 1.0 | 0.308417173063 | 20220913101410 | 20220803125157 | 1.04758591e9 | 1549.0 | wikitext | NULL | null |
| 6.8861899e7 | 3.0 | Annaspencer13 | 0.0 | 0.0 | 4.7991239914e-2 | 20221026145423 | 20221010150718 | 1.067213162e9 | 44860.0 | wikitext | NULL | null |
| 6.8861901e7 | 0.0 | Age_of_consent_in_Ireland | 1.0 | 1.0 | 0.496797919949 | 20221027165749 | 20221027165747 | 1.047585945e9 | 227.0 | wikitext | NULL | null |
| 6.8861902e7 | 3.0 | Ac2468 | 0.0 | 0.0 | 0.13607075548 | 20220906235936 | 20221010150719 | 1.101003158e9 | 25373.0 | wikitext | NULL | null |
| 6.8861903e7 | 1.0 | Mary_Camacho_Torres | 0.0 | 0.0 | 1.8038551388e-2 | 20221023093710 | 20220808033126 | 1.048070187e9 | 331.0 | wikitext | NULL | null |
| 6.8861904e7 | 3.0 | 2001:8F8:1825:2DA9:1057:4D27:D8C:57E0 | 0.0 | 1.0 | 0.846139371304 | 20220520205936 | 20221008154250 | 1.047586134e9 | 1315.0 | wikitext | NULL | null |
| 6.8861907e7 | 4.0 | Sockpuppet_investigations/Free1Soul/Archive | 0.0 | 1.0 | 0.432540925413 | 20221024143701 | 20221027013921 | 1.047586243e9 | 2504.0 | wikitext | NULL | null |
| 6.8861908e7 | 3.0 | 2A02:C7F:9808:A700:31CB:DAB8:BDCB:D98E | 0.0 | 1.0 | 0.287874441687 | 20220520222509 | 20221008154250 | 1.047586271e9 | 991.0 | wikitext | NULL | null |
| 6.8861911e7 | 0.0 | Caproni_Transaereo | 1.0 | 1.0 | 0.723926190856 | 20221018063514 | 20221018063512 | 1.047586406e9 | 27.0 | wikitext | NULL | null |
| 6.8861912e7 | 0.0 | Susil_Ranjan_Chatterjee | 1.0 | 1.0 | 8.671320226e-3 | 20221018172851 | 20221018172849 | 1.047586485e9 | 40.0 | wikitext | NULL | null |
| 6.8861913e7 | 0.0 | Lake_226 | 0.0 | 0.0 | 0.852973109236 | 20221023074722 | 20221018000040 | 1.085377763e9 | 12193.0 | wikitext | NULL | null |
| 6.8861914e7 | 3.0 | 2409:4073:40F:AD7D:0:0:17BE:38B1 | 0.0 | 1.0 | 0.58369705007 | 20220520214211 | 20221008154250 | 1.047586518e9 | 1158.0 | wikitext | NULL | null |
| 6.8861917e7 | 2.0 | Printy13/Denotation/Emma_Adriana_Peer_Review | 0.0 | 0.0 | 0.331069636456 | 20221023093710 | 20221003060233 | 1.04785712e9 | 6583.0 | wikitext | NULL | null |
| 6.8861918e7 | 3.0 | ADDSamuels | 0.0 | 0.0 | 0.943521830492 | 20221026145423 | 20221020153737 | 1.083226345e9 | 6711.0 | wikitext | NULL | null |
| 6.8861919e7 | 0.0 | Consulate-General_of_the_United_Kingdom,_Osaka | 1.0 | 0.0 | 0.830104407131 | 20221027074318 | 20221027074315 | 1.072946782e9 | 107.0 | wikitext | NULL | null |
| 6.886192e7 | 1.0 | Consulate-General_of_the_United_Kingdom,_Osaka | 1.0 | 0.0 | 0.485447801272 | 20221023093710 | 20221027074431 | 1.072946794e9 | 126.0 | wikitext | NULL | null |
| 6.8861921e7 | 3.0 | 2409:4073:212:5C5F:7179:AD1:ACC8:7655 | 0.0 | 1.0 | 0.143783638461 | 20220911065958 | 20220911065957 | 1.047586733e9 | 598.0 | wikitext | NULL | null |
| 6.8861922e7 | 3.0 | 71.233.44.133 | 0.0 | 1.0 | 0.41699307933 | 20220520233107 | 20221008154250 | 1.047586747e9 | 1156.0 | wikitext | NULL | null |
| 6.8861923e7 | 0.0 | CC-295_Kingfisher | 1.0 | 1.0 | 0.156389653778 | 20221018062921 | 20221018062920 | 1.047586758e9 | 29.0 | wikitext | NULL | null |
| 6.8861924e7 | 0.0 | CC-295 | 1.0 | 1.0 | 0.744837481136 | 20221018062921 | 20221018062920 | 1.047586792e9 | 29.0 | wikitext | NULL | null |
| 6.8861925e7 | 3.0 | Tereza_Rachinhas/TWA | 0.0 | 0.0 | 0.908649320722 | 20211001155634 | 20220929165626 | 1.047593913e9 | 1565.0 | wikitext | NULL | null |
| 6.8861926e7 | 2.0 | Nc1180lCm/Sample_page | 0.0 | 0.0 | 0.479813485944 | 20221011054852 | 20220728025148 | 1.047587095e9 | 61.0 | wikitext | NULL | null |
| 6.8861927e7 | 3.0 | 42.111.145.249 | 0.0 | 1.0 | 0.994624008234 | 20220520223345 | 20221008154250 | 1.047586871e9 | 1353.0 | wikitext | NULL | null |
| 6.8861928e7 | 3.0 | 1lavya28289 | 0.0 | 0.0 | 0.388343274816 | 20220917231312 | 20221010150718 | 1.078892878e9 | 14678.0 | wikitext | NULL | null |
| 6.8861929e7 | 3.0 | RTR1961 | 0.0 | 0.0 | 0.99895378596 | 20221031194404 | 20221020153741 | 1.049171577e9 | 3839.0 | wikitext | NULL | null |
| 6.886193e7 | 2.0 | Tereza_Rachinhas/TWA/Earth | 0.0 | 0.0 | 0.609928601472 | 20211001154940 | 20220929165626 | 1.047592683e9 | 1693.0 | wikitext | NULL | null |
| 6.8861931e7 | 0.0 | Embassy_of_the_State_of_Palestine,_Tokyo | 1.0 | 1.0 | 0.455965169176 | 20221027112856 | 20221027112854 | 1.047586976e9 | 88.0 | wikitext | NULL | null |
| 6.8861932e7 | 1.0 | Embassy_of_the_State_of_Palestine,_Tokyo | 1.0 | 1.0 | 0.438550505623 | 20221023093710 | 20221027113043 | 1.047586978e9 | 93.0 | wikitext | NULL | null |
| 6.8861933e7 | 0.0 | Pseudophilautus_munnarensis | 1.0 | 1.0 | 0.48019824697 | 20221018103403 | 20221018103402 | 1.047586979e9 | 76.0 | wikitext | NULL | null |
| 6.8861934e7 | 3.0 | Javad5351 | 0.0 | 0.0 | 0.566860048009 | 20220917231312 | 20221010150718 | 1.080541924e9 | 6690.0 | wikitext | NULL | null |
| 6.8861935e7 | 2.0 | MBge1644_2_PRO | 0.0 | 1.0 | 0.341220129777 | 20220728025149 | 20220728025148 | 1.047587011e9 | 81.0 | wikitext | NULL | null |
| 6.8861937e7 | 0.0 | Embassy_of_the_State_of_Palestine,_Manama | 1.0 | 1.0 | 0.832471659276 | 20221027112856 | 20221027112854 | 1.047587062e9 | 89.0 | wikitext | NULL | null |
| 6.8861938e7 | 1.0 | Embassy_of_the_State_of_Palestine,_Manama | 1.0 | 1.0 | 0.369152802806 | 20221023093710 | 20221027113042 | 1.047587064e9 | 94.0 | wikitext | NULL | null |
| 6.8861939e7 | 2.0 | Keith-S2dows/sandbox | 0.0 | 0.0 | 0.730965526507 | 20221023093710 | 20220803125345 | 1.048409694e9 | 147.0 | wikitext | NULL | null |
| 6.886194e7 | 0.0 | Philautus_munnarensis | 1.0 | 1.0 | 0.274782877886 | 20221018102607 | 20221018102606 | 1.047587079e9 | 76.0 | wikitext | NULL | null |
| 6.8861941e7 | 0.0 | Embassy_of_the_State_of_Palestine,_Hanoi | 1.0 | 1.0 | 0.61571053089 | 20221027112856 | 20221027112854 | 1.047587142e9 | 88.0 | wikitext | NULL | null |
| 6.8861942e7 | 1.0 | Embassy_of_the_State_of_Palestine,_Hanoi | 1.0 | 1.0 | 0.536381926203 | 20221023093710 | 20221027113042 | 1.047587146e9 | 93.0 | wikitext | NULL | null |
| 6.8861943e7 | 4.0 | Sockpuppet_investigations/Sherkohassan/Archive | 0.0 | 1.0 | 0.389762041558 | 20221024143701 | 20221027013921 | 1.047587177e9 | 4793.0 | wikitext | NULL | null |
| 6.8861944e7 | 3.0 | 49.204.128.208 | 0.0 | 0.0 | 0.752363175792 | 20220803125159 | 20220803125157 | 1.047587703e9 | 585.0 | wikitext | NULL | null |
| 6.8861945e7 | 0.0 | 2021–22_EuroLeague_Regular_Season | 0.0 | 0.0 | 0.449837602164 | 20221023074722 | 20221029003730 | 1.095308811e9 | 302424.0 | wikitext | NULL | null |
| 6.8861946e7 | 3.0 | AirportCodeTemplate | 0.0 | 0.0 | 0.328857306603 | 20221026145423 | 20221018143337 | 1.047848297e9 | 6093.0 | wikitext | NULL | null |
| 6.8861947e7 | 4.0 | Sockpuppet_investigations/Trane007/Archive | 0.0 | 0.0 | 0.881258721024 | 20221031194404 | 20221030074923 | 1.060512974e9 | 15262.0 | wikitext | NULL | null |
| 6.8861948e7 | 3.0 | 117.216.19.28 | 0.0 | 1.0 | 0.55033049026 | 20220520195746 | 20221008154250 | 1.047587333e9 | 1160.0 | wikitext | NULL | null |
| 6.8861949e7 | 2.0 | Tereza_Rachinhas | 0.0 | 0.0 | 0.36955742278 | 20220701113736 | 20220929054446 | 1.047599072e9 | 281.0 | wikitext | NULL | null |
| 6.886195e7 | 4.0 | Sockpuppet_investigations/Andlol17/Archive | 0.0 | 1.0 | 0.208652424958 | 20221031194404 | 20221030074923 | 1.047587372e9 | 5383.0 | wikitext | NULL | null |
| 6.8861951e7 | 3.0 | Tereza_Rachinhas/TWA/Earth | 0.0 | 0.0 | 4.2618267527e-2 | 20220712205541 | 20220929165626 | 1.047594761e9 | 7253.0 | wikitext | NULL | null |
| 6.8861952e7 | 3.0 | Parth006 | 0.0 | 1.0 | 0.388742685675 | 20220913101410 | 20220804014400 | 1.047587391e9 | 1159.0 | wikitext | NULL | null |
| 6.8861953e7 | 3.0 | Aj_indiana | 0.0 | 0.0 | 0.39802279306 | 20221026145423 | 20221010150718 | 1.080751899e9 | 24741.0 | wikitext | NULL | null |
| 6.8861954e7 | 0.0 | Invasión_de_Bahia_de_Cochinos | 1.0 | 0.0 | 0.720539326591 | 20221029083337 | 20221028133838 | 1.052406473e9 | 110.0 | wikitext | NULL | null |
| 6.8861955e7 | 0.0 | Hobble_Creek,_Utah | 0.0 | 1.0 | 0.528350052275 | 20221028121640 | 20221028121843 | 1.04758748e9 | 3417.0 | wikitext | NULL | null |
| 6.8861956e7 | 3.0 | Bayonetofficial | 0.0 | 0.0 | 0.910038885365 | 20221017141659 | 20221030074923 | 1.047619668e9 | 8831.0 | wikitext | NULL | null |
| 6.8861957e7 | 0.0 | List_of_islands_of_Sint_Maarten | 1.0 | 1.0 | 0.40058629686 | 20221101055734 | 20221018091850 | 1.047587508e9 | 54.0 | wikitext | NULL | null |
| 6.8861958e7 | 2.0 | Siyabonga7492 | 0.0 | 0.0 | 0.576091394572 | 20220728111157 | 20220728111155 | 1.054999184e9 | 349.0 | wikitext | NULL | null |
| 6.8861959e7 | 0.0 | The_Crozier_Pharaohs | 0.0 | 0.0 | 6.7074127843e-2 | 20221101064104 | 20221101064213 | 1.082825215e9 | 1891.0 | wikitext | NULL | null |
| 6.886196e7 | 1.0 | +–=÷x_Tour | 0.0 | 0.0 | 0.964977619774 | 20221021144754 | 20220902204758 | 1.098332963e9 | 1391.0 | wikitext | NULL | null |
| 6.8861961e7 | 0.0 | RetroCrush | 1.0 | 0.0 | 0.376468999751 | 20221031141824 | 20221031141820 | 1.090991252e9 | 105.0 | wikitext | NULL | null |
| 6.8861962e7 | 1.0 | List_of_islands_of_Sint_Maarten | 0.0 | 1.0 | 0.165307957322 | 20221021144754 | 20221004085507 | 1.047587599e9 | 54.0 | wikitext | NULL | null |
| 6.8861963e7 | 1.0 | The_Crozier_Pharaohs | 0.0 | 1.0 | 0.163782544605 | 20221021144754 | 20220828055325 | 1.047587604e9 | 51.0 | wikitext | NULL | null |
| 6.8861965e7 | 3.0 | Nc1180lCm/Sample_page | 0.0 | 0.0 | 0.776604017024 | 20220803125159 | 20220803125158 | 1.047587881e9 | 16.0 | wikitext | NULL | null |
| 6.8861966e7 | 3.0 | Sinssine97 | 0.0 | 1.0 | 0.608458681933 | 20220913101410 | 20220804014401 | 1.047587711e9 | 1288.0 | wikitext | NULL | null |
| 6.8861968e7 | 2.0 | Tereza_Rachinhas/TWA/Earth/2 | 0.0 | 0.0 | 0.676353152135 | 20220611214917 | 20220929165626 | 1.047597217e9 | 22779.0 | wikitext | NULL | null |
| 6.8861969e7 | 3.0 | Autodidacticthinker | 0.0 | 1.0 | 0.739077358624 | 20220913101410 | 20220803215305 | 1.04758783e9 | 1256.0 | wikitext | NULL | null |
Next, let us check that we got all the data, and there are no corrupted records:
readFromCSV.createOrReplaceTempView("pages")
SELECT * FROM pages WHERE _corrupt_record IS NOT NULL
| page_id | page_namespace | page_title | page_is_redirect | page_is_new | page_random | page_touched | page_links_updated | page_latest | page_len | page_content_model | page_lang | _corrupt_record |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7.170164e7 | 0.0 | 104-2,3,(6 | null | null | null | null | null | null | null | null | null | 71701640,0,'104-2,3,(6 |
| null | null | 1 | 1.0 | null | 2.0221101041357e13 | 20221028090110 | 1109047991 | 113.0 | null | NULL | null | 7),11',1,1,0.143243519864,'20221101041357','20221028090110',1109047991,113,'wikitext',NULL |
Okay, so, we lost a single row. It is a page that has since been redirected to this little bit of text in the Victoria (Australia) article:

So since the title of the article itself contained the string ),(, our splitting at that character combo broke the line into two rows, both of which are invalid records. Should be easy enough to deal with - we just need to filter out the two rows that have non-null _corrupt_record.
Let us now filter down to the data we actually want, and save this to the Delta Lake. First off, only pages in namespace zero are main-wikipedia articles, so we can drop everything outside of it. There are also a bunch of columns containing information we don't care about, so we can skip including those as well.
SELECT page_id, page_title, page_is_redirect, page_is_new AS has_been_edited, page_len, page_content_model, page_lang FROM pages WHERE (page_id IS NOT NULL) AND (page_namespace = 0) AND (page_title IS NOT NULL) AND (_corrupt_record IS NULL)
| page_id | page_title | page_is_redirect | has_been_edited | page_len | page_content_model | page_lang |
|---|---|---|---|---|---|---|
| 6.886083e7 | William_Alexander_(architect) | 0.0 | 0.0 | 4098.0 | wikitext | NULL |
| 6.8860833e7 | 1911_South_Sydney_season | 0.0 | 0.0 | 9240.0 | wikitext | NULL |
| 6.8860837e7 | Longtail_weasel | 1.0 | 1.0 | 32.0 | wikitext | NULL |
| 6.8860841e7 | RTL_Up | 1.0 | 1.0 | 66.0 | wikitext | NULL |
| 6.8860847e7 | The_Sex_Side_of_Life | 1.0 | 1.0 | 26.0 | wikitext | NULL |
| 6.886085e7 | Facemasks_during_the_Covid-19_pandemic | 1.0 | 1.0 | 53.0 | wikitext | NULL |
| 6.8860855e7 | List_of_awards_and_nominations_received_by_George_Lucas | 0.0 | 0.0 | 12877.0 | wikitext | NULL |
| 6.8860859e7 | Second_Chance_Motorsports | 0.0 | 0.0 | 144.0 | wikitext | NULL |
| 6.8860861e7 | Lenny_Massey | 0.0 | 0.0 | 5170.0 | wikitext | NULL |
| 6.8860862e7 | 2021–22_EHF_European_League | 0.0 | 0.0 | 26821.0 | wikitext | NULL |
| 6.8860864e7 | 2021_Asian_Table_Tennis_Championships_–_Women's_team | 0.0 | 0.0 | 10200.0 | wikitext | NULL |
| 6.8860867e7 | Rina_Fukushi | 0.0 | 0.0 | 2431.0 | wikitext | NULL |
| 6.8860871e7 | 1979_in_Finland | 0.0 | 0.0 | 2619.0 | wikitext | NULL |
| 6.8860884e7 | Charlie_Patino | 0.0 | 0.0 | 11964.0 | wikitext | NULL |
| 6.8860893e7 | Thomas_Beven | 0.0 | 0.0 | 3931.0 | wikitext | NULL |
| 6.8860897e7 | Tomas_Serra_Olives | 0.0 | 0.0 | 2357.0 | wikitext | NULL |
| 6.8860898e7 | Charlie_Patiño | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8860903e7 | Tetilla_(sponge) | 0.0 | 0.0 | 5630.0 | wikitext | NULL |
| 6.8860904e7 | Something_Real_(Phoebe_Snow_album) | 0.0 | 0.0 | 8917.0 | wikitext | NULL |
| 6.8860905e7 | Luca_Pretolesi | 0.0 | 0.0 | 6932.0 | wikitext | NULL |
| 6.8860914e7 | Jme_tire | 1.0 | 1.0 | 23.0 | wikitext | NULL |
| 6.8860916e7 | Doolot_Sydykov | 0.0 | 0.0 | 4332.0 | wikitext | NULL |
| 6.8860928e7 | Saterfrisian | 1.0 | 1.0 | 40.0 | wikitext | NULL |
| 6.8860935e7 | Al_McCoy_(baseball) | 0.0 | 0.0 | 2658.0 | wikitext | NULL |
| 6.8860936e7 | Ich_bin_weg_(Boro_boro) | 1.0 | 1.0 | 36.0 | wikitext | NULL |
| 6.8860938e7 | Ich_bin_weg_(Boro_Boro) | 1.0 | 0.0 | 47.0 | wikitext | NULL |
| 6.8860943e7 | Ich_bin_weg | 1.0 | 1.0 | 36.0 | wikitext | NULL |
| 6.8860953e7 | Yahya_Mahayni | 1.0 | 0.0 | 39.0 | wikitext | NULL |
| 6.8860957e7 | Barium_ethynediide | 1.0 | 1.0 | 27.0 | wikitext | NULL |
| 6.8860963e7 | P-synephrine | 1.0 | 1.0 | 24.0 | wikitext | NULL |
| 6.8860977e7 | Blessed_&_Free | 1.0 | 1.0 | 24.0 | wikitext | NULL |
| 6.886098e7 | Early-May_1933_tornado_outbreak_sequence | 1.0 | 1.0 | 106.0 | wikitext | NULL |
| 6.8861001e7 | Date_of_birth_and_personality | 1.0 | 1.0 | 35.0 | wikitext | NULL |
| 6.8861003e7 | Karl_Richard_Hanitsch | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8861005e7 | Personality_and_date_of_birth | 1.0 | 1.0 | 35.0 | wikitext | NULL |
| 6.8861007e7 | 2021–22_Serbian_Cup | 0.0 | 0.0 | 26127.0 | wikitext | NULL |
| 6.8861013e7 | Siege_of_Kufa | 1.0 | 0.0 | 145.0 | wikitext | NULL |
| 6.8861024e7 | Candy_Thuzar | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8861032e7 | Just_a_Waste_(PinkPantheress_song) | 1.0 | 0.0 | 36.0 | wikitext | NULL |
| 6.8861037e7 | La_Vie_d'artiste_(film) | 0.0 | 0.0 | 3611.0 | wikitext | NULL |
| 6.8861047e7 | US_Embassy_in_Berlin | 1.0 | 1.0 | 78.0 | wikitext | NULL |
| 6.8861053e7 | Gaualofa | 0.0 | 0.0 | 8798.0 | wikitext | NULL |
| 6.8861055e7 | Gilberto_García_(chess_player) | 0.0 | 0.0 | 2298.0 | wikitext | NULL |
| 6.8861064e7 | Andrée_Millar | 0.0 | 0.0 | 7806.0 | wikitext | NULL |
| 6.8861066e7 | Parliamentary_Office_for_the_Evaluation_of_Scientific_and_Technological_Choices | 0.0 | 0.0 | 23592.0 | wikitext | NULL |
| 6.8861068e7 | Attracted_to_You_(PinkPantheress_song) | 1.0 | 0.0 | 36.0 | wikitext | NULL |
| 6.8861073e7 | Nam_Tok_Sai_Yok_Noi_railway_halt | 1.0 | 1.0 | 66.0 | wikitext | NULL |
| 6.8861078e7 | Sonterra,_Texas | 0.0 | 0.0 | 3323.0 | wikitext | NULL |
| 6.8861079e7 | 2021–22_Zamalek_SC_(basketball)_season | 0.0 | 0.0 | 60744.0 | wikitext | NULL |
| 6.886108e7 | The_Work_(album) | 0.0 | 0.0 | 6095.0 | wikitext | NULL |
| 6.8861081e7 | The_Work_(Rivers_of_Nihil_album) | 1.0 | 1.0 | 41.0 | wikitext | NULL |
| 6.8861082e7 | Sonterra | 1.0 | 1.0 | 29.0 | wikitext | NULL |
| 6.8861084e7 | Rivers_of_Nihil_discography | 1.0 | 1.0 | 41.0 | wikitext | NULL |
| 6.8861113e7 | Jean_Paul_Hobler | 1.0 | 1.0 | 84.0 | wikitext | NULL |
| 6.8861121e7 | Listed_buildings_in_Barnsley_(Central_Ward) | 0.0 | 0.0 | 54609.0 | wikitext | NULL |
| 6.8861128e7 | Tetilla_capillosa | 0.0 | 0.0 | 2973.0 | wikitext | NULL |
| 6.8861131e7 | (326732)_2003_HB6 | 1.0 | 0.0 | 278.0 | wikitext | NULL |
| 6.8861134e7 | 2021_Ecuadorian_prison_riot | 1.0 | 0.0 | 50.0 | wikitext | NULL |
| 6.8861138e7 | Shanna_Swan | 0.0 | 0.0 | 5518.0 | wikitext | NULL |
| 6.8861154e7 | Funeral_Ceremonies | 0.0 | 0.0 | 5006.0 | wikitext | NULL |
| 6.8861156e7 | Abdorrasul_Zarrin | 0.0 | 0.0 | 9600.0 | wikitext | NULL |
| 6.8861158e7 | Banque_du_Peuple | 1.0 | 0.0 | 78.0 | wikitext | NULL |
| 6.8861159e7 | National_Board_of_Student_Aid_(Sweden) | 1.0 | 1.0 | 99.0 | wikitext | NULL |
| 6.8861163e7 | Kurdistan_Democratic_Independence_Party_(PASOK) | 0.0 | 0.0 | 2611.0 | wikitext | NULL |
| 6.8861164e7 | (285571)_2000_PQ9 | 1.0 | 0.0 | 278.0 | wikitext | NULL |
| 6.8861167e7 | 1951_South_Sydney_season | 0.0 | 0.0 | 12682.0 | wikitext | NULL |
| 6.886117e7 | Dea_Liane | 1.0 | 0.0 | 39.0 | wikitext | NULL |
| 6.8861186e7 | Barnabáš_Lacík | 0.0 | 1.0 | 2079.0 | wikitext | NULL |
| 6.8861201e7 | Alqabas | 1.0 | 1.0 | 69.0 | wikitext | NULL |
| 6.8861209e7 | Blauw-Wit_Beursbengels | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 6.8861211e7 | Petrol_panic | 1.0 | 0.0 | 52.0 | wikitext | NULL |
| 6.8861221e7 | Nick_McCloud | 0.0 | 0.0 | 5266.0 | wikitext | NULL |
| 6.8861227e7 | Unification_of_Germany_(1871) | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 6.8861231e7 | Santa_Rita_Ranch,_Texas | 0.0 | 0.0 | 3286.0 | wikitext | NULL |
| 6.8861233e7 | Santa_Rita_Ranch | 1.0 | 1.0 | 37.0 | wikitext | NULL |
| 6.8861234e7 | Jimmy_Dean_(baseball) | 0.0 | 0.0 | 2685.0 | wikitext | NULL |
| 6.8861244e7 | Watthana_Nakhon_railway_station | 0.0 | 0.0 | 1397.0 | wikitext | NULL |
| 6.8861245e7 | Border_abolitionism | 1.0 | 1.0 | 25.0 | wikitext | NULL |
| 6.8861248e7 | Boyfriend_(EP) | 1.0 | 1.0 | 18.0 | wikitext | NULL |
| 6.8861249e7 | Border_abolition | 1.0 | 1.0 | 25.0 | wikitext | NULL |
| 6.886125e7 | Boyfriend_(CKay_EP) | 1.0 | 1.0 | 18.0 | wikitext | NULL |
| 6.8861255e7 | Dickie_Moltisanti | 1.0 | 1.0 | 64.0 | wikitext | NULL |
| 6.8861258e7 | Faustina_Rehuher-Marugg | 1.0 | 0.0 | 57.0 | wikitext | NULL |
| 6.8861261e7 | Erkin_Tuniyaz | 0.0 | 0.0 | 6115.0 | wikitext | NULL |
| 6.8861262e7 | Nong_Sang_railway_station | 0.0 | 0.0 | 1364.0 | wikitext | NULL |
| 6.8861263e7 | Matthew_Smith | 0.0 | 0.0 | 3273.0 | wikitext | NULL |
| 6.8861267e7 | Caravaggio_(song) | 1.0 | 1.0 | 41.0 | wikitext | NULL |
| 6.8861268e7 | Matt_Smith_(disambiguation) | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 6.8861269e7 | Government_College_of_Education,_Komarapalayam | 0.0 | 0.0 | 3784.0 | wikitext | NULL |
| 6.886127e7 | Thomas_Burton_(16th_century_MP) | 0.0 | 0.0 | 4883.0 | wikitext | NULL |
| 6.8861274e7 | Caravaggio_(1.Cuz_song) | 1.0 | 1.0 | 19.0 | wikitext | NULL |
| 6.8861276e7 | Berlinia_grandiflora | 0.0 | 0.0 | 2781.0 | wikitext | NULL |
| 6.8861278e7 | Ban_Dong_Bang_railway_station | 0.0 | 0.0 | 1431.0 | wikitext | NULL |
| 6.886128e7 | Zhang_Jianmin | 0.0 | 0.0 | 4178.0 | wikitext | NULL |
| 6.8861283e7 | Deng_Jianjun | 0.0 | 0.0 | 3647.0 | wikitext | NULL |
| 6.8861286e7 | Michela_De_Rossi | 0.0 | 0.0 | 4008.0 | wikitext | NULL |
| 6.886129e7 | Mao_Jingwen | 0.0 | 0.0 | 3579.0 | wikitext | NULL |
| 6.8861293e7 | Prachantakham_railway_station | 0.0 | 0.0 | 1397.0 | wikitext | NULL |
| 6.8861294e7 | Mennekes_connector | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8861297e7 | Koreatwon,_Flushing | 1.0 | 1.0 | 31.0 | wikitext | NULL |
| 6.886131e7 | Alexandra_Intrator | 1.0 | 1.0 | 39.0 | wikitext | NULL |
| 6.8861312e7 | Khok_Makok_railway_station | 0.0 | 0.0 | 1388.0 | wikitext | NULL |
| 6.8861314e7 | Lauren_DiMario | 1.0 | 1.0 | 39.0 | wikitext | NULL |
| 6.8861317e7 | Johnny_Soprano | 1.0 | 1.0 | 56.0 | wikitext | NULL |
| 6.8861325e7 | Asclepiadeae | 1.0 | 1.0 | 41.0 | wikitext | NULL |
| 6.8861328e7 | Suicide_of_Etika | 1.0 | 0.0 | 19.0 | wikitext | NULL |
| 6.8861329e7 | Death_and_the_Maiden_(novel) | 0.0 | 0.0 | 1738.0 | wikitext | NULL |
| 6.8861334e7 | Sterphus_auricaudatus | 0.0 | 0.0 | 1526.0 | wikitext | NULL |
| 6.8861335e7 | Peter_Heering | 1.0 | 1.0 | 75.0 | wikitext | NULL |
| 6.8861343e7 | Build-up_(association_football) | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 6.8861344e7 | Ban_Pak_Phli_railway_station | 0.0 | 0.0 | 1697.0 | wikitext | NULL |
| 6.8861349e7 | Diocese_of_the_Romanian_Army | 0.0 | 0.0 | 5009.0 | wikitext | NULL |
| 6.886135e7 | Michael_and_Alice_Halkias | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8861353e7 | Auguste_Gérôme | 0.0 | 0.0 | 5689.0 | wikitext | NULL |
| 6.8861357e7 | Ban_Sang_railway_station | 0.0 | 0.0 | 1364.0 | wikitext | NULL |
| 6.886136e7 | Jehiel_Beman | 0.0 | 0.0 | 5038.0 | wikitext | NULL |
| 6.8861364e7 | Underbart_i_all_misär | 1.0 | 1.0 | 21.0 | wikitext | NULL |
| 6.8861365e7 | Bolshoy_Yeravna | 0.0 | 0.0 | 4534.0 | wikitext | NULL |
| 6.8861367e7 | Prachinburi_railway_station | 0.0 | 0.0 | 2222.0 | wikitext | NULL |
| 6.8861368e7 | Tanja_Gellenthien | 0.0 | 0.0 | 5679.0 | wikitext | NULL |
| 6.8861369e7 | Bolshoy_Yeravna_Lake | 1.0 | 1.0 | 29.0 | wikitext | NULL |
| 6.886137e7 | Melissa_Malzkuhn | 0.0 | 0.0 | 7541.0 | wikitext | NULL |
| 6.8861373e7 | Tanja_Jensen | 1.0 | 1.0 | 54.0 | wikitext | NULL |
| 6.8861376e7 | Mohamed_Shamas | 1.0 | 1.0 | 77.0 | wikitext | NULL |
| 6.8861382e7 | Bukit_Merah_double_murders | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8861384e7 | Fourth_Son_South | 0.0 | 0.0 | 3839.0 | wikitext | NULL |
| 6.8861387e7 | Two_Point_Campus | 0.0 | 0.0 | 9340.0 | wikitext | NULL |
| 6.8861389e7 | Angie_Ng_(murder_victim) | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.886139e7 | Naoki_Ishikawa_(photographer) | 0.0 | 0.0 | 20610.0 | wikitext | NULL |
| 6.8861391e7 | Jacaeber_Kastor | 0.0 | 0.0 | 10114.0 | wikitext | NULL |
| 6.8861393e7 | Crystal_Poh | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8861394e7 | List_of_English_football_transfers_winter_2021–22 | 0.0 | 0.0 | 168980.0 | wikitext | NULL |
| 6.8861399e7 | James_Winston | 0.0 | 0.0 | 425.0 | wikitext | NULL |
| 6.8861404e7 | Khlong_Bang_Phra_railway_station | 0.0 | 0.0 | 1475.0 | wikitext | NULL |
| 6.8861405e7 | Hans_Nylund | 0.0 | 1.0 | 1364.0 | wikitext | NULL |
| 6.8861409e7 | Anton_Edler_von_Schmid | 0.0 | 0.0 | 7688.0 | wikitext | NULL |
| 6.8861421e7 | Preng_railway_station | 0.0 | 0.0 | 1541.0 | wikitext | NULL |
| 6.8861422e7 | Khlong_Udom_Chonlajorn_Halt_railway_station | 1.0 | 1.0 | 49.0 | wikitext | NULL |
| 6.8861428e7 | Jan_Ørke | 0.0 | 1.0 | 1318.0 | wikitext | NULL |
| 6.8861436e7 | Jan_Orke | 1.0 | 1.0 | 22.0 | wikitext | NULL |
| 6.8861438e7 | Rathana_Club | 1.0 | 1.0 | 29.0 | wikitext | NULL |
| 6.886144e7 | 1923_West_Tennessee_State_Normal_football_team | 0.0 | 0.0 | 3809.0 | wikitext | NULL |
| 6.8861441e7 | Rakhagarhi | 1.0 | 1.0 | 24.0 | wikitext | NULL |
| 6.8861442e7 | Manlio_De_Domenico | 0.0 | 0.0 | 11894.0 | wikitext | NULL |
| 6.8861445e7 | Daniela_Rathana_discography | 1.0 | 1.0 | 41.0 | wikitext | NULL |
| 6.8861446e7 | Hans_Saksvik | 0.0 | 1.0 | 1374.0 | wikitext | NULL |
| 6.8861448e7 | Sarah_Story | 0.0 | 0.0 | 4272.0 | wikitext | NULL |
| 6.8861454e7 | Recursion_in_natural_languages | 1.0 | 1.0 | 82.0 | wikitext | NULL |
| 6.8861459e7 | Anton_von_Schmid | 1.0 | 1.0 | 36.0 | wikitext | NULL |
| 6.886146e7 | Sean_Rhyan | 0.0 | 0.0 | 7144.0 | wikitext | NULL |
| 6.8861463e7 | Epistlar | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8861466e7 | Kåre_Bjørnsen | 0.0 | 0.0 | 1462.0 | wikitext | NULL |
| 6.8861467e7 | Epistlar_(EP) | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8861472e7 | Kare_Bjornsen | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8861482e7 | Don_Si_Non_railway_station | 0.0 | 0.0 | 3011.0 | wikitext | NULL |
| 6.8861483e7 | Phil_L._Hudson_Municipal_Airport | 1.0 | 1.0 | 74.0 | wikitext | NULL |
| 6.8861496e7 | James_Winston_(thespian) | 0.0 | 0.0 | 765.0 | wikitext | NULL |
| 6.8861498e7 | 2021-22_Serbian_Cup | 1.0 | 1.0 | 178.0 | wikitext | NULL |
| 6.8861502e7 | 2021-22_EHF_European_League | 1.0 | 1.0 | 202.0 | wikitext | NULL |
| 6.8861503e7 | Phan_Thong_railway_station | 0.0 | 0.0 | 2992.0 | wikitext | NULL |
| 6.8861504e7 | Anton_Von_Schmid | 1.0 | 1.0 | 36.0 | wikitext | NULL |
| 6.8861507e7 | 2021_World_Wrestling_Championships_–_Men's_freestyle_61_kg | 0.0 | 0.0 | 8763.0 | wikitext | NULL |
| 6.8861508e7 | Listed_buildings_in_Cudworth,_South_Yorkshire | 0.0 | 0.0 | 3246.0 | wikitext | NULL |
| 6.886151e7 | Heropanti_2_(2022_film) | 1.0 | 0.0 | 63.0 | wikitext | NULL |
| 6.8861511e7 | List_of_English_football_transfers_winter_2021-22 | 1.0 | 1.0 | 268.0 | wikitext | NULL |
| 6.8861514e7 | 2021-22_Liga_IV_Galați | 1.0 | 0.0 | 272.0 | wikitext | NULL |
| 6.8861516e7 | Wilhelm_Eliassen | 0.0 | 0.0 | 1437.0 | wikitext | NULL |
| 6.8861518e7 | Tornado_outbreak_sequence_of_May_4-10,_1933 | 1.0 | 1.0 | 250.0 | wikitext | NULL |
| 6.8861523e7 | Servant_of_the_Mind | 0.0 | 0.0 | 21859.0 | wikitext | NULL |
| 6.8861525e7 | 2021_Asian_Table_Tennis_Championships_-_Women's_team | 1.0 | 1.0 | 277.0 | wikitext | NULL |
| 6.8861526e7 | Servant_of_the_Mind_(album) | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 6.8861529e7 | Servant_of_the_Mind_(Volbeat_album) | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 6.886153e7 | +-=÷x_Tour | 1.0 | 1.0 | 154.0 | wikitext | NULL |
| 6.8861532e7 | Bang_Phra_railway_station | 0.0 | 0.0 | 3003.0 | wikitext | NULL |
| 6.8861533e7 | Pucheng-Meizhou_railway | 1.0 | 1.0 | 190.0 | wikitext | NULL |
| 6.8861536e7 | 2021_World_Wrestling_Championships_-_Men's_freestyle_61_kg | 1.0 | 1.0 | 295.0 | wikitext | NULL |
| 6.8861545e7 | Kåre_Aasgaard | 0.0 | 0.0 | 1415.0 | wikitext | NULL |
| 6.8861551e7 | Kare_Aasgaard | 1.0 | 1.0 | 27.0 | wikitext | NULL |
| 6.8861552e7 | Ban_Huai_Khwang_railway_station | 0.0 | 0.0 | 3119.0 | wikitext | NULL |
| 6.8861553e7 | Jaume_Masiá | 1.0 | 1.0 | 73.0 | wikitext | NULL |
| 6.8861561e7 | Roald_Paulsen | 0.0 | 1.0 | 1343.0 | wikitext | NULL |
| 6.8861568e7 | Anthonio_Sanjairag | 0.0 | 0.0 | 4105.0 | wikitext | NULL |
| 6.886157e7 | Tor_Wæhler | 0.0 | 1.0 | 1327.0 | wikitext | NULL |
| 6.8861575e7 | Mulholland_Drive_(album) | 0.0 | 0.0 | 8628.0 | wikitext | NULL |
| 6.8861577e7 | Chonburi_railway_station | 0.0 | 0.0 | 3598.0 | wikitext | NULL |
| 6.8861578e7 | Tor_Waehler | 1.0 | 1.0 | 24.0 | wikitext | NULL |
| 6.8861582e7 | 1898_Nebraska_gubernatorial_election | 0.0 | 0.0 | 6402.0 | wikitext | NULL |
| 6.8861583e7 | Circuit_Laundry | 1.0 | 1.0 | 27.0 | wikitext | NULL |
| 6.8861585e7 | Cynanchum_pulchellum | 0.0 | 0.0 | 1238.0 | wikitext | NULL |
| 6.886159e7 | Svein_Hammerø | 0.0 | 1.0 | 1350.0 | wikitext | NULL |
| 6.8861593e7 | Svein_Hammero | 1.0 | 1.0 | 27.0 | wikitext | NULL |
| 6.8861596e7 | Savage_River_(TV_series) | 0.0 | 0.0 | 11382.0 | wikitext | NULL |
| 6.8861598e7 | Børge_Josefsen | 0.0 | 1.0 | 1358.0 | wikitext | NULL |
| 6.88616e7 | Borge_Josefsen | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8861602e7 | The_Dancing_Druids | 0.0 | 0.0 | 1868.0 | wikitext | NULL |
| 6.8861607e7 | Finn_Vådahl | 0.0 | 1.0 | 1333.0 | wikitext | NULL |
| 6.8861611e7 | Rutherford_B._Hayes_Presidential_Library_&_Museums | 1.0 | 1.0 | 53.0 | wikitext | NULL |
| 6.8861615e7 | Finn_Vadahl | 1.0 | 1.0 | 25.0 | wikitext | NULL |
| 6.8861617e7 | Oxalis_bifida | 0.0 | 0.0 | 2900.0 | wikitext | NULL |
| 6.8861627e7 | 2021_AFL_Sydney | 1.0 | 1.0 | 156.0 | wikitext | NULL |
| 6.886163e7 | Dancing_on_My_Knees | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 6.8861632e7 | Melbourne_Welsh_Church | 0.0 | 0.0 | 4587.0 | wikitext | NULL |
| 6.8861636e7 | Ole_Kristian_Olsen | 0.0 | 1.0 | 1366.0 | wikitext | NULL |
| 6.8861638e7 | Jarle_Bernhoft_discography | 1.0 | 1.0 | 40.0 | wikitext | NULL |
| 6.8861644e7 | SF_Mono | 1.0 | 1.0 | 57.0 | wikitext | NULL |
| 6.8861648e7 | Impact_of_the_COVID-19_pandemic_on_gridiron_football | 0.0 | 0.0 | 61782.0 | wikitext | NULL |
| 6.886165e7 | Erik_Karlsen | 0.0 | 0.0 | 1839.0 | wikitext | NULL |
| 6.8861663e7 | Angie_Ng_Wee_Peng | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8861665e7 | The_Art_of_Disappearing | 1.0 | 1.0 | 23.0 | wikitext | NULL |
| 6.8861667e7 | Crystal_Poh_Shi_Qi | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8861669e7 | Women's_Wrestling_Grand_Prize | 1.0 | 0.0 | 123.0 | wikitext | NULL |
| 6.886167e7 | Rune_Hansen | 0.0 | 0.0 | 1853.0 | wikitext | NULL |
| 6.8861671e7 | Murders_of_Angie_Ng_and_Crystal_Poh | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8861678e7 | Rachel_David | 0.0 | 0.0 | 9366.0 | wikitext | NULL |
| 6.8861686e7 | UFC_Fight_Night_199 | 1.0 | 0.0 | 48.0 | wikitext | NULL |
| 6.8861691e7 | Pa_Sheehy_discography | 1.0 | 1.0 | 35.0 | wikitext | NULL |
| 6.8861695e7 | Association_of_Polish_Electrical_Engineers | 0.0 | 0.0 | 6602.0 | wikitext | NULL |
| 6.8861697e7 | While_We_Live | 0.0 | 0.0 | 4140.0 | wikitext | NULL |
| 6.8861699e7 | The_Polymath | 0.0 | 0.0 | 15716.0 | wikitext | NULL |
| 6.88617e7 | Joshi_Puroresu_Grand_Prize | 1.0 | 0.0 | 123.0 | wikitext | NULL |
| 6.8861701e7 | Ground_Controlled_Approach_Squadron_RAF | 1.0 | 1.0 | 100.0 | wikitext | NULL |
| 6.8861702e7 | Ground_Controlled_Approach_Flight_RAF | 1.0 | 1.0 | 100.0 | wikitext | NULL |
| 6.8861703e7 | Human_hermaphroditism | 1.0 | 0.0 | 70.0 | wikitext | NULL |
| 6.8861707e7 | Joshua_Vanneck | 0.0 | 1.0 | 275.0 | wikitext | NULL |
| 6.8861711e7 | Franz_Schmidt_(serial_killer) | 0.0 | 0.0 | 7866.0 | wikitext | NULL |
| 6.8861715e7 | PonJola_Coney | 0.0 | 0.0 | 6349.0 | wikitext | NULL |
| 6.8861722e7 | The_Lathums_discography | 1.0 | 1.0 | 37.0 | wikitext | NULL |
| 6.8861723e7 | East_Basin,_Utah | 0.0 | 1.0 | 3712.0 | wikitext | NULL |
| 6.8861739e7 | 2021_World_Wrestling_Championships_–_Men's_freestyle_125_kg | 0.0 | 0.0 | 6785.0 | wikitext | NULL |
| 6.8861773e7 | Ida_Bagus_Putra_Manuaba | 0.0 | 0.0 | 4073.0 | wikitext | NULL |
| 6.8861783e7 | Drina_National_Park | 0.0 | 0.0 | 4738.0 | wikitext | NULL |
| 6.8861784e7 | Ceriogaster_auricaudatus | 1.0 | 1.0 | 35.0 | wikitext | NULL |
| 6.8861798e7 | Oleh_Synyehubov | 0.0 | 0.0 | 9731.0 | wikitext | NULL |
| 6.88618e7 | No._1312_Mobile_Wing_RAF_Regiment | 1.0 | 1.0 | 96.0 | wikitext | NULL |
| 6.8861801e7 | No._1315_Mobile_Wing_RAF_Regiment | 1.0 | 1.0 | 96.0 | wikitext | NULL |
| 6.8861803e7 | Nijel_Pack | 0.0 | 0.0 | 8170.0 | wikitext | NULL |
| 6.8861807e7 | Sienna_Mapelli_Mozzi | 1.0 | 0.0 | 302.0 | wikitext | NULL |
| 6.8861828e7 | No._2893_Squadron_RAF_Regiment | 1.0 | 1.0 | 104.0 | wikitext | NULL |
| 6.886183e7 | Parti_Libre_Canada | 1.0 | 0.0 | 107.0 | wikitext | NULL |
| 6.8861845e7 | 1941_Spring_Hill_Badgers_football_team | 0.0 | 0.0 | 5517.0 | wikitext | NULL |
| 6.8861848e7 | Adolfo_Infante | 0.0 | 0.0 | 8149.0 | wikitext | NULL |
| 6.8861849e7 | 2021_UCI_Road_World_Championships_–_Men's_under-23_time_trial | 0.0 | 0.0 | 6687.0 | wikitext | NULL |
| 6.8861859e7 | Volevo_fare_la_Rockstar | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8861861e7 | Richard_Sseruwagi | 0.0 | 0.0 | 4659.0 | wikitext | NULL |
| 6.8861863e7 | Volevo_fare_la_rockstar | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8861868e7 | Volevo_fare_la_rockstar_(album) | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8861879e7 | Meredith_Calhoun | 0.0 | 0.0 | 1684.0 | wikitext | NULL |
| 6.886188e7 | Caproni_Transaero | 1.0 | 0.0 | 87.0 | wikitext | NULL |
| 6.8861881e7 | Susil_Ranjan_Chattopadhyay | 0.0 | 0.0 | 2211.0 | wikitext | NULL |
| 6.8861891e7 | Meglio_del_cinema | 1.0 | 1.0 | 19.0 | wikitext | NULL |
| 6.8861901e7 | Age_of_consent_in_Ireland | 1.0 | 1.0 | 227.0 | wikitext | NULL |
| 6.8861911e7 | Caproni_Transaereo | 1.0 | 1.0 | 27.0 | wikitext | NULL |
| 6.8861912e7 | Susil_Ranjan_Chatterjee | 1.0 | 1.0 | 40.0 | wikitext | NULL |
| 6.8861913e7 | Lake_226 | 0.0 | 0.0 | 12193.0 | wikitext | NULL |
| 6.8861919e7 | Consulate-General_of_the_United_Kingdom,_Osaka | 1.0 | 0.0 | 107.0 | wikitext | NULL |
| 6.8861923e7 | CC-295_Kingfisher | 1.0 | 1.0 | 29.0 | wikitext | NULL |
| 6.8861924e7 | CC-295 | 1.0 | 1.0 | 29.0 | wikitext | NULL |
| 6.8861931e7 | Embassy_of_the_State_of_Palestine,_Tokyo | 1.0 | 1.0 | 88.0 | wikitext | NULL |
| 6.8861933e7 | Pseudophilautus_munnarensis | 1.0 | 1.0 | 76.0 | wikitext | NULL |
| 6.8861937e7 | Embassy_of_the_State_of_Palestine,_Manama | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886194e7 | Philautus_munnarensis | 1.0 | 1.0 | 76.0 | wikitext | NULL |
| 6.8861941e7 | Embassy_of_the_State_of_Palestine,_Hanoi | 1.0 | 1.0 | 88.0 | wikitext | NULL |
| 6.8861945e7 | 2021–22_EuroLeague_Regular_Season | 0.0 | 0.0 | 302424.0 | wikitext | NULL |
| 6.8861954e7 | Invasión_de_Bahia_de_Cochinos | 1.0 | 0.0 | 110.0 | wikitext | NULL |
| 6.8861955e7 | Hobble_Creek,_Utah | 0.0 | 1.0 | 3417.0 | wikitext | NULL |
| 6.8861957e7 | List_of_islands_of_Sint_Maarten | 1.0 | 1.0 | 54.0 | wikitext | NULL |
| 6.8861959e7 | The_Crozier_Pharaohs | 0.0 | 0.0 | 1891.0 | wikitext | NULL |
| 6.8861961e7 | RetroCrush | 1.0 | 0.0 | 105.0 | wikitext | NULL |
| 6.8861976e7 | Greater_Turkey | 1.0 | 1.0 | 29.0 | wikitext | NULL |
| 6.8861991e7 | Cyclone_Shaheen | 1.0 | 0.0 | 67.0 | wikitext | NULL |
| 6.8861999e7 | Thomas_Morita | 1.0 | 1.0 | 27.0 | wikitext | NULL |
| 6.8862001e7 | Postmodern_television | 0.0 | 0.0 | 7221.0 | wikitext | NULL |
| 6.886201e7 | Opaas | 1.0 | 0.0 | 76.0 | wikitext | NULL |
| 6.8862012e7 | Paul_Quessenberry | 0.0 | 0.0 | 6169.0 | wikitext | NULL |
| 6.8862017e7 | René_Bochmann | 0.0 | 0.0 | 1679.0 | wikitext | NULL |
| 6.8862021e7 | NWA_Hard_Times_2 | 0.0 | 0.0 | 18575.0 | wikitext | NULL |
| 6.8862024e7 | John_Norman_(16th_century_MP) | 0.0 | 0.0 | 6164.0 | wikitext | NULL |
| 6.8862028e7 | Binaghi | 1.0 | 0.0 | 78.0 | wikitext | NULL |
| 6.8862037e7 | Frojen | 1.0 | 0.0 | 77.0 | wikitext | NULL |
| 6.8862041e7 | Volkswagen_ID._Life | 0.0 | 0.0 | 6022.0 | wikitext | NULL |
| 6.8862047e7 | Shani_Alhassan_Saibu | 0.0 | 0.0 | 949.0 | wikitext | NULL |
| 6.8862049e7 | RMS_Arabia_(1852) | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8862051e7 | List_of_PDO_products_by_country | 0.0 | 0.0 | 271068.0 | wikitext | NULL |
| 6.8862059e7 | Siegelaar | 0.0 | 0.0 | 481.0 | wikitext | NULL |
| 6.8862062e7 | Schins | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 6.8862077e7 | Roman_Museum_Remchingen | 0.0 | 0.0 | 4957.0 | wikitext | NULL |
| 6.8862082e7 | Marie_Therese_Schins | 1.0 | 1.0 | 87.0 | wikitext | NULL |
| 6.8862083e7 | Marie-Therese_Schins | 1.0 | 1.0 | 60.0 | wikitext | NULL |
| 6.8862087e7 | Marie_Thérèse_Schins | 1.0 | 1.0 | 64.0 | wikitext | NULL |
| 6.8862088e7 | Paul_Stanhope | 0.0 | 0.0 | 18199.0 | wikitext | NULL |
| 6.8862093e7 | NWA_Hard_Times | 0.0 | 0.0 | 3086.0 | wikitext | NULL |
| 6.88621e7 | 3Φ_power | 1.0 | 1.0 | 40.0 | wikitext | NULL |
| 6.8862113e7 | Ducati_350_Sebring | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8862119e7 | No._1_Anti-Aircraft_Calibration_Flight_RAF | 1.0 | 1.0 | 34.0 | wikitext | NULL |
| 6.886212e7 | Jaedon_Descheneau | 0.0 | 0.0 | 1291.0 | wikitext | NULL |
| 6.8862126e7 | Brazen_Tongue | 0.0 | 0.0 | 1558.0 | wikitext | NULL |
| 6.8862127e7 | No._1311_Mobile_Wing_RAF_Regiment | 1.0 | 1.0 | 96.0 | wikitext | NULL |
| 6.8862136e7 | No._2881_Squadron_RAF_Regiment | 1.0 | 1.0 | 104.0 | wikitext | NULL |
| 6.8862138e7 | No._2883_Squadron_RAF_Regiment | 1.0 | 1.0 | 104.0 | wikitext | NULL |
| 6.8862139e7 | No._2895_Squadron_RAF_Regiment | 1.0 | 1.0 | 104.0 | wikitext | NULL |
| 6.8862141e7 | Yeshiva_Toras_Emes_Kamenitz | 1.0 | 1.0 | 41.0 | wikitext | NULL |
| 6.8862142e7 | Matzliach_ben_Phinhas_ben_Yitzhaq_ben_Shalma | 0.0 | 0.0 | 2181.0 | wikitext | NULL |
| 6.8862143e7 | Body_Offering_(novel) | 0.0 | 0.0 | 7338.0 | wikitext | NULL |
| 6.8862147e7 | Max_(French_magazine) | 0.0 | 0.0 | 2246.0 | wikitext | NULL |
| 6.8862154e7 | Manlio_de_domenico | 1.0 | 1.0 | 79.0 | wikitext | NULL |
| 6.8862161e7 | Likambo_Ya_Ngana | 1.0 | 0.0 | 27.0 | wikitext | NULL |
| 6.8862162e7 | Jacaeber_kastor | 1.0 | 1.0 | 76.0 | wikitext | NULL |
| 6.8862166e7 | Magazine_Max | 1.0 | 0.0 | 44.0 | wikitext | NULL |
| 6.886217e7 | Recreational_obfuscation | 1.0 | 1.0 | 61.0 | wikitext | NULL |
| 6.8862172e7 | Town_of_Bloomsburg | 1.0 | 1.0 | 92.0 | wikitext | NULL |
| 6.8862176e7 | Asher_ben_Matzliach_ben_Phinhas | 0.0 | 0.0 | 1925.0 | wikitext | NULL |
| 6.8862177e7 | Dena_G._Hernandez | 0.0 | 0.0 | 1624.0 | wikitext | NULL |
| 6.8862184e7 | No._2721_Squadron_RAF_Regiment | 1.0 | 1.0 | 104.0 | wikitext | NULL |
| 6.8862185e7 | Phinehas_X_ben_Matzliach_ben_Phinehas | 0.0 | 0.0 | 2029.0 | wikitext | NULL |
| 6.886219e7 | Hucclecote_(parish) | 0.0 | 0.0 | 1911.0 | wikitext | NULL |
| 6.8862191e7 | Ontario_Association_of_Art_Galleries | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 6.8862195e7 | MŠK_Žilina_Africa_F.C. | 0.0 | 0.0 | 7158.0 | wikitext | NULL |
| 6.8862199e7 | Amarillo_Badgers | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8862229e7 | Signals_Co-operation_Flight_RAF | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8862235e7 | Myrrha_(short_story) | 0.0 | 0.0 | 1910.0 | wikitext | NULL |
| 6.8862291e7 | 2006_FIBA_Americas_Under-20_Championship_for_Women | 0.0 | 0.0 | 7143.0 | wikitext | NULL |
| 6.8862303e7 | Euskotren_3150_series | 0.0 | 0.0 | 6316.0 | wikitext | NULL |
| 6.8862306e7 | Mountjoy_Prison_Complex | 1.0 | 0.0 | 62.0 | wikitext | NULL |
| 6.8862315e7 | Nokia_8800_Sirocco_Edition | 1.0 | 0.0 | 98.0 | wikitext | NULL |
| 6.8862321e7 | Diplomat's_Folly | 0.0 | 0.0 | 1778.0 | wikitext | NULL |
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| 6.886245e7 | Trieschmann | 0.0 | 0.0 | 290.0 | wikitext | NULL |
| 6.8862451e7 | Paragonaster | 0.0 | 0.0 | 1176.0 | wikitext | NULL |
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| 6.8862462e7 | C'mon,_You_Know | 1.0 | 0.0 | 28.0 | wikitext | NULL |
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| 6.8862478e7 | Paul_Booker | 1.0 | 1.0 | 28.0 | wikitext | NULL |
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| 6.8862885e7 | MD_871G | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863023e7 | Maryland_Route_879D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863024e7 | Jasper_Forest_Park | 1.0 | 1.0 | 75.0 | wikitext | NULL |
| 6.8863025e7 | Maryland_State_Highway_879D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863028e7 | Maryland_State_Route_879D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863031e7 | Maryland_879D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863033e7 | MD_879D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863035e7 | Route_879D_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863036e7 | Maryland_Route_879E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863038e7 | Maryland_State_Highway_879E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863039e7 | Maryland_State_Route_879E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886304e7 | Beth_medrash | 1.0 | 1.0 | 26.0 | wikitext | NULL |
| 6.8863042e7 | Maryland_879E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863044e7 | MD_879E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863045e7 | Route_879E_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886305e7 | Maryland_State_Route_895 | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863053e7 | Maryland_895 | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863056e7 | MD_895 | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863057e7 | 2021–22_in_Bangladeshi_Football | 0.0 | 0.0 | 7334.0 | wikitext | NULL |
| 6.8863058e7 | Team_Carinthia | 0.0 | 0.0 | 11123.0 | wikitext | NULL |
| 6.8863059e7 | Route_895_(Maryland) | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863061e7 | Maryland_Route_899A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863063e7 | Maryland_State_Highway_899A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863065e7 | Maryland_State_Route_899A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863068e7 | Maryland_899A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863069e7 | MD_899A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886307e7 | CYCLOPS_(junction) | 1.0 | 1.0 | 36.0 | wikitext | NULL |
| 6.8863072e7 | Route_899A_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863074e7 | Dicranopalpus_fraternus | 0.0 | 0.0 | 1141.0 | wikitext | NULL |
| 6.8863076e7 | 1978_Montana_State_Bobcats_football_team | 0.0 | 0.0 | 3702.0 | wikitext | NULL |
| 6.8863078e7 | Maryland_State_Route_901 | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863079e7 | Maryland_901 | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863083e7 | 1963_European_Ladies'_Team_Championship | 0.0 | 0.0 | 11608.0 | wikitext | NULL |
| 6.886309e7 | MD_901 | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863093e7 | Route_901_(Maryland) | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863096e7 | Edward_Ledwich_(Dean_of_Kildare) | 1.0 | 1.0 | 84.0 | wikitext | NULL |
| 6.8863098e7 | Maryland_Route_904A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863099e7 | 1976_Delaware_State_Hornets_football_team | 0.0 | 0.0 | 7567.0 | wikitext | NULL |
| 6.8863102e7 | Maryland_State_Highway_904A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863104e7 | Maryland_State_Route_904A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863105e7 | Maryland_904A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863107e7 | MD_904A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863108e7 | Route_904A_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863109e7 | Klaus_Zyciora | 0.0 | 0.0 | 3889.0 | wikitext | NULL |
| 6.886311e7 | Maryland_Route_904D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863111e7 | Maryland_State_Highway_904D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863112e7 | The_Tourist_(2021_film) | 1.0 | 0.0 | 63.0 | wikitext | NULL |
| 6.8863113e7 | Maryland_State_Route_904D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863117e7 | Edward_Layton | 0.0 | 0.0 | 216.0 | wikitext | NULL |
| 6.8863121e7 | Maryland_904D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863122e7 | MD_904D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863123e7 | Route_904D_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863125e7 | Maryland_Route_904F | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863129e7 | François_Vérove | 0.0 | 0.0 | 15196.0 | wikitext | NULL |
| 6.8863131e7 | Maryland_State_Highway_904F | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863133e7 | Maryland_State_Route_904F | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863136e7 | Maryland_904F | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863139e7 | MD_904F | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863141e7 | Route_904F_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863143e7 | Maryland_Route_904H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863144e7 | Maryland_State_Highway_904H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863145e7 | Chantal_Youdom | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8863147e7 | Maryland_State_Route_904H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863148e7 | Maryland_904H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863149e7 | MD_904H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863151e7 | Route_904H_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863152e7 | Maryland_Route_904I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863153e7 | François_Verove | 1.0 | 1.0 | 31.0 | wikitext | NULL |
| 6.8863155e7 | Maryland_State_Highway_904I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863158e7 | Maryland_State_Route_904I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863161e7 | Maryland_904I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863163e7 | Francois_Vérove | 1.0 | 1.0 | 31.0 | wikitext | NULL |
| 6.8863164e7 | MD_904I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863165e7 | Vérove | 1.0 | 0.0 | 81.0 | wikitext | NULL |
| 6.8863166e7 | Route_904I_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863171e7 | Maryland_Route_910B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863173e7 | Maryland_State_Highway_910B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863175e7 | Sterphus_aurifrons | 0.0 | 0.0 | 1048.0 | wikitext | NULL |
| 6.8863176e7 | Maryland_State_Route_910B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863178e7 | Maryland_910B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863179e7 | MD_910B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886318e7 | Route_910B_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863183e7 | Maryland_Route_910C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863186e7 | Maryland_State_Highway_910C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886319e7 | Maryland_State_Route_910C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863192e7 | Merlon_(disambiguation) | 0.0 | 0.0 | 316.0 | wikitext | NULL |
| 6.8863193e7 | Maryland_910C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863194e7 | MD_910C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863195e7 | Berezyne,_Odesa_Oblast | 0.0 | 0.0 | 4384.0 | wikitext | NULL |
| 6.8863196e7 | Route_910C_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863198e7 | Maryland_Route_912A | 1.0 | 1.0 | 71.0 | wikitext | NULL |
| 6.8863199e7 | Maryland_State_Highway_912A | 1.0 | 1.0 | 71.0 | wikitext | NULL |
| 6.8863201e7 | Maryland_State_Route_912A | 1.0 | 1.0 | 71.0 | wikitext | NULL |
| 6.8863206e7 | Maryland_912A | 1.0 | 1.0 | 71.0 | wikitext | NULL |
| 6.8863207e7 | MD_912A | 1.0 | 1.0 | 71.0 | wikitext | NULL |
| 6.8863209e7 | Route_912A_(Maryland) | 1.0 | 1.0 | 71.0 | wikitext | NULL |
| 6.8863217e7 | King-Lincoln | 1.0 | 1.0 | 38.0 | wikitext | NULL |
| 6.8863223e7 | Maryland_State_Highway_915A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863226e7 | Maryland_State_Route_915A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863227e7 | Ctenacanthidae | 0.0 | 0.0 | 2028.0 | wikitext | NULL |
| 6.8863228e7 | 1936–37_NHL_transactions | 0.0 | 0.0 | 8631.0 | wikitext | NULL |
| 6.8863229e7 | Maryland_915A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886323e7 | MD_915A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863231e7 | Route_915A_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863232e7 | Maryland_State_Highway_915H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863233e7 | Maryland_State_Route_915H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863235e7 | Maryland_915H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863237e7 | MD_915H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863238e7 | Route_915H_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863242e7 | Maryland_State_Highway_920J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863244e7 | Maryland_State_Route_920J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863246e7 | Maryland_920J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863248e7 | MD_920J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886325e7 | Medicare_negotiation_of_drug_prices | 1.0 | 1.0 | 87.0 | wikitext | NULL |
| 6.8863262e7 | 2021-2022_Kalamata_F.C._Season | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 6.8863264e7 | Route_920J_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863265e7 | QLD_PGA_Championship | 1.0 | 0.0 | 41.0 | wikitext | NULL |
| 6.8863266e7 | Maryland_State_Highway_920K | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863272e7 | Maryland_State_Route_920K | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863273e7 | Maryland_920K | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863275e7 | MD_920K | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863277e7 | Route_920K_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886328e7 | Maryland_State_Highway_920L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863281e7 | No_such_thing_as_a_dumb_question | 1.0 | 1.0 | 77.0 | wikitext | NULL |
| 6.8863282e7 | Maryland_State_Route_920L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863283e7 | Maryland_920L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863284e7 | Pablo_Dabezies | 0.0 | 0.0 | 1585.0 | wikitext | NULL |
| 6.8863285e7 | Paul_Dabezies | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8863286e7 | MD_920L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863288e7 | 2021–2022_Kalamata_F.C._season | 1.0 | 1.0 | 91.0 | wikitext | NULL |
| 6.8863289e7 | Route_920L_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863291e7 | Maryland_State_Highway_920M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863293e7 | Maryland_State_Route_920M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863294e7 | Table_Mountain_Facility | 1.0 | 0.0 | 88.0 | wikitext | NULL |
| 6.8863296e7 | Maryland_920M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863298e7 | John_K._Kruschke | 0.0 | 0.0 | 22217.0 | wikitext | NULL |
| 6.8863299e7 | MD_920M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.88633e7 | Poke_It_Out_(song) | 1.0 | 1.0 | 25.0 | wikitext | NULL |
| 6.8863302e7 | Route_920M_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863306e7 | Maryland_State_Highway_920N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863308e7 | National_Center_for_Science_and_Engineering_Statistics | 0.0 | 0.0 | 29945.0 | wikitext | NULL |
| 6.886331e7 | Sienna_Elizabeth_Mapelli_Mozzi | 1.0 | 0.0 | 45.0 | wikitext | NULL |
| 6.8863311e7 | Maryland_State_Route_920N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863312e7 | Edward_Monckton_(MP) | 1.0 | 1.0 | 104.0 | wikitext | NULL |
| 6.8863313e7 | Poke_It_Out_(Playboi_Carti_song) | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 6.8863319e7 | Maryland_920N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886332e7 | Sterphus_batesi | 0.0 | 0.0 | 1545.0 | wikitext | NULL |
| 6.8863321e7 | MD_920N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863322e7 | Route_920N_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863323e7 | Maryland_State_Highway_920O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863327e7 | Maryland_State_Route_920O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863332e7 | Maryland_920O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863333e7 | MD_920O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863335e7 | Poke_It_Out_(Playboi_Carti_and_Nicki_Minaj_song) | 1.0 | 1.0 | 240.0 | wikitext | NULL |
| 6.8863336e7 | Route_920O_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863337e7 | Maryland_State_Highway_920P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863338e7 | Maryland_State_Route_920P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886334e7 | Maryland_920P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863343e7 | MD_920P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863344e7 | Route_920P_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863345e7 | Maryland_State_Highway_920Q | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863346e7 | Maryland_State_Route_920Q | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863347e7 | Maryland_920Q | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863349e7 | MD_920Q | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863351e7 | Eisenhower_boom | 1.0 | 1.0 | 39.0 | wikitext | NULL |
| 6.8863353e7 | Route_920Q_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863354e7 | Maryland_State_Highway_920R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863355e7 | Maryland_State_Route_920R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863357e7 | FolarIIn | 1.0 | 0.0 | 48.0 | wikitext | NULL |
| 6.8863358e7 | Maryland_920R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886336e7 | MD_920R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863361e7 | Route_920R_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863363e7 | Yoshishige_Saitō | 0.0 | 0.0 | 24817.0 | wikitext | NULL |
| 6.8863365e7 | Maryland_State_Highway_920S | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863367e7 | Maryland_State_Route_920S | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863368e7 | Maryland_920S | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863369e7 | Spinifex_littoreus | 0.0 | 0.0 | 2616.0 | wikitext | NULL |
| 6.8863371e7 | MD_920S | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863372e7 | Route_920S_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863373e7 | Maryland_State_Highway_920T | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863374e7 | Maryland_State_Route_920T | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863376e7 | Maryland_920T | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863377e7 | Jean-Michel_Kibushi_Ndjate_Wooto | 0.0 | 0.0 | 6515.0 | wikitext | NULL |
| 6.8863378e7 | MD_920T | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863379e7 | Route_920T_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886338e7 | Donga_bonga | 1.0 | 1.0 | 73.0 | wikitext | NULL |
| 6.8863383e7 | Maryland_Route_921A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863384e7 | Maryland_State_Highway_921A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863387e7 | Maryland_State_Route_921A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863389e7 | Maryland_921A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863391e7 | MD_921A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863392e7 | Route_921A_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863393e7 | Maryland_Route_921B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863394e7 | Maryland_State_Highway_921B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863396e7 | Lincoln_(CDP),_Vermont | 0.0 | 1.0 | 3714.0 | wikitext | NULL |
| 6.8863397e7 | Maryland_State_Route_921B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863399e7 | Maryland_921B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.88634e7 | Addai-Sebo | 1.0 | 1.0 | 32.0 | wikitext | NULL |
| 6.8863402e7 | MD_921B | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863404e7 | Maralixibat | 1.0 | 1.0 | 81.0 | wikitext | NULL |
| 6.8863406e7 | Route_921B_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863407e7 | Maryland_Route_921C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863408e7 | SpaceStation_Gaming | 1.0 | 1.0 | 80.0 | wikitext | NULL |
| 6.886341e7 | Maryland_State_Highway_921C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863411e7 | Maryland_State_Route_921C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863412e7 | Maryland_921C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863413e7 | MD_921C | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863414e7 | Route_921C_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863415e7 | Maryland_Route_921D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863416e7 | Maryland_State_Highway_921D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863417e7 | Maryland_State_Route_921D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863418e7 | Timothy_Mark_Hely_Hutchinson | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 6.8863419e7 | Maryland_921D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886342e7 | MD_921D | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863436e7 | MD_921E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863444e7 | Maryland_State_Highway_921F | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863449e7 | Maryland_921F | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886345e7 | MD_921F | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863452e7 | Michael_A._Sussmann | 1.0 | 0.0 | 58.0 | wikitext | NULL |
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| 6.8863455e7 | Maryland_State_Highway_921G | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863456e7 | Maryland_State_Route_921G | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863457e7 | Maryland_921G | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863458e7 | MD_921G | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863466e7 | Gutai_group | 1.0 | 1.0 | 82.0 | wikitext | NULL |
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| 6.8863473e7 | Maryland_State_Highway_921H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863475e7 | Maryland_State_Route_921H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863477e7 | Maryland_921H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863479e7 | MD_921H | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863483e7 | Maryland_Route_921I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863485e7 | Maryland_State_Highway_921I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863486e7 | Maryland_State_Route_921I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863487e7 | Maryland_921I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863489e7 | MD_921I | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863491e7 | Maryland_Route_921J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863492e7 | Maryland_State_Highway_921J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863493e7 | Island_Hall | 0.0 | 0.0 | 4142.0 | wikitext | NULL |
| 6.8863494e7 | Maryland_State_Route_921J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863496e7 | Maryland_921J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863497e7 | BDO_British_Open | 1.0 | 1.0 | 81.0 | wikitext | NULL |
| 6.8863499e7 | Le_Grele | 1.0 | 1.0 | 31.0 | wikitext | NULL |
| 6.8863501e7 | MD_921J | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863508e7 | Maryland_State_Highway_921K | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863513e7 | Maryland_State_Route_921K | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863515e7 | Maryland_921K | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863517e7 | MD_921K | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863522e7 | Maryland_Route_921L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863524e7 | Maryland_State_Highway_921L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863527e7 | Maryland_State_Route_921L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.886353e7 | Maryland_921L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863533e7 | MD_921L | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863534e7 | Route_921L_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863535e7 | Maryland_Route_921M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863537e7 | Maryland_State_Highway_921M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863538e7 | The_Beaker_Girls | 0.0 | 0.0 | 11298.0 | wikitext | NULL |
| 6.8863539e7 | Maryland_State_Route_921M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863541e7 | Maryland_921M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863542e7 | MD_921M | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863543e7 | Tocado | 1.0 | 0.0 | 95.0 | wikitext | NULL |
| 6.8863544e7 | Route_921M_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863547e7 | Maryland_Route_921N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863548e7 | Maryland_State_Highway_921N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863549e7 | Maryland_State_Route_921N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863551e7 | Maryland_921N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863552e7 | MD_921N | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863554e7 | Route_921N_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863556e7 | Maryland_Route_921O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863561e7 | Maryland_State_Highway_921O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863563e7 | Maryland_State_Route_921O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863565e7 | Maryland_921O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863568e7 | MD_921O | 1.0 | 1.0 | 89.0 | wikitext | NULL |
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| 6.8863573e7 | Maryland_Route_921P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863574e7 | Maryland_State_Highway_921P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863575e7 | Maryland_State_Route_921P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863576e7 | Edward_Rath | 0.0 | 1.0 | 199.0 | wikitext | NULL |
| 6.8863577e7 | Maryland_921P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863578e7 | MD_921P | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863579e7 | Route_921P_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863581e7 | Maryland_Route_921R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863582e7 | Maryland_State_Highway_921R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863583e7 | Maryland_State_Route_921R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863585e7 | Maryland_921R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863586e7 | MD_921R | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863587e7 | Peter_Antonovich_Devier | 1.0 | 1.0 | 73.0 | wikitext | NULL |
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| 6.8863598e7 | Maryland_State_Highway_922E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.88636e7 | Maryland_State_Route_922E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863602e7 | Maryland_922E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863604e7 | MD_922E | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863606e7 | Route_922E_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863607e7 | Maryland_State_Highway_927A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863608e7 | Maryland_State_Route_927A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863609e7 | Edward_Pugh | 0.0 | 0.0 | 342.0 | wikitext | NULL |
| 6.8863612e7 | Maryland_927A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863615e7 | MD_927A | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863616e7 | Route_927A_(Maryland) | 1.0 | 1.0 | 89.0 | wikitext | NULL |
| 6.8863621e7 | Alphonso_Cox | 0.0 | 0.0 | 2254.0 | wikitext | NULL |
| 6.8863623e7 | Melvin_Coleman | 0.0 | 0.0 | 2363.0 | wikitext | NULL |
| 6.8863631e7 | Harry_Catto | 0.0 | 0.0 | 2032.0 | wikitext | NULL |
| 6.8863635e7 | Dallas_Carter_(baseball) | 0.0 | 0.0 | 2072.0 | wikitext | NULL |
| 6.8863638e7 | Nana_Kwame_Ampadu | 1.0 | 1.0 | 25.0 | wikitext | NULL |
| 6.8863648e7 | Catch_Me_(Cliff_Richard_song) | 1.0 | 1.0 | 94.0 | wikitext | NULL |
| 6.8863659e7 | Grete_Wold | 0.0 | 0.0 | 1826.0 | wikitext | NULL |
| 6.8863664e7 | Dell_Boomi | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863678e7 | Rafael_Pinheiro | 1.0 | 1.0 | 37.0 | wikitext | NULL |
| 6.886368e7 | Nina_Skorupska | 0.0 | 0.0 | 6465.0 | wikitext | NULL |
| 6.8863685e7 | Hira_Umer | 0.0 | 0.0 | 4062.0 | wikitext | NULL |
| 6.886369e7 | J._Harper_Smith_Mansion | 0.0 | 0.0 | 2848.0 | wikitext | NULL |
| 6.8863694e7 | George_Washington_Albright | 1.0 | 1.0 | 32.0 | wikitext | NULL |
| 6.8863698e7 | Edward_Römer | 0.0 | 1.0 | 205.0 | wikitext | NULL |
| 6.8863699e7 | Edward_Romer | 1.0 | 1.0 | 26.0 | wikitext | NULL |
| 6.8863714e7 | WHOOP_(company) | 0.0 | 0.0 | 16113.0 | wikitext | NULL |
| 6.8863722e7 | 2021_World_Wrestling_Championships_–_Men's_freestyle_74_kg | 0.0 | 0.0 | 9078.0 | wikitext | NULL |
| 6.8863729e7 | Block_E_(Minneapolis) | 1.0 | 1.0 | 79.0 | wikitext | NULL |
| 6.8863732e7 | New_Haven_(CDP),_Vermont | 0.0 | 1.0 | 3416.0 | wikitext | NULL |
| 6.8863736e7 | Pat_Walker_(philanthropist) | 0.0 | 0.0 | 6376.0 | wikitext | NULL |
| 6.8863741e7 | Nancy_Berliner | 0.0 | 0.0 | 4675.0 | wikitext | NULL |
| 6.8863743e7 | Lemme_Find_Out | 1.0 | 1.0 | 45.0 | wikitext | NULL |
| 6.8863755e7 | Harbin_Songbei_Yiteng_F.C. | 1.0 | 1.0 | 41.0 | wikitext | NULL |
| 6.8863762e7 | 2021_Asian_Table_Tennis_Championships_–_Men's_singles | 0.0 | 0.0 | 49278.0 | wikitext | NULL |
| 6.8863775e7 | Nelson_Gill | 0.0 | 0.0 | 3588.0 | wikitext | NULL |
| 6.8863781e7 | 2021_Asian_Table_Tennis_Championships_–_Women's_singles | 0.0 | 0.0 | 34043.0 | wikitext | NULL |
| 6.8863784e7 | South_Lincoln,_Vermont | 0.0 | 1.0 | 3535.0 | wikitext | NULL |
| 6.8863786e7 | Missouri_Auditor | 1.0 | 1.0 | 38.0 | wikitext | NULL |
| 6.8863794e7 | South_Lincoln | 1.0 | 1.0 | 36.0 | wikitext | NULL |
| 6.8863799e7 | Neverland_II | 1.0 | 1.0 | 50.0 | wikitext | NULL |
| 6.8863806e7 | Never_Land_II | 1.0 | 1.0 | 50.0 | wikitext | NULL |
| 6.886382e7 | 2021_Asian_Table_Tennis_Championships_–_Men's_doubles | 0.0 | 0.0 | 26706.0 | wikitext | NULL |
| 6.8863833e7 | Veliko_Rujište | 1.0 | 1.0 | 22.0 | wikitext | NULL |
| 6.8863839e7 | 2021_Asian_Table_Tennis_Championships_–_Women's_doubles | 0.0 | 0.0 | 19644.0 | wikitext | NULL |
| 6.8863843e7 | W246DT | 1.0 | 1.0 | 18.0 | wikitext | NULL |
| 6.8863852e7 | Markus_Schagerl | 0.0 | 0.0 | 6084.0 | wikitext | NULL |
| 6.8863859e7 | Grau-du-Roi | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.886386e7 | Ctenacanthida | 1.0 | 1.0 | 59.0 | wikitext | NULL |
| 6.8863862e7 | Delfina_Entrecanales | 0.0 | 0.0 | 17427.0 | wikitext | NULL |
| 6.8863864e7 | Mulholland_Drive_(Eyedress_Album) | 1.0 | 1.0 | 85.0 | wikitext | NULL |
| 6.8863872e7 | 2021_Asian_Table_Tennis_Championships_–_Mixed_doubles | 0.0 | 0.0 | 20779.0 | wikitext | NULL |
| 6.8863874e7 | Nuutti_Lintamo | 0.0 | 0.0 | 2384.0 | wikitext | NULL |
| 6.8863877e7 | Nuuti_Lintamo | 1.0 | 1.0 | 27.0 | wikitext | NULL |
| 6.8863884e7 | 王八盒子 | 1.0 | 1.0 | 61.0 | wikitext | NULL |
| 6.8863892e7 | King_Cobra_(DC_Comics) | 1.0 | 1.0 | 56.0 | wikitext | NULL |
| 6.8863896e7 | Sulo_Salo | 0.0 | 1.0 | 1625.0 | wikitext | NULL |
| 6.8863897e7 | Want_It_All_(Burna_Boy_song) | 1.0 | 1.0 | 31.0 | wikitext | NULL |
| 6.88639e7 | 歪把子 | 1.0 | 1.0 | 74.0 | wikitext | NULL |
| 6.8863908e7 | Want_It_All_(Burna_Boy_and_Polo_G_song) | 1.0 | 1.0 | 31.0 | wikitext | NULL |
| 6.886392e7 | Mpho_Moerane | 0.0 | 0.0 | 8359.0 | wikitext | NULL |
| 6.8863923e7 | Lauri_Taipale | 0.0 | 1.0 | 1652.0 | wikitext | NULL |
| 6.8863929e7 | Tom_Hutchinson_(disambiguation) | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.886393e7 | Tom_Hutchison_(disambiguation) | 1.0 | 1.0 | 29.0 | wikitext | NULL |
| 6.8863937e7 | Paavo_Virtanen | 0.0 | 0.0 | 2342.0 | wikitext | NULL |
| 6.886394e7 | José_Roberto_Figueroa | 1.0 | 1.0 | 88.0 | wikitext | NULL |
| 6.8863954e7 | Erick_Fú_Lanza | 1.0 | 1.0 | 70.0 | wikitext | NULL |
| 6.8863962e7 | Tim_Hutchinson_(disambiguation) | 1.0 | 1.0 | 31.0 | wikitext | NULL |
| 6.8863963e7 | Timothy_Hutchinson_(disambiguation) | 1.0 | 0.0 | 59.0 | wikitext | NULL |
| 6.8863972e7 | APM_line_(Guangzhou_Metro) | 1.0 | 1.0 | 61.0 | wikitext | NULL |
| 6.886398e7 | APM_Line_(Guangzhou_Metro) | 1.0 | 1.0 | 61.0 | wikitext | NULL |
| 6.8863985e7 | Matteo_Calamai | 0.0 | 0.0 | 5518.0 | wikitext | NULL |
| 6.8863995e7 | Sadau_Por.Pisitchet | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8863999e7 | Oğuzhan_Asiltürk | 0.0 | 0.0 | 9187.0 | wikitext | NULL |
| 6.8864009e7 | Suadao_Por.Pisitchet | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8864037e7 | Fife_Witches_Trail | 0.0 | 0.0 | 6024.0 | wikitext | NULL |
| 6.886404e7 | Sangharsh_aur_Vijay | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8864042e7 | Mark_Tildesley_(production_designer) | 0.0 | 0.0 | 3361.0 | wikitext | NULL |
| 6.886405e7 | Gët_Busy | 1.0 | 1.0 | 18.0 | wikitext | NULL |
| 6.8864052e7 | Islamic_Emirate_of_Afghanistan_(2021–present) | 1.0 | 1.0 | 25.0 | wikitext | NULL |
| 6.8864061e7 | Get_Busy_(Yeat_song) | 1.0 | 1.0 | 18.0 | wikitext | NULL |
| 6.8864063e7 | Istituto_Nazionale_di_Fisica_Nucleara | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 6.8864069e7 | Up_2_Me | 1.0 | 0.0 | 22.0 | wikitext | NULL |
| 6.8864075e7 | Thomas_Wainwright_(Stoke_footballer) | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8864084e7 | Fiona_Hukula | 0.0 | 0.0 | 4511.0 | wikitext | NULL |
| 6.8864106e7 | Andrea_Domenico_Di_Liberto | 1.0 | 1.0 | 31.0 | wikitext | NULL |
| 6.8864111e7 | USPS_Board_of_Directors | 1.0 | 1.0 | 68.0 | wikitext | NULL |
| 6.8864121e7 | Dick_Clark_Architecture | 1.0 | 1.0 | 84.0 | wikitext | NULL |
| 6.8864122e7 | Fall_Braun | 1.0 | 1.0 | 71.0 | wikitext | NULL |
| 6.8864129e7 | Pankeyevo | 0.0 | 0.0 | 7285.0 | wikitext | NULL |
| 6.8864137e7 | Richard_Yarde-Buller,_4th_Baron_Churston | 0.0 | 0.0 | 10052.0 | wikitext | NULL |
| 6.8864141e7 | Private_and_public_schools_in_China | 1.0 | 1.0 | 75.0 | wikitext | NULL |
| 6.8864146e7 | Richard_Francis_Roger_Yarde-Buller,_4th_Baron_Churston | 1.0 | 1.0 | 54.0 | wikitext | NULL |
| 6.8864151e7 | La_Sepultura_Biosphere_Reserve | 0.0 | 0.0 | 5373.0 | wikitext | NULL |
| 6.8864152e7 | 1982_Montana_State_Bobcats_football_team | 0.0 | 0.0 | 6314.0 | wikitext | NULL |
| 6.8864154e7 | Harper's_Bazaar_Arabia | 1.0 | 1.0 | 54.0 | wikitext | NULL |
| 6.8864155e7 | Hadi_Manafi | 0.0 | 0.0 | 1721.0 | wikitext | NULL |
| 6.8864157e7 | Star_Engine | 1.0 | 1.0 | 73.0 | wikitext | NULL |
| 6.886418e7 | Our_Young_Man | 0.0 | 0.0 | 2541.0 | wikitext | NULL |
| 6.8864181e7 | 2021-22_in_Bangladeshi_Football | 1.0 | 1.0 | 214.0 | wikitext | NULL |
| 6.8864183e7 | 2021-22_Kalamata_F.C._season | 1.0 | 1.0 | 205.0 | wikitext | NULL |
| 6.8864186e7 | Chris_Conn-Clarke | 0.0 | 0.0 | 11232.0 | wikitext | NULL |
| 6.8864188e7 | 2021_UCI_Road_World_Championships_-_Men's_under-23_time_trial | 1.0 | 1.0 | 304.0 | wikitext | NULL |
| 6.886419e7 | 2021-22_EuroLeague_Regular_Season | 1.0 | 1.0 | 220.0 | wikitext | NULL |
| 6.8864191e7 | MFG_-_Austria_People_-_Freedom_-_Fundamental_Rights | 1.0 | 0.0 | 368.0 | wikitext | NULL |
| 6.8864193e7 | Islamic_Emirate_of_Afghanistan_(2021-present) | 1.0 | 1.0 | 300.0 | wikitext | NULL |
| 6.8864194e7 | 2021-2022_Kalamata_F.C._season | 1.0 | 1.0 | 274.0 | wikitext | NULL |
| 6.8864195e7 | SLFL | 1.0 | 1.0 | 37.0 | wikitext | NULL |
| 6.8864198e7 | 2021_Asian_Table_Tennis_Championships_-_Women's_singles | 1.0 | 1.0 | 286.0 | wikitext | NULL |
| 6.88642e7 | 2021–22_Ligue_Magnus_season | 0.0 | 0.0 | 24260.0 | wikitext | NULL |
| 6.8864202e7 | Kick_II | 0.0 | 0.0 | 17135.0 | wikitext | NULL |
| 6.8864206e7 | 2021_Asian_Table_Tennis_Championships_-_Mixed_doubles | 1.0 | 1.0 | 280.0 | wikitext | NULL |
| 6.8864208e7 | 2021_Asian_Table_Tennis_Championships_-_Men's_singles | 1.0 | 1.0 | 280.0 | wikitext | NULL |
| 6.886421e7 | Armenian_Weightlifting_Federation | 0.0 | 0.0 | 2201.0 | wikitext | NULL |
| 6.8864218e7 | Amine_Linganzi_Koumba | 1.0 | 1.0 | 28.0 | wikitext | NULL |
| 6.8864223e7 | Charlton_Abbots | 0.0 | 0.0 | 3057.0 | wikitext | NULL |
| 6.8864224e7 | Kathleen_Krekels | 0.0 | 0.0 | 1380.0 | wikitext | NULL |
| 6.8864227e7 | Piecemeal_(cyborg) | 1.0 | 1.0 | 59.0 | wikitext | NULL |
| 6.8864228e7 | 2021_Asian_Table_Tennis_Championships_-_Men's_doubles | 1.0 | 1.0 | 280.0 | wikitext | NULL |
| 6.8864231e7 | 2021_World_Wrestling_Championships_-_Men's_freestyle_125_kg | 1.0 | 1.0 | 298.0 | wikitext | NULL |
| 6.8864232e7 | 2021_Asian_Table_Tennis_Championships_-_Women's_doubles | 1.0 | 1.0 | 286.0 | wikitext | NULL |
| 6.8864233e7 | Voldemars_Irbe | 1.0 | 1.0 | 79.0 | wikitext | NULL |
| 6.8864234e7 | 2021_World_Wrestling_Championships_-_Men's_freestyle_86_kg | 1.0 | 1.0 | 295.0 | wikitext | NULL |
| 6.8864236e7 | 2021-22_EML_season | 1.0 | 1.0 | 175.0 | wikitext | NULL |
| 6.8864237e7 | 2021_World_Wrestling_Championships_-_Men's_freestyle_74_kg | 1.0 | 1.0 | 295.0 | wikitext | NULL |
| 6.8864239e7 | 2021-22_Ligue_Magnus_season | 1.0 | 1.0 | 202.0 | wikitext | NULL |
| 6.8864249e7 | Avicennia_resinifera | 1.0 | 1.0 | 30.0 | wikitext | NULL |
| 6.8864253e7 | Suzanne_Anderson | 0.0 | 0.0 | 5511.0 | wikitext | NULL |
| 6.8864254e7 | Evgheni_Gorodețchi | 0.0 | 0.0 | 2587.0 | wikitext | NULL |
| 6.8864259e7 | Charlie_Hancock | 0.0 | 0.0 | 2114.0 | wikitext | NULL |
| 6.8864261e7 | Hadamard_test | 1.0 | 1.0 | 49.0 | wikitext | NULL |
| 6.8864264e7 | Fred_Hicks_(baseball) | 0.0 | 0.0 | 1879.0 | wikitext | NULL |
| 6.8864266e7 | Elbert_Hall | 0.0 | 0.0 | 2025.0 | wikitext | NULL |
| 6.8864272e7 | Infrastructure_as_Software | 1.0 | 0.0 | 35.0 | wikitext | NULL |
| 6.8864294e7 | Daniel_Ben_Murphy | 1.0 | 1.0 | 50.0 | wikitext | NULL |
| 6.8864297e7 | Rosa_'Jens_Munk' | 0.0 | 0.0 | 5435.0 | wikitext | NULL |
| 6.8864328e7 | Glycan_nomenclature | 0.0 | 0.0 | 20614.0 | wikitext | NULL |
| 6.886433e7 | 2021_Qualico_Mixed_Doubles_Classic | 0.0 | 0.0 | 30698.0 | wikitext | NULL |
| 6.8864336e7 | \"Granatieri_di_Sardegna\" | 1.0 | 1.0 | 57.0 | wikitext | NULL |
| 6.8864342e7 | Luchy_Donalds | 0.0 | 0.0 | 4976.0 | wikitext | NULL |
| 6.8864344e7 | Manuela_Van_Werde | 0.0 | 0.0 | 2807.0 | wikitext | NULL |
| 6.8864345e7 | List_of_editiones_principes_in_languages_other_than_Latin_or_Greek | 0.0 | 0.0 | 24846.0 | wikitext | NULL |
| 6.8864346e7 | Silver_in_the_money_system | 1.0 | 1.0 | 25.0 | wikitext | NULL |
| 6.8864355e7 | Fjellanger_Widerøe | 1.0 | 0.0 | 72.0 | wikitext | NULL |
| 6.886436e7 | Price_ratio_between_gold_and_silver | 1.0 | 0.0 | 37.0 | wikitext | NULL |
| 6.8864361e7 | Cory_Johnson_(basketball,_born_1988) | 1.0 | 1.0 | 86.0 | wikitext | NULL |
| 6.8864369e7 | No._2953_Squadron_RAF_Regiment | 1.0 | 1.0 | 104.0 | wikitext | NULL |
| 6.8864376e7 | Speculative_investment | 1.0 | 1.0 | 25.0 | wikitext | NULL |
| 6.8864379e7 | Mchawcha | 0.0 | 0.0 | 1157.0 | wikitext | NULL |
| 6.8864383e7 | Souren_Melikian | 0.0 | 0.0 | 8104.0 | wikitext | NULL |
| 6.8864384e7 | Cody_White | 0.0 | 0.0 | 264.0 | wikitext | NULL |
| 6.8864386e7 | Freethinkers_of_America | 1.0 | 1.0 | 34.0 | wikitext | NULL |
| 6.8864396e7 | Consolidation_of_wealth | 1.0 | 1.0 | 36.0 | wikitext | NULL |
| 6.8864397e7 | Eugen_Gorodețchi | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8864398e7 | Cecil_Johnson_(baseball) | 0.0 | 0.0 | 2277.0 | wikitext | NULL |
| 6.88644e7 | Chris_Naggar | 0.0 | 0.0 | 6727.0 | wikitext | NULL |
| 6.8864402e7 | Sonny_Boy_Jeffries | 0.0 | 0.0 | 2068.0 | wikitext | NULL |
| 6.8864404e7 | Cody_Thompson | 0.0 | 0.0 | 132.0 | wikitext | NULL |
| 6.8864406e7 | Evgheni_Gorodeţchi | 1.0 | 1.0 | 33.0 | wikitext | NULL |
| 6.8864413e7 | 36_officers_problem | 1.0 | 1.0 | 75.0 | wikitext | NULL |
val rowsToSave = spark.sql("SELECT page_id, page_title, page_is_redirect, page_is_new AS has_been_edited, page_len, page_content_model, page_lang FROM pages WHERE (page_id IS NOT NULL) AND (page_namespace = 0) AND (page_title IS NOT NULL) AND (_corrupt_record IS NULL)")
rowsToSave.write.saveAsTable("enwiki_page")
rowsToSave: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 5 more fields]
Loading of the Wikipedia data
This is very nearly just a copy of the 02 notebook that loaded the pages.
As a first step, we download the .sql file:
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
FileUtils.copyURLToFile(new URL("https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pagelinks.sql.gz"), new File("/tmp/enwiki-latest-pagelinks.sql.gz"))
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
Having done this, we first unzip the file, and then move the file from local storage to the DBFS:
gzip -d /tmp/enwiki-latest-pagelinks.sql.gz
mv file:/tmp/enwiki-latest-pagelinks.sql /enwiki-latest-pagelinks.sql
res1: Boolean = true
Having gotten the data onto the DBFS, we can now read it into Spark:
val rawSQLdump = spark.read.textFile("/enwiki-latest-pagelinks.sql")
rawSQLdump: org.apache.spark.sql.Dataset[String] = [value: string]
The first forty lines are setting up the database, then we get a lot of very long INSERT INTO lines with many many entries being inserted.
println(rawSQLdump.take(40).mkString("\n"))
-- MySQL dump 10.19 Distrib 10.3.34-MariaDB, for debian-linux-gnu (x86_64)
--
-- Host: db1106 Database: enwiki
-- ------------------------------------------------------
-- Server version 10.4.25-MariaDB-log
/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;
/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;
/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;
/*!40101 SET NAMES utf8mb4 */;
/*!40103 SET @OLD_TIME_ZONE=@@TIME_ZONE */;
/*!40103 SET TIME_ZONE='+00:00' */;
/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;
/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;
/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;
/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;
--
-- Table structure for table `pagelinks`
--
DROP TABLE IF EXISTS `pagelinks`;
/*!40101 SET @saved_cs_client = @@character_set_client */;
/*!40101 SET character_set_client = utf8 */;
CREATE TABLE `pagelinks` (
`pl_from` int(8) unsigned NOT NULL DEFAULT 0,
`pl_namespace` int(11) NOT NULL DEFAULT 0,
`pl_title` varbinary(255) NOT NULL DEFAULT '',
`pl_from_namespace` int(11) NOT NULL DEFAULT 0,
PRIMARY KEY (`pl_from`,`pl_namespace`,`pl_title`),
KEY `pl_namespace` (`pl_namespace`,`pl_title`,`pl_from`),
KEY `pl_backlinks_namespace` (`pl_from_namespace`,`pl_namespace`,`pl_title`,`pl_from`)
) ENGINE=InnoDB DEFAULT CHARSET=binary ROW_FORMAT=COMPRESSED;
/*!40101 SET character_set_client = @saved_cs_client */;
--
-- Dumping data for table `pagelinks`
--
/*!40000 ALTER TABLE `pagelinks` DISABLE KEYS */;
The remaining rows look something like this, except much much longer:
println(rawSQLdump.take(41)(40).substring(0,106) + ",...," + rawSQLdump.take(41)(40).substring(rawSQLdump.take(41)(40).length()-42,rawSQLdump.take(41)(40).length()))
INSERT INTO `pagelinks` VALUES (586,0,'!',0),(4748,0,'!',0),(9773,0,'!',0),(15019,0,'!',0),(15154,0,'!',0),...,(64264744,0,'\'Abd_al-Haqq_al-Dehlawi',0);
Next up, let us strip out the INSERT INTO bit and the initial and final parentheses, then split at each ),(, so that we get each entry as its own string.
val pageDataRows = rawSQLdump.filter(x => x.startsWith("INSERT INTO"))
.flatMap(x => x.substring(32, x.length()-2).split("""\),\("""))
pageDataRows: org.apache.spark.sql.Dataset[String] = [value: string]
So now our data looks like this:
println(pageDataRows.take(10).mkString("\n"))
586,0,'!',0
4748,0,'!',0
9773,0,'!',0
15019,0,'!',0
15154,0,'!',0
25213,0,'!',0
73634,0,'!',0
193891,0,'!',0
410443,0,'!',0
533706,0,'!',0
With a heckuva lot of rows - 1.48 billion, to be particular.
pageDataRows.count()
The above looks a whole lot like a CSV file, doesn't it? Let's write it to file as such. Note that we write it as text instead of as CSV because our data is in the format of a single string per row.
pageDataRows.toDF().write.mode("overwrite").text("/WikipediaData/enwiki-pagelinks.csv")
Now we want to read this back in, but with the right schema and column names and so on. So we start by creating the schema. In order to be sure that all the rows got parsed correctly, we add an extra column named _corrupt_record, which will get the raw CSV text whenever it couldn't be parsed right, and otherwise be set to NULL.
import org.apache.spark.sql.types._
// Start by creating a case class of a row entry:
case class WikiPageLink(pl_from:Int,
pl_namespace:Int,
pl_title:String,
pl_from_namespace:Int)
// then we generate a schema object from the case class: (code copypasted from here: https://sparkbyexamples.com/spark/convert-case-class-to-spark-schema/)
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
val pageSchema = ScalaReflection.schemaFor[WikiPageLink].dataType.asInstanceOf[StructType].add("_corrupt_record", StringType, true)
import org.apache.spark.sql.types._
defined class WikiPageLink
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
pageSchema: org.apache.spark.sql.types.StructType = StructType(StructField(pl_from,IntegerType,false),StructField(pl_namespace,IntegerType,false),StructField(pl_title,StringType,true),StructField(pl_from_namespace,IntegerType,false),StructField(_corrupt_record,StringType,true))
Then we read it back in with the schema we just created:
val readFromCSV = spark.read
.options(Map("quote" -> "'", "mode" -> "PERMISSIVE", "columnNameOfCorruptRecord" -> "_corrupt_record"))
.schema(pageSchema)
.csv("/WikipediaData/enwiki-pagelinks.csv")
readFromCSV: org.apache.spark.sql.DataFrame = [pl_from: int, pl_namespace: int ... 3 more fields]
Let's have a look at what we just created:
display(readFromCSV)
| pl_from | pl_namespace | pl_title | pl_from_namespace |
|---|---|---|---|
| 6.2036177e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2036214e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2044245e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2044286e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2044799e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2044969e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2045091e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2045917e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2046017e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2046144e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2046198e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2046286e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2050182e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2050468e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2052923e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2053001e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2053086e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2053582e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2054277e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2054373e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.205447e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2054863e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2054926e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.205505e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2055493e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2055914e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2061079e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2065101e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2069857e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2070489e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2070509e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2070906e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.207097e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.20722e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2072539e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2072666e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2074281e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2074669e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2074726e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2080468e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2081011e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2081481e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2081692e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2081905e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2083035e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2085589e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2086399e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2086635e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.208665e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2089168e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2089206e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.20939e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2095774e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2098617e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2098663e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2098696e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2098732e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.209879e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2098856e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2098946e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099048e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099056e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099092e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099425e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099468e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099557e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099599e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.209967e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099703e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099713e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2099782e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.209993e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2100389e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2101217e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2101317e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2103796e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2104062e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2104545e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.210551e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2111121e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2111432e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2111607e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.211293e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2112989e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2113555e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2113799e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2113972e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2114422e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2114573e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2114632e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2114692e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.211474e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2114771e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2114897e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2115054e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2115672e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.2117407e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
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| 6.5298314e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5303719e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5308354e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5365394e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5366797e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5369386e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5372016e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.541149e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5420509e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5421334e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
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| 6.5428361e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5428841e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.542951e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5433482e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5434624e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5436186e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5437285e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5437888e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5438014e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5438434e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5442824e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5442948e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.544437e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5445079e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5445322e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5445549e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5485445e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5498384e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5503167e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5506379e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5513411e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.552545e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.553197e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5534025e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5551324e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5551493e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5577638e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5636571e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5638115e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5657305e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
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| 6.5672345e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5672874e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5701318e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5701504e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5738593e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5738822e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5738971e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5739085e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5747567e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5766576e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5768071e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5777999e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5801215e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5817875e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5836178e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.584045e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5856791e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5876171e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5901597e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5905107e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5912095e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5919596e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5922111e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5922411e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5979588e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.59799e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.5979982e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
| 6.6026034e7 | 0.0 | National_Register_of_Historic_Places_listings_in_Louisiana | 0.0 |
Now, let us check that we have no corrupted records:
readFromCSV.createOrReplaceTempView("pagelinks")
SELECT * FROM pagelinks WHERE _corrupt_record IS NOT NULL
| pl_from | pl_namespace | pl_title | pl_from_namespace | _corrupt_record |
|---|---|---|---|---|
| 3.9650412e7 | 4.0 | Articles_for_creation/SANCHAR_NIGAM_ASSOCIATION_OF_TELECOM_TECHNICAL_ASSISTANTS_(SNATTA | null | 39650412,4,'Articles_for_creation/SANCHAR_NIGAM_ASSOCIATION_OF_TELECOM_TECHNICAL_ASSISTANTS_(SNATTA |
| null | 4.0 | null | null | Regd.No._–_DRFS/152)',4 |
| 3.9650412e7 | 5.0 | Articles_for_creation/SANCHAR_NIGAM_ASSOCIATION_OF_TELECOM_TECHNICAL_ASSISTANTS_(SNATTA | null | 39650412,5,'Articles_for_creation/SANCHAR_NIGAM_ASSOCIATION_OF_TELECOM_TECHNICAL_ASSISTANTS_(SNATTA |
| null | 4.0 | null | null | Regd.No._–_DRFS/152)',4 |
| 3.9651844e7 | 5.0 | Articles_for_creation/SANCHAR_NIGAM_ASSOCIATION_OF_TELECOM_TECHNICAL_ASSISTANTS_(SNATTA | null | 39651844,5,'Articles_for_creation/SANCHAR_NIGAM_ASSOCIATION_OF_TELECOM_TECHNICAL_ASSISTANTS_(SNATTA |
| null | 3.0 | null | null | Regd.No._–_DRFS/152)',3 |
| 4.0435962e7 | 0.0 | Bhongir_(Lok_Sabha_constituency | null | 40435962,0,'Bhongir_(Lok_Sabha_constituency |
| null | 2.0 | null | null | Assembly_constituency)',2 |
| 4.3661887e7 | 0.0 | Bhongir_(Lok_Sabha_constituency | null | 43661887,0,'Bhongir_(Lok_Sabha_constituency |
| null | 2.0 | null | null | Assembly_constituency)',2 |
| 2.2480556e7 | 1.0 | Jessica_Smith_(12 | null | 22480556,1,'Jessica_Smith_(12 |
| null | 3.0 | null | null | backing_singer/actress)',3 |
| 4.3712571e7 | 2.0 | Enlisted_USAF_(ret | null | 43712571,2,'Enlisted_USAF_(ret |
| null | 2.0 | null | null | DAV)',2 |
| 2.2480556e7 | 0.0 | Jessica_Smith_(12 | null | 22480556,0,'Jessica_Smith_(12 |
| null | 3.0 | null | null | backing_singer/actress)',3 |
On the scale of 1.48 billion rows, having sixteen bad rows is basically the same as zero. We've only lost eight edges in our graph, and none of them are actually between main-namespace articles, only between talk pages and files and such.
So, let us take this data, remove the corrupt rows and rows with data we don't care about, and save the data to Delta Lake. Only rows with plnamespace and plfrom_namespace both equal to zero are links between main Wikipedia articles - the other namespaces are things like user talk pages or image pages and so on.
SELECT pl_from, pl_title FROM pagelinks WHERE (pl_from IS NOT NULL) AND (pl_namespace = 0) AND (pl_title IS NOT NULL) AND (pl_from_namespace = 0) AND (_corrupt_record IS NULL)
| pl_from | pl_title |
|---|---|
| 6.2036177e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2036214e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2044245e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2044286e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2044799e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2044969e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2045091e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2045917e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2046017e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2046144e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2046198e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2046286e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2050182e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2050468e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2052923e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2053001e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2053086e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2053582e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2054277e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2054373e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.205447e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2054863e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2054926e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.205505e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2055493e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2055914e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2061079e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2065101e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2069857e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2070489e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2070509e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2070906e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.207097e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.20722e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2072539e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2072666e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2074281e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2074669e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2074726e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2080468e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
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| 6.2081481e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2081692e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2081905e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2083035e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2085589e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2086399e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2086635e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.208665e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2089168e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2089206e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.20939e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2095774e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2098617e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2098663e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2098696e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2098732e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.209879e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2098856e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2098946e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099048e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099056e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099092e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099425e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099468e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099557e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099599e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.209967e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099703e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099713e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2099782e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.209993e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2100389e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2101217e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2101317e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2103796e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2104062e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2104545e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
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| 6.2111121e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2111432e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2111607e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.211293e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2112989e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2113555e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2113799e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2113972e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2114422e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2114573e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2114632e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2114692e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.211474e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2114771e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2114897e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2115054e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2115672e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2117407e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2117442e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.21217e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2123268e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2131095e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.213484e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2134949e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2135004e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2135333e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.213541e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2135858e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2135931e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2135984e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2136991e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2137571e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2137796e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2140651e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2140938e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2141518e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2143495e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2143563e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2143817e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2143889e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2144019e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2145232e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.214547e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.214645e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.214948e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2150355e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2151715e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.215186e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2151999e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2152086e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2152256e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2152322e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2152384e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2152442e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2154164e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2155835e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.215897e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2159682e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2159752e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2160675e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.2161033e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
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| 6.5243131e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5244864e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5245521e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.524588e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5298314e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5303719e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5308354e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5365394e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5366797e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5369386e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5372016e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.541149e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5420509e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5421334e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5425845e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5426861e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5428361e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5428841e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.542951e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5433482e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5434624e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5436186e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5437285e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5437888e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5438014e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5438434e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5442824e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5442948e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.544437e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5445079e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5445322e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5445549e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5485445e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5498384e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5503167e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5506379e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5513411e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.552545e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.553197e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5534025e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5551324e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5551493e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5577638e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5636571e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5638115e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5657305e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5668538e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5672345e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5672874e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5701318e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5701504e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5738593e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5738822e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5738971e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5739085e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5747567e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5766576e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5768071e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5777999e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5801215e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5817875e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5836178e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.584045e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5856791e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5876171e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5901597e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5905107e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5912095e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5919596e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5922111e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5922411e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5979588e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.59799e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.5979982e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
| 6.6026034e7 | National_Register_of_Historic_Places_listings_in_Louisiana |
val rowsToSave = spark.sql("SELECT pl_from, pl_title FROM pagelinks WHERE (pl_from IS NOT NULL) AND (pl_namespace = 0) AND (pl_title IS NOT NULL) AND (pl_from_namespace = 0) AND (_corrupt_record IS NULL)")
rowsToSave.write.saveAsTable("enwiki_pagelinks")
rowsToSave: org.apache.spark.sql.DataFrame = [pl_from: int, pl_title: string]
Loading of the Wikipedia data
This is very nearly just a copy of the 02 notebook that loaded the pages.
As a first step, we download the .sql file:
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
FileUtils.copyURLToFile(new URL("https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-categorylinks.sql.gz"), new File("/tmp/enwiki-latest-categorylinks.sql.gz"))
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
Having done this, we first unzip the file, and then move the file from local storage to the DBFS:
gzip -d /tmp/enwiki-latest-categorylinks.sql.gz
mv file:/tmp/enwiki-latest-categorylinks.sql /enwiki-latest-categorylinks.sql
res1: Boolean = true
Having gotten the data onto the DBFS, we can now read it into Spark:
val rawSQLdump = spark.read.textFile("/enwiki-latest-categorylinks.sql")
rawSQLdump: org.apache.spark.sql.Dataset[String] = [value: string]
The first fortyfour lines are setting up the database, then we get a lot of very long INSERT INTO lines with many many entries being inserted.
println(rawSQLdump.take(44).mkString("\n"))
-- MySQL dump 10.19 Distrib 10.3.34-MariaDB, for debian-linux-gnu (x86_64)
--
-- Host: db1106 Database: enwiki
-- ------------------------------------------------------
-- Server version 10.4.25-MariaDB-log
/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;
/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;
/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;
/*!40101 SET NAMES utf8mb4 */;
/*!40103 SET @OLD_TIME_ZONE=@@TIME_ZONE */;
/*!40103 SET TIME_ZONE='+00:00' */;
/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;
/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;
/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;
/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;
--
-- Table structure for table `categorylinks`
--
DROP TABLE IF EXISTS `categorylinks`;
/*!40101 SET @saved_cs_client = @@character_set_client */;
/*!40101 SET character_set_client = utf8 */;
CREATE TABLE `categorylinks` (
`cl_from` int(8) unsigned NOT NULL DEFAULT 0,
`cl_to` varbinary(255) NOT NULL DEFAULT '',
`cl_sortkey` varbinary(230) NOT NULL DEFAULT '',
`cl_timestamp` timestamp NOT NULL DEFAULT current_timestamp() ON UPDATE current_timestamp(),
`cl_sortkey_prefix` varbinary(255) NOT NULL DEFAULT '',
`cl_collation` varbinary(32) NOT NULL DEFAULT '',
`cl_type` enum('page','subcat','file') NOT NULL DEFAULT 'page',
PRIMARY KEY (`cl_from`,`cl_to`),
KEY `cl_timestamp` (`cl_to`,`cl_timestamp`),
KEY `cl_sortkey` (`cl_to`,`cl_type`,`cl_sortkey`,`cl_from`),
KEY `cl_collation_ext` (`cl_collation`,`cl_to`,`cl_type`,`cl_from`)
) ENGINE=InnoDB DEFAULT CHARSET=binary ROW_FORMAT=COMPRESSED;
/*!40101 SET character_set_client = @saved_cs_client */;
--
-- Dumping data for table `categorylinks`
--
/*!40000 ALTER TABLE `categorylinks` DISABLE KEYS */;
The remaining rows look something like this, except much much longer:
println(rawSQLdump.take(45)(44).substring(0,254) + ",...," + rawSQLdump.take(45)(44).substring(rawSQLdump.take(45)(44).length() - 135, rawSQLdump.take(45)(44).length()))
INSERT INTO `categorylinks` VALUES (10,'Redirects_from_moves','*..2NN:,@2.FBHRP:D6ܽ�','2014-10-26 04:50:23','','uca-default-u-kn','page'),(10,'Redirects_with_old_history','*..2NN:,@2.FBHRP:D6ܽ�','2010-08-26 22:38:36','','uca-default-u-kn','page'),...,(1038,'Wikipedia_articles_needing_clarification_from_November_2017','**L8RN\n� ','2017-11-08 17:52:14','','uca-default-u-kn','page');
Next up, let us strip out the INSERT INTO bit and the initial and final parentheses, then split at each ),(, so that we get each entry as its own string.
val pageDataRows = rawSQLdump.filter(x => x.startsWith("INSERT INTO"))
.flatMap(x => x.substring(36, x.length()-2).split("""\),\("""))
pageDataRows: org.apache.spark.sql.Dataset[String] = [value: string]
So now our data looks like this:
println(pageDataRows.take(10).mkString("\n"))
10,'Redirects_from_moves','*..2NN:,@2.FBHRP:D6ܽ�','2014-10-26 04:50:23','','uca-default-u-kn','page'
10,'Redirects_with_old_history','*..2NN:,@2.FBHRP:D6ܽ�','2010-08-26 22:38:36','','uca-default-u-kn','page'
10,'Unprintworthy_redirects','*..2NN:,@2.FBHRP:D6ܽ�','2010-08-26 22:38:36','','uca-default-u-kn','page'
12,'Anarchism','*D*L.8:NB��','2020-01-23 13:27:44',' ','uca-default-u-kn','page'
12,'Anti-capitalism','*D*L.8:NB\r�','2020-01-23 13:27:44','','uca-default-u-kn','page'
12,'Anti-fascism','*D*L.8:NB\r�','2020-01-23 13:27:44','','uca-default-u-kn','page'
12,'Articles_containing_French-language_text','*D*L.8:NB\r�','2020-01-23 13:27:44','','uca-default-u-kn','page'
12,'Articles_containing_Spanish-language_text','*D*L.8:NB\r�','2020-01-23 13:27:44','','uca-default-u-kn','page'
12,'Articles_prone_to_spam_from_November_2014','*D*L.8:NB\r�','2020-01-23 13:27:44','','uca-default-u-kn','page'
12,'Articles_with_BNE_identifiers','*D*L.8:NB\r�','2021-08-29 20:33:32','','uca-default-u-kn','page'
With quite a lot of rows - 181 million, to be particular.
pageDataRows.count()
res15: Long = 181884985
The above looks a whole lot like a CSV file, doesn't it? Let's write it to file as such. Note that we write it as text instead of as CSV because our data is in the format of a single string per row.
pageDataRows.toDF().write.mode("overwrite").text("/WikipediaData/enwiki-categorylinks.csv")
Now we want to read this back in, but with the right schema and column names and so on. So we start by creating the schema. So we start by creating the schema. In order to be sure that all the rows got parsed correctly, we add an extra column named _corrupt_record, which will get the raw CSV text whenever it couldn't be parsed right, and otherwise be set to NULL.
import org.apache.spark.sql.types._
// Start by creating a case class of a row entry:
case class WikiCategoryLink(cl_from:Int,
cl_to:String,
cl_sortkey:String,
cl_timestamp:String,
cl_sortkey_prefix:String,
cl_collation:String,
cl_type:String)
// then we generate a schema object from the case class: (code copypasted from here: https://sparkbyexamples.com/spark/convert-case-class-to-spark-schema/)
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
val pageSchema = ScalaReflection.schemaFor[WikiCategoryLink].dataType.asInstanceOf[StructType].add("_corrupt_record", StringType, true)
import org.apache.spark.sql.types._
defined class WikiCategoryLink
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
pageSchema: org.apache.spark.sql.types.StructType = StructType(StructField(cl_from,IntegerType,false),StructField(cl_to,StringType,true),StructField(cl_sortkey,StringType,true),StructField(cl_timestamp,StringType,true),StructField(cl_sortkey_prefix,StringType,true),StructField(cl_collation,StringType,true),StructField(cl_type,StringType,true),StructField(_corrupt_record,StringType,true))
Then we read it back in with the schema we just created:
val readFromCSV = spark.read
.options(Map("quote" -> "'", "mode" -> "PERMISSIVE", "columnNameOfCorruptRecord" -> "_corrupt_record"))
.schema(pageSchema)
.csv("/WikipediaData/enwiki-categorylinks.csv")
readFromCSV: org.apache.spark.sql.DataFrame = [cl_from: int, cl_to: string ... 6 more fields]
Let's have a look at what we just created:
display(readFromCSV)
| cl_from | cl_to | cl_sortkey | cl_timestamp | cl_sortkey_prefix | cl_collation | cl_type |
|---|---|---|---|---|---|---|
| 10.0 | Redirects_from_moves | *..2NN:,@2.FBHRP:D6ܽ� | 2014-10-26 04:50:23 | null | uca-default-u-kn | page |
| 10.0 | Redirects_with_old_history | *..2NN:,@2.FBHRP:D6ܽ� | 2010-08-26 22:38:36 | null | uca-default-u-kn | page |
| 10.0 | Unprintworthy_redirects | *..2NN:,@2.FBHRP:D6ܽ� | 2010-08-26 22:38:36 | null | uca-default-u-kn | page |
| 12.0 | Anarchism | *D*L.8:NB�� | 2020-01-23 13:27:44 | uca-default-u-kn | page | |
| 12.0 | Anti-capitalism | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Anti-fascism | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Articles_containing_French-language_text | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Articles_containing_Spanish-language_text | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Articles_prone_to_spam_from_November_2014 | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_BNE_identifiers | *D*L.8:NB\r� | 2021-08-29 20:33:32 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_BNF_identifiers | *D*L.8:NB\r� | 2021-08-29 20:33:32 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_EMU_identifiers | *D*L.8:NB\r� | 2021-08-29 20:33:32 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_FAST_identifiers | *D*L.8:NB\r� | 2022-10-11 09:10:41 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_GND_identifiers | *D*L.8:NB\r� | 2021-08-29 20:33:32 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_HDS_identifiers | *D*L.8:NB\r� | 2021-08-29 20:33:32 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_J9U_identifiers | *D*L.8:NB\r� | 2022-02-21 19:07:42 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_LCCN_identifiers | *D*L.8:NB\r� | 2021-08-29 20:33:32 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_LNB_identifiers | *D*L.8:NB\r� | 2022-10-10 10:08:24 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_NDL_identifiers | *D*L.8:NB\r� | 2022-10-10 09:50:32 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_NKC_identifiers | *D*L.8:NB\r� | 2021-08-29 20:33:32 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_SUDOC_identifiers | *D*L.8:NB\r� | 2022-10-11 08:43:54 | null | uca-default-u-kn | page |
| 12.0 | Articles_with_short_description | *D*L.8:NB\r� | 2020-10-08 06:51:49 | null | uca-default-u-kn | page |
| 12.0 | Economic_ideologies | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Far-left_politics | *D*L.8:NB\r� | 2022-01-28 19:04:36 | null | uca-default-u-kn | page |
| 12.0 | Good_articles | *D*L.8:NB\r� | 2020-10-08 06:51:49 | null | uca-default-u-kn | page |
| 12.0 | Left-wing_politics | *D*L.8:NB\r� | 2020-08-06 12:43:19 | null | uca-default-u-kn | page |
| 12.0 | Libertarian_socialism | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Libertarianism | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Political_culture | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Political_ideologies | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Political_movements | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Short_description_matches_Wikidata | *D*L.8:NB\r� | 2020-10-08 06:51:49 | null | uca-default-u-kn | page |
| 12.0 | Social_theories | *D*L.8:NB\r� | 2020-01-23 13:27:44 | null | uca-default-u-kn | page |
| 12.0 | Socialism | *D*L.8:NB\r� | 2020-11-21 21:31:32 | null | uca-default-u-kn | page |
| 12.0 | Use_British_English_from_August_2021 | *D*L.8:NB\r� | 2021-08-11 06:52:48 | null | uca-default-u-kn | page |
| 12.0 | Use_dmy_dates_from_August_2021 | *D*L.8:NB\r� | 2021-08-11 06:52:48 | null | uca-default-u-kn | page |
| 12.0 | Wikipedia_indefinitely_semi-protected_pages | *D*L.8:NB*D*L.8:NBܽ� | 2021-03-17 03:39:19 | Anarchism | uca-default-u-kn | page |
| 13.0 | Redirects_with_old_history | *468*D:NP*D8:NPFLZܼ�\n | 2007-04-19 22:12:13 | null | uca-default-u-kn | page |
| 13.0 | Unprintworthy_redirects | *468*D:NP*D8:NPFLZܼ�\n | 2006-09-08 04:15:52 | null | uca-default-u-kn | page |
| 14.0 | Redirects_with_old_history | *468*D:NP*D62F6L*H8Zܼ� | 2007-04-19 22:12:13 | null | uca-default-u-kn | page |
| 14.0 | Unprintworthy_redirects | *468*D:NP*D62F6L*H8Zܼ� | 2006-09-08 04:15:36 | null | uca-default-u-kn | page |
| 15.0 | Redirects_with_old_history | *468*D:NP*DH2FH@2ܼ� | 2007-04-19 22:12:13 | null | uca-default-u-kn | page |
| 15.0 | Unprintworthy_redirects | *468*D:NP*DH2FH@2ܼ� | 2006-09-08 04:15:11 | null | uca-default-u-kn | page |
| 18.0 | Redirects_with_old_history | *468*D:NP*D.FBBRD:.*P:FDNܼ� | 2007-04-19 22:12:14 | null | uca-default-u-kn | page |
| 18.0 | Unprintworthy_redirects | *468*D:NP*D.FBBRD:.*P:FDNܼ� | 2006-09-08 04:14:42 | null | uca-default-u-kn | page |
| 19.0 | Redirects_with_old_history | *468*D:NP*DPL*DNHFLP*P:FDNܼ� | 2007-04-19 22:12:14 | null | uca-default-u-kn | page |
| 19.0 | Unprintworthy_redirects | *468*D:NP*DPL*DNHFLP*P:FDNܼ� | 2006-09-08 04:14:07 | null | uca-default-u-kn | page |
| 20.0 | Redirects_from_moves | *468*D:NP*DB:@:P*LZܼ� | 2022-06-14 10:44:25 | null | uca-default-u-kn | page |
| 20.0 | Redirects_with_old_history | *468*D:NP*DB:@:P*LZܼ� | 2007-04-19 22:12:14 | null | uca-default-u-kn | page |
| 20.0 | Unprintworthy_redirects | *468*D:NP*DB:@:P*LZܼ� | 2006-09-08 04:13:27 | null | uca-default-u-kn | page |
| 21.0 | Redirects_with_old_history | *468*D:NP*DPL*DND*P:FD*@:NNR2N\"ܼܺ� | 2007-04-19 22:12:14 | null | uca-default-u-kn | page |
| 21.0 | Unprintworthy_redirects | *468*D:NP*DPL*DND*P:FD*@:NNR2N\"ܼܺ� | 2006-04-01 12:08:42 | null | uca-default-u-kn | page |
| 23.0 | Redirects_with_old_history | *NN:NP:T2P2.8DF@F6Zܾ�\r | 2007-04-19 22:12:14 | null | uca-default-u-kn | page |
| 23.0 | Unprintworthy_redirects | *NN:NP:T2P2.8DF@F6Zܾ�\r | 2006-09-08 04:17:00 | null | uca-default-u-kn | page |
| 24.0 | Redirects_with_old_history | *BF2,F:0P*X*ܿ� | 2007-04-19 22:12:14 | null | uca-default-u-kn | page |
| 24.0 | Unprintworthy_redirects | *BF2,F:0P*X*ܿ� | 2006-09-08 04:17:51 | null | uca-default-u-kn | page |
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| 25.0 | Redirects_from_merges | *RP:NB\n� | 2022-06-25 02:15:53 | null | uca-default-u-kn | page |
| 25.0 | Semi-protected_redirects | *RP:NB\n� | 2022-06-25 02:15:53 | null | uca-default-u-kn | page |
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| 27.0 | Redirects_with_old_history | *@,*D:*8:NPFLZ���\n | 2007-04-19 22:12:14 | null | uca-default-u-kn | page |
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| 39.0 | 1760s_neologisms | *@,20F\n� | 2020-11-05 23:18:57 | null | uca-default-u-kn | page |
| 39.0 | All_articles_with_failed_verification | *@,20F\n� | 2020-01-30 15:59:17 | null | uca-default-u-kn | page |
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| 39.0 | Articles_with_unsourced_statements_from_November_2013 | *@,20F\n� | 2022-05-13 04:12:29 | null | uca-default-u-kn | page |
| 39.0 | CS1_errors:_missing_periodical | *@,20F\n� | 2019-09-03 13:15:11 | null | uca-default-u-kn | page |
| 39.0 | Climate_change_feedbacks | *@,20F\n� | 2020-04-17 09:47:32 | null | uca-default-u-kn | page |
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| 48.0 | Redirects_with_old_history | *,,*0:02N\rܿ� | 2007-04-19 22:12:16 | null | uca-default-u-kn | page |
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| 50.0 | Unprintworthy_redirects | *,,2T:@@24L*D.2ܾ� | 2006-09-08 04:21:11 | null | uca-default-u-kn | page |
| 51.0 | Redirects_with_old_history | *,,2Z ��� | 2007-04-19 22:12:16 | null | uca-default-u-kn | page |
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| 52.0 | Redirects_with_old_history | *,,FP ��� | 2007-04-19 22:12:16 | null | uca-default-u-kn | page |
| 52.0 | Unprintworthy_redirects | *,,FP ��� | 2006-04-01 12:13:02 | null | uca-default-u-kn | page |
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| 60.0 | Redirects_with_old_history | *Z2LNBRN:.HR,@:N8:D6.FBH*DZ����ܽ�\n | 2007-04-19 22:12:41 | null | uca-default-u-kn | page |
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| 128.0 | All_Wikipedia_level-5_vital_articles | *P@*NN8LR6620��� | 2018-06-10 00:04:46 | null | uca-default-u-kn | page |
| 128.0 | All_Wikipedia_vital_articles | *P@*NN8LR6620��� | 2018-06-10 00:04:46 | null | uca-default-u-kn | page |
| 128.0 | All_Wikipedia_vital_articles_in_Art | *P@*NN8LR6620��� | 2018-06-10 00:04:46 | null | uca-default-u-kn | page |
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| 307.0 | Abraham_Lincoln | *,L*8*B@:D.F@D�ܿ�\n | 2016-01-29 11:54:22 | uca-default-u-kn | page | |
| 307.0 | All_Wikipedia_articles_written_in_American_English | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-09-18 18:53:36 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_abolitionists | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-10-12 12:05:55 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_colonization_movement | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2018-12-08 15:18:51 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_lawyers_admitted_to_the_practice_of_law_by_reading_law | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2017-07-15 19:35:02 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_military_personnel_of_the_Indian_Wars | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-04-09 10:49:51 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_militia_officers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-04-09 10:53:02 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_nationalists | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2020-12-19 06:15:56 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_people_of_English_descent | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_political_party_founders | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-05-16 17:00:10 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | American_surveyors | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-09-08 18:06:18 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_BIBSYS_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_BNC_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_BNE_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_BNF_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_BPN_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_CANTICN_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2022-03-09 13:28:31 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_CINII_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_DTBIO_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2022-02-21 19:07:43 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_FAST_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_GND_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_ISNI_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_Internet_Archive_links | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-07-19 23:13:45 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_J9U_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2022-02-21 19:07:43 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_KULTURNAV_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_LCCN_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_LNB_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_LibriVox_links | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-07-19 23:13:45 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_MusicBrainz_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NARA_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NCL_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NDL_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NKC_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NLA_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NLG_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NLK_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NSK_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_NTA_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_PLWABN_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_Project_Gutenberg_links | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-02-16 17:55:37 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_RERO_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_RSL_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_SELIBR_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_SNAC-ID_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_SUDOC_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_Trove_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_ULAN_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_USCongress_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_VIAF_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_VcBA_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_WORLDCATID_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_multiple_identifiers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-29 20:33:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Articles_with_short_description | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-09-18 18:53:36 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Assassinated_heads_of_state | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2022-04-29 20:15:13 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Assassinated_presidents_of_the_United_States | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-04-14 04:36:19 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Burials_at_Oak_Ridge_Cemetery | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | CS1:_Julian–Gregorian_uncertainty | *,L*8*B@:D.F@Dܿ�\n | 2022-08-01 13:53:30 | null | uca-default-u-kn | page |
| 307.0 | CS1_maint:_url-status | *,L*8*B@:D.F@Dܿ�\n | 2022-08-01 13:53:30 | null | uca-default-u-kn | page |
| 307.0 | Candidates_in_the_1860_United_States_presidential_election | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-07-11 08:52:43 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Candidates_in_the_1864_United_States_presidential_election | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2019-07-11 08:53:01 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Good_articles | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-12-31 14:29:27 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Hall_of_Fame_for_Great_Americans_inductees | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Illinois_Central_Railroad_people | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2017-05-25 01:12:25 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Illinois_Republicans | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Illinois_lawyers | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Illinois_postmasters | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-08 23:01:55 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Lincoln_family | *,L*8*B*,L*8*B@:D.F@Dܿܿ�\n | 2016-01-29 11:54:22 | Abraham | uca-default-u-kn | page |
| 307.0 | Male_murder_victims | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-07-08 03:56:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Members_of_the_Illinois_House_of_Representatives | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Pages_using_Sister_project_links_with_hidden_wikidata | 0*,L*8*B@:D.F@D�ܿ�\n | 2020-12-28 20:00:04 | d | uca-default-u-kn | page |
| 307.0 | Pages_using_multiple_image_with_auto_scaled_images | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2020-09-28 05:56:06 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | People_associated_with_the_assassination_of_Abraham_Lincoln | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-08-21 00:38:15 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | People_from_Coles_County,_Illinois | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | People_from_LaRue_County,_Kentucky | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | People_from_Macon_County,_Illinois | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | People_from_Spencer_County,_Indiana | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | People_murdered_in_Washington,_D.C. | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | People_of_Illinois_in_the_American_Civil_War | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | People_with_mood_disorders | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Politicians_from_Springfield,_Illinois | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Presidents_of_the_United_States | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Republican_Party_(United_States)_presidential_nominees | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2016-01-29 11:54:22 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Republican_Party_presidents_of_the_United_States | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-04-14 04:53:44 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Short_description_matches_Wikidata | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2022-10-08 15:51:33 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Union_(American_Civil_War)_political_leaders | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2022-08-04 08:30:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Use_American_English_from_July_2020 | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-12-31 14:29:27 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Use_mdy_dates_from_May_2022 | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2022-05-30 06:21:56 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Webarchive_template_wayback_links | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2021-08-24 02:21:04 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Whig_Party_members_of_the_United_States_House_of_Representatives_from_Illinois | @:D.F@D*,L*8*B*,L*8*B@:D.F@D$ܾܿܿ�\n | 2022-07-11 00:50:32 | Lincoln, Abraham | uca-default-u-kn | page |
| 307.0 | Wikipedia_indefinitely_move-protected_pages | *,L*8*B@:D.F@D*,L*8*B@:D.F@D#ܿܿܿ�\n | 2021-08-20 09:16:21 | Abraham Lincoln | uca-default-u-kn | page |
| 307.0 | Wikipedia_indefinitely_semi-protected_pages | *,L*8*B@:D.F@D*,L*8*B@:D.F@D#ܿܿܿ�\n | 2021-12-31 14:29:27 | Abraham Lincoln | uca-default-u-kn | page |
| 308.0 | 322_BC_deaths | *L:NPFP@2\r� | 2016-05-10 21:34:57 | null | uca-default-u-kn | page |
| 308.0 | 384_BC_births | *L:NPFP@2\r� | 2016-05-10 21:34:57 | null | uca-default-u-kn | page |
| 308.0 | 4th-century_BC_mathematicians | *L:NPFP@2\r� | 2021-04-22 01:11:56 | null | uca-default-u-kn | page |
| 308.0 | 4th-century_BC_philosophers | *L:NPFP@2\r� | 2015-09-22 14:47:02 | null | uca-default-u-kn | page |
| 308.0 | 4th-century_BC_writers | *L:NPFP@2\r� | 2015-09-22 14:47:02 | null | uca-default-u-kn | page |
| 308.0 | AC_with_38_elements | *L:NPFP@2\r� | 2022-04-06 12:22:19 | null | uca-default-u-kn | page |
| 308.0 | Academic_philosophers | *L:NPFP@2\r� | 2015-09-22 14:47:02 | null | uca-default-u-kn | page |
| 308.0 | Acting_theorists | *L:NPFP@2\r� | 2015-09-22 14:47:02 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_biologists | *L:NPFP@2\r� | 2015-09-22 14:47:02 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_cosmologists | *L:NPFP@2\r� | 2021-06-28 09:26:38 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_economists | *L:NPFP@2\r� | 2020-02-23 15:44:10 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_epistemologists | *L:NPFP@2\r� | 2016-12-14 22:12:57 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_ethicists | *L:NPFP@2\r� | 2016-12-14 22:12:57 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_logicians | *L:NPFP@2\r� | 2015-09-22 14:47:02 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_mathematicians | *L:NPFP@2\r� | 2015-09-22 14:47:02 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_metaphilosophers | *L:NPFP@2\r� | 2019-01-06 15:05:34 | null | uca-default-u-kn | page |
| 308.0 | Ancient_Greek_metaphysicians | *L:NPFP@2\r� | 2016-12-14 22:12:57 | null | uca-default-u-kn | page |
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| 332.0 | 1986_children's_books | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2017-05-16 04:35:21 | Animalia (Book) | uca-default-u-kn | page |
| 332.0 | Alphabet_books | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2016-08-14 01:21:33 | Animalia (Book) | uca-default-u-kn | page |
| 332.0 | Articles_with_short_description | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2021-08-30 01:18:23 | Animalia (Book) | uca-default-u-kn | page |
| 332.0 | Australian_children's_books | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2016-08-14 01:21:33 | Animalia (Book) | uca-default-u-kn | page |
| 332.0 | Picture_books_by_Graeme_Base | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2016-08-14 01:21:33 | Animalia (Book) | uca-default-u-kn | page |
| 332.0 | Puffin_Books_books | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2021-02-04 10:29:32 | Animalia (Book) | uca-default-u-kn | page |
| 332.0 | Puzzle_books | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2016-08-14 01:21:33 | Animalia (Book) | uca-default-u-kn | page |
| 332.0 | Short_description_matches_Wikidata | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2021-08-30 01:18:23 | Animalia (Book) | uca-default-u-kn | page |
| 332.0 | Use_dmy_dates_from_June_2013 | *D:B*@:* �,FF> �*D:B*@:* �,FF> �#ܽ��� | 2016-08-14 01:21:33 | Animalia (Book) | uca-default-u-kn | page |
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| 334.0 | Articles_containing_French-language_text | :DP2LD*P:FD*@*PFB:.P:B2ܹ��� | 2022-05-15 03:41:41 | null | uca-default-u-kn | page |
| 334.0 | Articles_containing_potentially_dated_statements_from_January_2017 | :DP2LD*P:FD*@*PFB:.P:B2ܹ��� | 2022-05-15 03:41:41 | null | uca-default-u-kn | page |
| 334.0 | Articles_with_EMU_identifiers | :DP2LD*P:FD*@*PFB:.P:B2ܹ��� | 2021-08-29 20:33:32 | null | uca-default-u-kn | page |
| 334.0 | Articles_with_short_description | :DP2LD*P:FD*@*PFB:.P:B2ܹ��� | 2019-06-06 18:45:30 | null | uca-default-u-kn | page |
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| 334.0 | Time_scales | :DP2LD*P:FD*@*PFB:.P:B2ܹ��� | 2007-06-16 21:23:19 | null | uca-default-u-kn | page |
| 334.0 | Use_British_English_from_April_2020 | :DP2LD*P:FD*@*PFB:.P:B2ܹ��� | 2020-09-02 08:12:25 | null | uca-default-u-kn | page |
| 334.0 | Use_dmy_dates_from_August_2022 | :DP2LD*P:FD*@*PFB:.P:B2ܹ��� | 2022-08-02 00:55:23 | null | uca-default-u-kn | page |
| 334.0 | Wikipedia_articles_needing_clarification_from_April_2020 | :DP2LD*P:FD*@*PFB:.P:B2ܹ��� | 2022-05-15 03:41:41 | null | uca-default-u-kn | page |
| 336.0 | Altruism | *@PLR:NB�� | 2015-11-12 18:53:47 | uca-default-u-kn | page | |
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| 336.0 | Auguste_Comte | *@PLR:NB� | 2015-11-12 18:53:47 | null | uca-default-u-kn | page |
| 336.0 | Commons_category_link_from_Wikidata | *@PLR:NB� | 2019-08-24 23:41:10 | null | uca-default-u-kn | page |
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| 336.0 | Moral_psychology | *@PLR:NB� | 2019-04-15 10:40:49 | null | uca-default-u-kn | page |
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| 336.0 | Use_dmy_dates_from_May_2020 | *@PLR:NB� | 2020-05-26 19:48:30 | null | uca-default-u-kn | page |
| 336.0 | Virtue | *@PLR:NB� | 2015-11-12 18:53:47 | null | uca-default-u-kn | page |
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| 339.0 | 1905_births | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
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| 339.0 | 20th-century_American_philosophers | L*D0*ZD*ZDL*D0������� | 2017-04-09 05:24:17 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | 20th-century_American_screenwriters | L*D0*ZD*ZDL*D0������� | 2021-06-20 20:20:26 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | 20th-century_American_women_writers | L*D0*ZD*ZDL*D0������� | 2017-07-21 20:12:00 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | 20th-century_Russian_philosophers | L*D0*ZD*ZDL*D0������� | 2018-02-28 02:03:22 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | 20th-century_atheists | L*D0*ZD*ZDL*D0������� | 2017-06-29 14:48:06 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | 20th-century_essayists | L*D0*ZD*ZDL*D0������� | 2019-02-05 14:12:02 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | 20th-century_pseudonymous_writers | L*D0*ZD*ZDL*D0������� | 2021-07-31 03:53:04 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | AC_with_31_elements | L*D0*ZD*ZDL*D0������� | 2022-03-06 22:57:28 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Activists_from_New_York_(state) | L*D0*ZD*ZDL*D0������� | 2017-08-08 19:54:55 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | All_articles_containing_potentially_dated_statements | L*D0*ZD*ZDL*D0������� | 2018-09-29 00:52:57 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_abortion-rights_activists | L*D0*ZD*ZDL*D0������� | 2019-05-25 11:44:17 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_anti-communists | L*D0*ZD*ZDL*D0������� | 2021-02-01 22:24:10 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_anti-fascists | L*D0*ZD*ZDL*D0������� | 2018-04-07 02:27:29 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_atheist_writers | L*D0*ZD*ZDL*D0������� | 2019-09-30 21:09:34 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_essayists | L*D0*ZD*ZDL*D0������� | 2013-08-02 05:37:00 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_ethicists | L*D0*ZD*ZDL*D0������� | 2013-02-01 08:14:27 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_people_of_Russian-Jewish_descent | L*D0*ZD*ZDL*D0������� | 2015-11-20 22:18:35 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_political_activists | L*D0*ZD*ZDL*D0������� | 2016-07-27 06:28:04 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_political_philosophers | L*D0*ZD*ZDL*D0������� | 2019-10-26 09:23:13 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_science_fiction_writers | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_secularists | L*D0*ZD*ZDL*D0������� | 2016-09-22 13:03:45 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_women_activists | L*D0*ZD*ZDL*D0������� | 2016-07-27 06:23:13 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_women_dramatists_and_playwrights | L*D0*ZD*ZDL*D0������� | 2014-10-15 04:51:25 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_women_essayists | L*D0*ZD*ZDL*D0������� | 2016-09-04 08:31:22 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_women_novelists | L*D0*ZD*ZDL*D0������� | 2013-01-02 21:48:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_women_philosophers | L*D0*ZD*ZDL*D0������� | 2013-10-25 19:57:45 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_women_screenwriters | L*D0*ZD*ZDL*D0������� | 2014-09-05 18:23:09 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | American_writers_of_Russian_descent | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Aristotelian_philosophers | L*D0*ZD*ZDL*D0������� | 2018-05-04 21:57:26 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_containing_Hebrew-language_text | *ZDL*D0��� | 2022-09-19 06:57:04 | null | uca-default-u-kn | page |
| 339.0 | Articles_containing_Russian-language_text | *ZDL*D0��� | 2022-03-13 00:42:34 | null | uca-default-u-kn | page |
| 339.0 | Articles_containing_potentially_dated_statements_from_2020 | L*D0*ZD*ZDL*D0������� | 2022-07-30 21:43:28 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_BIBSYS_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_BNE_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_BNF_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_CANTICN_identifiers | L*D0*ZD*ZDL*D0������� | 2022-03-09 13:28:31 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_CINII_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_Curlie_links | L*D0*ZD*ZDL*D0������� | 2018-02-12 16:46:52 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_DTBIO_identifiers | L*D0*ZD*ZDL*D0������� | 2022-02-21 19:07:43 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_FAST_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_GND_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_ICCU_identifiers | L*D0*ZD*ZDL*D0������� | 2022-03-06 22:57:28 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_ISNI_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_Internet_Archive_links | L*D0*ZD*ZDL*D0������� | 2016-07-19 23:15:39 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_Internet_Encyclopedia_of_Philosophy_links | L*D0*ZD*ZDL*D0������� | 2019-09-23 12:56:07 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_J9U_identifiers | L*D0*ZD*ZDL*D0������� | 2022-02-21 19:07:43 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_LCCN_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_LNB_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_LibriVox_links | L*D0*ZD*ZDL*D0������� | 2016-07-19 23:06:42 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_MusicBrainz_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_NDL_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_NKC_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_NLA_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_NLG_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_NLK_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_NSK_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_NTA_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_Open_Library_links | L*D0*ZD*ZDL*D0������� | 2016-07-19 23:30:04 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_PLWABN_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_Project_Gutenberg_links | L*D0*ZD*ZDL*D0������� | 2016-07-19 23:30:04 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_RERO_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_SELIBR_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_SNAC-ID_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_SUDOC_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_Trove_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_ULAN_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_VIAF_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_VcBA_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_WORLDCATID_identifiers | L*D0*ZD*ZDL*D0������� | 2021-08-29 20:33:33 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Articles_with_short_description | L*D0*ZD*ZDL*D0������� | 2020-02-13 07:05:37 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Atheist_philosophers | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Atheists_from_the_Russian_Empire | L*D0*ZD*ZDL*D0������� | 2022-10-03 06:32:50 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Ayn_Rand | *ZDL*D0���� | 2014-08-05 11:30:10 | uca-default-u-kn | page | |
| 339.0 | Burials_at_Kensico_Cemetery | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Critics_of_Christianity | L*D0*ZD*ZDL*D0������� | 2022-02-16 06:12:17 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Critics_of_Marxism | L*D0*ZD*ZDL*D0������� | 2017-01-26 13:21:42 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Dramatists_and_playwrights_from_the_Russian_Empire | L*D0*ZD*ZDL*D0������� | 2022-10-03 06:49:25 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Epistemologists | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Exophonic_writers | L*D0*ZD*ZDL*D0������� | 2017-10-17 08:46:03 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Female_critics_of_feminism | L*D0*ZD*ZDL*D0������� | 2017-12-25 23:29:09 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Good_articles | L*D0*ZD*ZDL*D0������� | 2020-02-13 07:05:37 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jewish_American_activists | L*D0*ZD*ZDL*D0������� | 2022-07-30 18:00:25 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jewish_American_atheists | L*D0*ZD*ZDL*D0������� | 2022-02-19 10:16:22 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jewish_American_dramatists_and_playwrights | L*D0*ZD*ZDL*D0������� | 2015-11-20 22:18:35 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jewish_American_novelists | L*D0*ZD*ZDL*D0������� | 2015-11-20 22:18:35 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jewish_anti-communists | L*D0*ZD*ZDL*D0������� | 2019-04-16 23:22:59 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jewish_anti-fascists | L*D0*ZD*ZDL*D0������� | 2019-02-12 01:52:28 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jewish_philosophers | L*D0*ZD*ZDL*D0������� | 2015-11-20 22:18:35 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jewish_women_writers | L*D0*ZD*ZDL*D0������� | 2015-11-20 22:18:35 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Jews_from_the_Russian_Empire | L*D0*ZD*ZDL*D0������� | 2022-09-26 11:11:14 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Metaphysicians | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Novelists_from_New_York_(state) | L*D0*ZD*ZDL*D0������� | 2018-02-10 17:22:22 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Objectivists | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | People_with_acquired_American_citizenship | L*D0*ZD*ZDL*D0������� | 2016-08-12 20:05:20 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Philosophers_from_New_York_(state) | L*D0*ZD*ZDL*D0������� | 2017-08-01 09:48:22 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Political_philosophers | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Pseudonymous_women_writers | L*D0*ZD*ZDL*D0������� | 2018-05-28 13:27:19 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Saint_Petersburg_State_University_alumni | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Screenwriters_from_New_York_(state) | L*D0*ZD*ZDL*D0������� | 2018-10-28 01:25:42 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Short_description_matches_Wikidata | L*D0*ZD*ZDL*D0������� | 2022-09-16 16:34:28 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Social_critics | L*D0*ZD*ZDL*D0������� | 2022-02-16 06:12:17 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Soviet_emigrants_to_the_United_States | L*D0*ZD*ZDL*D0������� | 2013-08-06 03:33:42 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Use_mdy_dates_from_July_2022 | L*D0*ZD*ZDL*D0������� | 2022-07-10 12:25:42 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Women_science_fiction_and_fantasy_writers | L*D0*ZD*ZDL*D0������� | 2012-11-12 04:28:54 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Writers_from_New_York_City | L*D0*ZD*ZDL*D0������� | 2013-04-29 07:43:45 | Rand, Ayn | uca-default-u-kn | page |
| 339.0 | Writers_from_Saint_Petersburg | L*D0*ZD*ZDL*D0������� | 2014-04-03 21:22:10 | Rand, Ayn | uca-default-u-kn | page |
| 340.0 | 1947_births | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | 20th-century_French_mathematicians | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | 21st-century_French_mathematicians | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | All_articles_to_be_expanded | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_needing_translation_from_French_Wikipedia | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_to_be_expanded_from_September_2020 | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_using_small_message_boxes | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_BIBSYS_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_BNF_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_DBLP_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-08-03 13:01:37 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_GND_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_Google_Scholar_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-07-28 17:15:49 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_ISNI_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_J9U_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-07-27 01:53:15 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_LCCN_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_LNB_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_MATHSN_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_MGP_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_NDL_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_NKC_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_NTA_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_PLWABN_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_SNAC-ID_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_SUDOC_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_Trove_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_VIAF_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
| 340.0 | Articles_with_WORLDCATID_identifiers | .FDD2N*@*:D*@*:D.FDD2Nܿ����� | 2022-03-18 08:23:30 | Connes, Alain | uca-default-u-kn | page |
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| 358.0 | Articles_with_excerpts | *@62L:*�\n | 2020-05-17 16:28:35 | null | uca-default-u-kn | page |
| 358.0 | Articles_with_hAudio_microformats | *@62L:*�\n | 2022-10-20 21:01:42 | null | uca-default-u-kn | page |
| 358.0 | Articles_with_multiple_identifiers | *@62L:*�\n | 2022-08-25 13:14:54 | null | uca-default-u-kn | page |
| 358.0 | Articles_with_short_description | *@62L:*�\n | 2022-08-25 13:14:54 | null | uca-default-u-kn | page |
| 358.0 | Articles_with_text_in_Berber_languages | *@62L:*�\n | 2022-10-20 21:01:42 | null | uca-default-u-kn | page |
| 358.0 | Articles_with_unsourced_statements_from_February_2021 | *@62L:*�\n | 2021-10-11 17:06:21 | null | uca-default-u-kn | page |
| 358.0 | Articles_with_unsourced_statements_from_July_2022 | *@62L:*�\n | 2022-07-08 19:31:42 | null | uca-default-u-kn | page |
| 358.0 | Articles_with_unsourced_statements_from_March_2021 | *@62L:*�\n | 2021-08-09 14:23:12 | null | uca-default-u-kn | page |
| 358.0 | Berber-speaking_countries_and_territories | *@62L:*�\n | 2018-05-01 19:36:47 | null | uca-default-u-kn | page |
| 358.0 | CS1:_Julian–Gregorian_uncertainty | *@62L:*�\n | 2022-08-01 14:40:10 | null | uca-default-u-kn | page |
| 358.0 | CS1_Arabic-language_sources_(ar) | *@62L:*�\n | 2022-08-01 14:40:10 | null | uca-default-u-kn | page |
| 358.0 | CS1_French-language_sources_(fr) | *@62L:*�\n | 2022-08-01 14:40:10 | null | uca-default-u-kn | page |
| 358.0 | CS1_Spanish-language_sources_(es) | *@62L:*�\n | 2022-08-01 14:40:10 | null | uca-default-u-kn | page |
| 358.0 | Coordinates_on_Wikidata | *@62L:*�\n | 2022-08-25 13:14:54 | null | uca-default-u-kn | page |
| 358.0 | Countries_in_Africa | *@62L:*�\n | 2022-05-23 01:13:11 | null | uca-default-u-kn | page |
| 358.0 | French-speaking_countries_and_territories | *@62L:*�\n | 2018-05-01 19:36:47 | null | uca-default-u-kn | page |
The cl_sortkey column is supposed to look like that - it is somehow used in sorting the categories a page is in, so it is just a string (or prefix of a string, usually) that will sort in the same order as the actual name of the category. So it is not a column we need to make use of.
Let us now check if we got any corrupt records:
readFromCSV.createOrReplaceTempView("categorylinks")
SELECT * FROM categorylinks WHERE _corrupt_record IS NOT NULL
No corrupt records! Excellent.
Finally, let us save this data to the Delta Lake, after having removed columns we don't care about.
SELECT cl_from, cl_to, cl_type FROM categorylinks WHERE (cl_from IS NOT NULL) AND (cl_to IS NOT NULL) AND (cl_type IS NOT NULL) AND (_corrupt_record IS NULL)
| cl_from | cl_to | cl_type |
|---|---|---|
| 6782105.0 | Mid-importance_U.S._road_transport_articles | page |
| 6782105.0 | Oklahoma_articles_without_listas_parameter | page |
| 6782105.0 | Oklahoma_road_transport_articles | page |
| 6782105.0 | Wikipedia_CD_Selection-GAs | page |
| 6782105.0 | Wikipedia_good_articles | page |
| 6782106.0 | Comics_articles_needing_attention_to_coverage_and_accuracy | page |
| 6782106.0 | Comics_articles_needing_attention_to_grammar | page |
| 6782106.0 | Comics_articles_needing_attention_to_referencing_and_citation | page |
| 6782106.0 | Comics_articles_needing_attention_to_structure | page |
| 6782106.0 | Comics_articles_needing_attention_to_supporting_materials | page |
| 6782106.0 | Comics_articles_with_incomplete_B-Class_checklists | page |
| 6782106.0 | Low-importance_Comics_articles | page |
| 6782106.0 | Start-Class_Comics_articles | page |
| 6782106.0 | Start-Class_Comics_articles_of_Low-importance | page |
| 6782106.0 | WikiProject_Comics_articles | page |
| 6782109.0 | Redirects_from_other_capitalisations | page |
| 6782109.0 | Unprintworthy_redirects | page |
| 6782117.0 | All_Wikipedia_articles_written_in_Indian_English | page |
| 6782117.0 | All_articles_needing_additional_references | page |
| 6782117.0 | All_set_index_articles | page |
| 6782117.0 | Articles_needing_additional_references_from_July_2014 | page |
| 6782117.0 | Articles_with_short_description | page |
| 6782117.0 | Bahun | page |
| 6782117.0 | Ethnic_groups_in_Nepal | page |
| 6782117.0 | Indian_surnames | page |
| 6782117.0 | Khas_surnames | page |
| 6782117.0 | Nepali-language_surnames | page |
| 6782117.0 | Occupational_surnames | page |
| 6782117.0 | Short_description_is_different_from_Wikidata | page |
| 6782117.0 | Surnames | page |
| 6782117.0 | Use_Indian_English_from_October_2019 | page |
| 6782121.0 | Film_articles_with_one_associated_task_force | page |
| 6782121.0 | Filmmaking_task_force_articles | page |
| 6782121.0 | Stub-Class_film_articles | page |
| 6782121.0 | Stub-Class_filmmaking_articles | page |
| 6782121.0 | WikiProject_Film_articles | page |
| 6782123.0 | All_article_disambiguation_pages | page |
| 6782123.0 | All_disambiguation_pages | page |
| 6782123.0 | Disambiguation_pages | page |
| 6782123.0 | Disambiguation_pages_with_short_descriptions | page |
| 6782123.0 | Short_description_is_different_from_Wikidata | page |
| 6782126.0 | Commons_category_link_is_on_Wikidata | page |
| 6782126.0 | County_routes_in_Suffolk_County,_New_York | page |
| 6782128.0 | Redirects_connected_to_a_Wikidata_item | page |
| 6782129.0 | All_articles_with_dead_external_links | page |
| 6782129.0 | Articles_with_ISNI_identifiers | page |
| 6782129.0 | Articles_with_dead_external_links_from_July_2021 | page |
| 6782129.0 | Articles_with_short_description | page |
| 6782129.0 | Coordinates_on_Wikidata | page |
| 6782129.0 | Foundation_schools_in_Hampshire | page |
| 6782129.0 | Infobox_mapframe_without_OSM_relation_ID_on_Wikidata | page |
| 6782129.0 | Pages_using_the_Kartographer_extension | page |
| 6782129.0 | Secondary_schools_in_Hampshire | page |
| 6782129.0 | Short_description_is_different_from_Wikidata | page |
| 6782129.0 | Use_dmy_dates_from_January_2020 | page |
| 6782129.0 | Webarchive_template_wayback_links | page |
| 6782129.0 | Whitchurch,_Hampshire | page |
| 6782130.0 | Articles_with_unassessed_etymologies | page |
| 6782130.0 | Articles_with_unknown-importance_etymologies | page |
| 6782130.0 | Etymology_Task_Force_etymologies | page |
| 6782130.0 | Low-importance_English_Language_articles | page |
| 6782130.0 | Low-importance_Linguistics_articles | page |
| 6782130.0 | Start-Class_English_Language_articles | page |
| 6782130.0 | Start-Class_Linguistics_articles | page |
| 6782130.0 | WikiProject_English_Language_articles | page |
| 6782130.0 | WikiProject_Linguistics_articles | page |
| 6782131.0 | List-Class_Comics_articles | page |
| 6782131.0 | List-Class_Comics_articles_of_Low-importance | page |
| 6782131.0 | List-Class_List_articles | page |
| 6782131.0 | List-Class_Years_articles | page |
| 6782131.0 | List-Class_Years_articles_of_Low-importance | page |
| 6782131.0 | Low-importance_Comics_articles | page |
| 6782131.0 | Low-importance_List_articles | page |
| 6782131.0 | Low-importance_Years_articles | page |
| 6782131.0 | WikiProject_Comics_articles | page |
| 6782131.0 | WikiProject_Lists_articles | page |
| 6782133.0 | All_article_disambiguation_pages | page |
| 6782133.0 | All_disambiguation_pages | page |
| 6782133.0 | Disambiguation_pages | page |
| 6782133.0 | Disambiguation_pages_with_short_descriptions | page |
| 6782133.0 | Short_description_is_different_from_Wikidata | page |
| 6782134.0 | All_WikiProject_Canada_pages | page |
| 6782134.0 | NA-importance_Canada-related_articles | page |
| 6782134.0 | NA-importance_Ontario_articles | page |
| 6782134.0 | Redirect-Class_Canada-related_articles | page |
| 6782134.0 | Redirect-Class_Ontario_articles | page |
| 6782144.0 | All_Wikipedia_B-Class_vital_articles | page |
| 6782144.0 | All_Wikipedia_level-4_vital_articles | page |
| 6782144.0 | All_Wikipedia_vital_articles | page |
| 6782144.0 | All_Wikipedia_vital_articles_in_Science | page |
| 6782144.0 | B-Class_Geology_articles | page |
| 6782144.0 | High-importance_B-Class_Geology_articles | page |
| 6782144.0 | High-importance_Geology_articles | page |
| 6782144.0 | WikiProject_Geology_articles | page |
| 6782144.0 | Wikipedia_B-Class_level-4_vital_articles | page |
| 6782144.0 | Wikipedia_B-Class_vital_articles_in_Science | page |
| 6782144.0 | Wikipedia_level-4_vital_articles_in_Science | page |
| 6782157.0 | All_non-free_logos | file |
| 6782157.0 | All_non-free_media | file |
| 6782157.0 | Completed_non-free_use_rationale_logo_transclusions | file |
| 6782157.0 | Files_with_no_machine-readable_author | file |
| 6782157.0 | Noindexed_pages | file |
| 6782157.0 | Software_logos | file |
| 6782157.0 | Wikipedia_non-free_files_with_NFUR_stated | file |
| 6782157.0 | Wikipedia_non-free_files_with_valid_backlink | file |
| 6782160.0 | 2000s_alternative_metal_album_stubs | page |
| 6782160.0 | 2001_debut_albums | page |
| 6782160.0 | Album_articles_lacking_alt_text_for_covers | page |
| 6782160.0 | All_articles_needing_additional_references | page |
| 6782160.0 | All_stub_articles | page |
| 6782160.0 | Articles_needing_additional_references_from_September_2016 | page |
| 6782160.0 | Articles_with_MusicBrainz_release_group_identifiers | page |
| 6782160.0 | Articles_with_hAudio_microformats | page |
| 6782160.0 | Articles_with_short_description | page |
| 6782160.0 | Short_description_is_different_from_Wikidata | page |
| 6782160.0 | Systematic_(band)_albums | page |
| 6782168.0 | All_articles_needing_additional_references | page |
| 6782168.0 | All_stub_articles | page |
| 6782168.0 | Articles_needing_additional_references_from_April_2022 | page |
| 6782168.0 | Articles_with_short_description | page |
| 6782168.0 | Communities_in_Halifax,_Nova_Scotia | page |
| 6782168.0 | Coordinates_on_Wikidata | page |
| 6782168.0 | Halifax_County,_Nova_Scotia_geography_stubs | page |
| 6782168.0 | Short_description_is_different_from_Wikidata | page |
| 6782169.0 | All_non-free_logos | file |
| 6782169.0 | All_non-free_media | file |
| 6782169.0 | German_football_logos | file |
| 6782169.0 | Noindexed_pages | file |
| 6782169.0 | Wikipedia_non-free_files_with_NFUR_stated | file |
| 6782169.0 | Wikipedia_non-free_files_with_valid_backlink | file |
| 6782171.0 | Albums_by_artist | subcat |
| 6782171.0 | Drum_and_bass_albums | subcat |
| 6782171.0 | Electronic_dance_music_albums_by_Japanese_artists | subcat |
| 6782171.0 | House_music_albums_by_Japanese_artists | subcat |
| 6782171.0 | Jazz_albums_by_Japanese_artists | subcat |
| 6782171.0 | Set_categories | subcat |
| 6782171.0 | Shibuya-kei_albums | subcat |
| 6782171.0 | Synth-pop_albums_by_Japanese_artists | subcat |
| 6782171.0 | Trip_hop_albums_by_Japanese_artists | subcat |
| 6782176.0 | 1887_establishments_in_Ireland | page |
| 6782176.0 | All_Wikipedia_articles_written_in_Hiberno-English | page |
| 6782176.0 | All_articles_lacking_reliable_references | page |
| 6782176.0 | All_articles_with_a_promotional_tone | page |
| 6782176.0 | Articles_lacking_reliable_references_from_March_2008 | page |
| 6782176.0 | Articles_with_a_promotional_tone_from_February_2017 | page |
| 6782176.0 | Articles_with_multiple_maintenance_issues | page |
| 6782176.0 | Coordinates_not_on_Wikidata | page |
| 6782176.0 | Gaelic_Athletic_Association_clubs_established_in_1887 | page |
| 6782176.0 | Gaelic_football_clubs_in_County_Clare | page |
| 6782176.0 | Gaelic_games_clubs_in_County_Clare | page |
| 6782176.0 | Hurling_clubs_in_County_Clare | page |
| 6782176.0 | Pages_using_the_Kartographer_extension | page |
| 6782176.0 | Use_Hiberno-English_from_August_2020 | page |
| 6782176.0 | Use_dmy_dates_from_August_2020 | page |
| 6782178.0 | 1994_debut_albums | page |
| 6782178.0 | Album_articles_lacking_alt_text_for_covers | page |
| 6782178.0 | Albums_recorded_at_Chung_King_Studios | page |
| 6782178.0 | Articles_with_MusicBrainz_release_group_identifiers | page |
| 6782178.0 | Articles_with_hAudio_microformats | page |
| 6782178.0 | Articles_with_short_description | page |
| 6782178.0 | CS1_Japanese-language_sources_(ja) | page |
| 6782178.0 | Elektra_Records_albums | page |
| 6782178.0 | Short_description_is_different_from_Wikidata | page |
| 6782178.0 | Towa_Tei_albums | page |
| 6782178.0 | Track_listings_that_use_the_collapsed_parameter | page |
| 6782179.0 | Album_covers | file |
| 6782179.0 | All_non-free_media | file |
| 6782179.0 | Arcade_Fire_album_covers | file |
| 6782179.0 | Files_with_no_machine-readable_author | file |
| 6782179.0 | Noindexed_pages | file |
| 6782179.0 | Wikipedia_non-free_files_with_NFUR_stated | file |
| 6782179.0 | Wikipedia_non-free_files_with_valid_backlink | file |
| 6782182.0 | Communities_in_Russell,_Ontario | page |
| 6782192.0 | All_article_disambiguation_pages | page |
| 6782192.0 | All_disambiguation_pages | page |
| 6782192.0 | Disambiguation_pages | page |
| 6782192.0 | Disambiguation_pages_with_short_descriptions | page |
| 6782192.0 | Short_description_is_different_from_Wikidata | page |
| 6782193.0 | 1981_British_television_episodes | page |
| 6782193.0 | All_articles_with_unsourced_statements | page |
| 6782193.0 | Articles_with_short_description | page |
| 6782193.0 | Articles_with_unsourced_statements_from_August_2019 | page |
| 6782193.0 | BBC_episode_ID_same_as_Wikidata | page |
| 6782193.0 | Only_Fools_and_Horses_(series_1)_episodes | page |
| 6782193.0 | Pages_using_infobox_television_episode_with_image-related_values_without_an_image | page |
| 6782193.0 | Short_description_is_different_from_Wikidata | page |
| 6782193.0 | Television_episode_articles_with_short_description_and_disambiguated_page_names | page |
| 6782193.0 | Television_episode_articles_with_short_description_for_single_episodes | page |
| 6782193.0 | Use_dmy_dates_from_November_2020 | page |
| 6782199.0 | Biography_articles_of_living_people | page |
| 6782199.0 | Low-importance_Australia_articles | page |
| 6782199.0 | Low-importance_Australian_music_articles | page |
| 6782199.0 | Low-importance_biography_(musicians)_articles | page |
| 6782199.0 | Musicians_work_group_articles | page |
| 6782199.0 | Noindexed_pages | page |
| 6782199.0 | Stub-Class_Australia_articles | page |
| 6782199.0 | Stub-Class_Australian_music_articles | page |
| 6782199.0 | Stub-Class_biography_(musicians)_articles | page |
| 6782199.0 | Stub-Class_biography_articles | page |
| 6782199.0 | Unassessed_electronic_music_articles | page |
| 6782199.0 | Unknown-importance_electronic_music_articles | page |
| 6782199.0 | WikiProject_Australia_articles | page |
| 6782199.0 | WikiProject_Australian_music_articles | page |
| 6782199.0 | WikiProject_Biography_articles | page |
| 6782199.0 | WikiProject_Electronic_music_articles | page |
| 6782210.0 | Communities_in_Russell,_Ontario | page |
| 6782212.0 | American_cinema_task_force_articles | page |
| 6782212.0 | Film_articles_with_one_associated_task_force | page |
| 6782212.0 | Start-Class_American_cinema_articles | page |
| 6782212.0 | Start-Class_film_articles | page |
| 6782212.0 | Start-Class_television_articles | page |
| 6782212.0 | Unknown-importance_television_articles | page |
| 6782212.0 | WikiProject_Film_articles | page |
| 6782212.0 | WikiProject_Television_articles | page |
| 6782220.0 | 1888_births | page |
| 6782220.0 | 1970_deaths | page |
| 6782220.0 | 20th-century_Scottish_businesspeople | page |
| 6782220.0 | Anglo-Persian_Oil_Company | page |
| 6782220.0 | British_businesspeople_in_the_oil_industry | page |
| 6782220.0 | Burials_at_Putney_Vale_Cemetery | page |
| 6782220.0 | CS1:_Julian–Gregorian_uncertainty | page |
| 6782220.0 | Chairmen_of_BP | page |
| 6782220.0 | Commanders_of_the_Order_of_the_British_Empire | page |
| 6782220.0 | Hereditary_barons_created_by_Elizabeth_II | page |
| 6782220.0 | Pages_containing_London_Gazette_template_with_parameter_supp_set_to_y | page |
| 6782220.0 | Use_dmy_dates_from_January_2012 | page |
| 6782220.0 | Wikipedia_articles_needing_page_number_citations_from_February_2013 | page |
| 6782221.0 | Biography_articles_of_living_people | page |
| 6782221.0 | Low-importance_Australia_articles | page |
| 6782221.0 | Low-importance_Australian_music_articles | page |
| 6782221.0 | Musicians_work_group_articles | page |
| 6782221.0 | Noindexed_pages | page |
| 6782221.0 | Start-Class_Australia_articles | page |
| 6782221.0 | Start-Class_Australian_music_articles | page |
| 6782221.0 | Start-Class_biography_(musicians)_articles | page |
| 6782221.0 | Start-Class_biography_articles | page |
| 6782221.0 | Unknown-importance_biography_(musicians)_articles | page |
| 6782221.0 | WikiProject_Australia_articles | page |
| 6782221.0 | WikiProject_Australian_music_articles | page |
| 6782221.0 | WikiProject_Biography_articles | page |
| 6782227.0 | Redirects_from_other_capitalisations | page |
| 6782227.0 | Unprintworthy_redirects | page |
| 6782229.0 | AC_with_0_elements | page |
| 6782229.0 | Articles_with_short_description | page |
| 6782229.0 | Coordinates_on_Wikidata | page |
| 6782229.0 | Infobox_mapframe_without_OSM_relation_ID_on_Wikidata | page |
| 6782229.0 | Pages_using_the_Kartographer_extension | page |
| 6782229.0 | Public_high_schools_in_New_York_(state) | page |
| 6782229.0 | Schools_in_Onondaga_County,_New_York | page |
| 6782229.0 | Short_description_is_different_from_Wikidata | page |
| 6782229.0 | Syracuse_City_School_District | page |
| 6782238.0 | 1940_births | page |
| 6782238.0 | 2011_deaths | page |
| 6782238.0 | All_articles_covered_by_WikiProject_Wikify | page |
| 6782238.0 | All_articles_with_bare_URLs_for_citations | page |
| 6782238.0 | American_electrical_engineers | page |
| 6782238.0 | American_technology_writers | page |
| 6782238.0 | Analog_electronics_engineers | page |
| 6782238.0 | Articles_covered_by_WikiProject_Wikify_from_August_2022 | page |
| 6782238.0 | Articles_needing_cleanup_from_August_2022 | page |
| 6782238.0 | Articles_with_BNF_identifiers | page |
| 6782238.0 | Articles_with_ISNI_identifiers | page |
| 6782238.0 | Articles_with_J9U_identifiers | page |
| 6782238.0 | Articles_with_LCCN_identifiers | page |
| 6782238.0 | Articles_with_NKC_identifiers | page |
| 6782238.0 | Articles_with_PLWABN_identifiers | page |
| 6782238.0 | Articles_with_SUDOC_identifiers | page |
| 6782238.0 | Articles_with_Trove_identifiers | page |
| 6782238.0 | Articles_with_VIAF_identifiers | page |
| 6782238.0 | Articles_with_WORLDCATID_identifiers | page |
| 6782238.0 | Articles_with_bare_URLs_for_citations_from_August_2022 | page |
| 6782238.0 | Articles_with_hCards | page |
| 6782238.0 | CS1_errors:_missing_periodical | page |
| 6782238.0 | CS1_maint:_bot:_original_URL_status_unknown | page |
| 6782238.0 | Engineers_from_Connecticut | page |
| 6782238.0 | Integrated_circuits | page |
| 6782238.0 | MIT_School_of_Engineering_alumni | page |
| 6782238.0 | Northfield_Mount_Hermon_School_alumni | page |
| 6782238.0 | People_from_Rockville,_Connecticut | page |
| 6782238.0 | Road_incident_deaths_in_California | page |
| 6782249.0 | All_free_media | file |
| 6782249.0 | Copy_to_Wikimedia_Commons_(bot-assessed) | file |
| 6782249.0 | Creative_Commons_Attribution_2.5_files | file |
| 6782249.0 | Files_with_no_machine-readable_author | file |
| 6782249.0 | Files_with_no_machine-readable_description | file |
| 6782249.0 | Files_with_no_machine-readable_source | file |
| 6782249.0 | Hidden_templates_using_styles | file |
| 6782249.0 | Wikipedia_orphaned_files | file |
| 6782253.0 | 1848_births | page |
| 6782253.0 | 1899_deaths | page |
| 6782253.0 | Articles_containing_Japanese-language_text | page |
| 6782253.0 | Articles_with_FAST_identifiers | page |
| 6782253.0 | Articles_with_ISNI_identifiers | page |
| 6782253.0 | Articles_with_LCCN_identifiers | page |
| 6782253.0 | Articles_with_NDL_identifiers | page |
| 6782253.0 | Articles_with_VIAF_identifiers | page |
| 6782253.0 | Articles_with_WORLDCATID_identifiers | page |
| 6782253.0 | Articles_with_short_description | page |
| 6782253.0 | Japanese_generals | page |
| 6782253.0 | Japanese_military_personnel_of_the_First_Sino-Japanese_War | page |
| 6782253.0 | Kazoku | page |
| 6782253.0 | Military_strategists | page |
| 6782253.0 | People_from_Satsuma_Domain | page |
| 6782253.0 | People_of_Meiji-period_Japan | page |
| 6782253.0 | People_of_the_Boshin_War | page |
| 6782253.0 | Shimazu_retainers | page |
| 6782253.0 | Short_description_is_different_from_Wikidata | page |
| 6782257.0 | Redirects_from_other_capitalisations | page |
| 6782257.0 | Unprintworthy_redirects | page |
| 6782260.0 | Redirects_from_misspellings | page |
| 6782260.0 | Unprintworthy_redirects | page |
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| 6782398.0 | Albums_recorded_at_Chung_King_Studios | page |
| 6782398.0 | Albums_recorded_at_MSR_Studios | page |
| 6782398.0 | Articles_with_MusicBrainz_release_group_identifiers | page |
| 6782398.0 | Articles_with_hAudio_microformats | page |
| 6782398.0 | Articles_with_short_description | page |
| 6782398.0 | CS1_German-language_sources_(de) | page |
| 6782398.0 | CS1_Japanese-language_sources_(ja) | page |
| 6782398.0 | CS1_maint:_others_in_cite_AV_media_(notes) | page |
| 6782398.0 | East_West_Records_albums | page |
| 6782398.0 | Elektra_Records_albums | page |
| 6782398.0 | Museums_in_popular_culture | page |
| 6782398.0 | Short_description_is_different_from_Wikidata | page |
| 6782398.0 | Towa_Tei_albums | page |
| 6782402.0 | All_stub_articles | page |
| 6782402.0 | Articles_with_'species'_microformats | page |
| 6782402.0 | Articles_with_short_description | page |
| 6782402.0 | Commons_category_link_is_on_Wikidata | page |
| 6782402.0 | Flora_of_Argentina | page |
| 6782402.0 | Flora_of_Europe | page |
| 6782402.0 | Flora_of_New_Zealand | page |
| 6782402.0 | Flora_of_Norfolk_Island | page |
| 6782402.0 | Flora_of_Russia | page |
| 6782402.0 | Flora_of_Siberia | page |
| 6782402.0 | Flora_of_Uruguay | page |
| 6782402.0 | Galium | page |
| 6782402.0 | Plants_described_in_1753 | page |
| 6782402.0 | Rubioideae_stubs | page |
| 6782402.0 | Short_description_matches_Wikidata | page |
| 6782402.0 | Taxa_named_by_Carl_Linnaeus | page |
| 6782402.0 | Taxonbars_with_30–34_taxon_IDs | page |
| 6782407.0 | 1954_births | page |
| 6782407.0 | Alumni_of_Christ's_College,_Cambridge | page |
| 6782407.0 | Articles_with_short_description | page |
| 6782407.0 | Commanders_of_the_Order_of_the_British_Empire | page |
| 6782407.0 | Directors_of_George_Weston_Limited | page |
| 6782407.0 | English_chief_executives | page |
| 6782407.0 | English_publishers_(people) | page |
| 6782407.0 | Financial_Times_people | page |
| 6782407.0 | Living_people | page |
| 6782407.0 | Short_description_matches_Wikidata | page |
| 6782407.0 | Use_British_English_from_February_2015 | page |
| 6782407.0 | Use_dmy_dates_from_February_2015 | page |
| 6782411.0 | 1996_German_television_series_debuts | page |
| 6782411.0 | 2005_German_television_series_endings | page |
| 6782411.0 | German-language_television_shows | page |
| 6782411.0 | German_comedy_television_series | page |
| 6782411.0 | IMDb_ID_same_as_Wikidata | page |
| 6782411.0 | RTL_(German_TV_channel)_original_programming | page |
| 6782411.0 | Use_dmy_dates_from_July_2013 | page |
| 6782416.0 | 1979_debut_singles | page |
| 6782416.0 | 1979_songs | page |
| 6782416.0 | 1980_singles | page |
| 6782416.0 | 1997_singles | page |
| 6782416.0 | 2006_singles | page |
| 6782416.0 | All_Around_the_World_Productions_singles | page |
| 6782416.0 | Articles_with_MusicBrainz_work_identifiers | page |
| 6782416.0 | Articles_with_hAudio_microformats | page |
| 6782416.0 | Articles_with_multiple_identifiers | page |
| 6782416.0 | Articles_with_short_description | page |
| 6782416.0 | CS1_Dutch-language_sources_(nl) | page |
| 6782416.0 | CS1_French-language_sources_(fr) | page |
| 6782416.0 | CS1_German-language_sources_(de) | page |
| 6782416.0 | CS1_maint:_others_in_cite_AV_media_(notes) | page |
| 6782416.0 | Carrere_Records_singles | page |
| 6782416.0 | Certification_Table_Entry_usages_for_France | page |
| 6782416.0 | Certification_Table_Entry_usages_for_United_Kingdom | page |
| 6782416.0 | N-Trance_songs | page |
| 6782416.0 | Number-one_singles_in_Norway | page |
| 6782416.0 | Ottawan_songs | page |
| 6782416.0 | Pages_using_certification_Table_Entry_with_sales_figures | page |
| 6782416.0 | Pages_using_certification_Table_Entry_with_sales_footnote | page |
| 6782416.0 | Short_description_matches_Wikidata | page |
| 6782416.0 | Singlechart_called_without_artist | page |
| 6782416.0 | Singlechart_called_without_song | page |
| 6782416.0 | Singlechart_usages_for_Austria | page |
| 6782416.0 | Singlechart_usages_for_Dutch100 | page |
| 6782416.0 | Singlechart_usages_for_Dutch40 | page |
| 6782416.0 | Singlechart_usages_for_Finland | page |
| 6782416.0 | Singlechart_usages_for_Flanders | page |
| 6782416.0 | Singlechart_usages_for_France | page |
| 6782416.0 | Singlechart_usages_for_Ireland2 | page |
| 6782416.0 | Singlechart_usages_for_Norway | page |
| 6782416.0 | Singlechart_usages_for_Scotland | page |
| 6782416.0 | Singlechart_usages_for_Switzerland | page |
| 6782416.0 | Singlechart_usages_for_UK | page |
| 6782416.0 | Singlechart_usages_for_UKdance | page |
| 6782416.0 | Singlechart_usages_for_United_Kingdom | page |
| 6782416.0 | Singlechart_usages_for_West_Germany | page |
| 6782416.0 | Songs_about_disco | page |
| 6782416.0 | Songs_written_by_Daniel_Vangarde | page |
| 6782416.0 | Songs_written_by_Jean_Kluger | page |
| 6782416.0 | Universal_Music_Group_singles | page |
| 6782416.0 | Use_dmy_dates_from_December_2013 | page |
| 6782419.0 | Shared_IP_addresses_from_educational_institutions | page |
| 6782421.0 | 1926_births | page |
| 6782421.0 | 2012_deaths | page |
| 6782421.0 | All_BLP_articles_lacking_sources | page |
| 6782421.0 | All_stub_articles | page |
| 6782421.0 | Alumni_of_the_University_of_Glasgow | page |
| 6782421.0 | Articles_with_short_description | page |
| 6782421.0 | BLP_articles_lacking_sources_from_November_2010 | page |
| 6782421.0 | CMS_Grammar_School,_Lagos_alumni | page |
| 6782421.0 | EngvarB_from_July_2022 | page |
| 6782421.0 | Foreign_ministers_of_Nigeria | page |
| 6782421.0 | International_Olympic_Committee_members | page |
| 6782421.0 | Nigerian_generals | page |
| 6782421.0 | Nigerian_military_doctors | page |
| 6782421.0 | Nigerian_politician_stubs | page |
| 6782421.0 | People_from_Kaduna | page |
| 6782421.0 | Short_description_matches_Wikidata | page |
| 6782421.0 | Use_dmy_dates_from_July_2022 | page |
| 6782421.0 | Yoruba_physicians | page |
| 6782421.0 | Yoruba_politicians | page |
| 6782422.0 | 1990_establishments_in_Uttar_Pradesh | page |
| 6782422.0 | All_Wikipedia_articles_written_in_Indian_English | page |
| 6782422.0 | All_articles_with_bare_URLs_for_citations | page |
| 6782422.0 | Articles_with_PDF_format_bare_URLs_for_citations | page |
| 6782422.0 | Articles_with_bare_URLs_for_citations_from_June_2022 | page |
| 6782422.0 | Articles_with_short_description | page |
| 6782422.0 | Development_finance_institutions | page |
| 6782422.0 | Financial_services_companies_of_India | page |
| 6782422.0 | Government_agencies_established_in_1990 | page |
| 6782422.0 | Microfinance_in_India | page |
| 6782422.0 | Short_description_is_different_from_Wikidata | page |
| 6782422.0 | Small-scale_industry_in_India | page |
| 6782422.0 | Use_Indian_English_from_January_2016 | page |
| 6782422.0 | Use_dmy_dates_from_January_2016 | page |
| 6782422.0 | Wikipedia_articles_with_possible_conflicts_of_interest_from_June_2018 | page |
| 6782423.0 | 1945_births | page |
| 6782423.0 | All_articles_with_unsourced_statements | page |
| 6782423.0 | Articles_with_unsourced_statements_from_December_2017 | page |
| 6782423.0 | CS1_maint:_bot:_original_URL_status_unknown | page |
| 6782423.0 | Living_people | page |
| 6782423.0 | Mayors_of_places_in_New_Jersey | page |
| 6782423.0 | People_from_Brooklyn | page |
| 6782423.0 | People_from_Fanwood,_New_Jersey | page |
| 6782423.0 | Saint_Elizabeth_University_alumni | page |
| 6782423.0 | Women_mayors_of_places_in_New_Jersey | page |
| 6782430.0 | 1930s_sailboat_type_designs | page |
| 6782430.0 | All_Wikipedia_articles_written_in_American_English | page |
| 6782430.0 | Articles_with_short_description | page |
| 6782430.0 | Commons_category_link_from_Wikidata | page |
| 6782430.0 | Keelboats | page |
| 6782430.0 | Sailboat_type_designs_by_C._Raymond_Hunt_Associates | page |
| 6782430.0 | Sailboat_types_built_by_Cape_Cod_Shipbuilding | page |
| 6782430.0 | Sailboat_types_built_by_George_Lawley_&_Son | page |
| 6782430.0 | Sailboat_types_built_by_Graves_Yacht_Yard | page |
| 6782430.0 | Sailboat_types_built_by_New_Holland_Marine_Group | page |
| 6782430.0 | Sailboat_types_built_by_W._D._Schock_Corp | page |
| 6782430.0 | Sailing_yachts | page |
| 6782430.0 | Short_description_matches_Wikidata | page |
| 6782430.0 | Two-person_sailboats | page |
| 6782430.0 | Use_American_English_from_November_2020 | page |
| 6782430.0 | Use_dmy_dates_from_November_2020 | page |
| 6782436.0 | Printworthy_redirects | page |
| 6782436.0 | Redirects_from_alternative_scientific_names_of_plants | page |
| 6782441.0 | AC_with_0_elements | page |
| 6782441.0 | All_articles_with_unsourced_statements | page |
| 6782441.0 | Articles_with_short_description | page |
| 6782441.0 | Articles_with_unsourced_statements_from_December_2019 | page |
| 6782441.0 | Climate_change_organizations_based_in_the_United_States | page |
| 6782441.0 | Organizations_established_in_2019 | page |
| 6782441.0 | Short_description_matches_Wikidata | page |
| 6782445.0 | Printworthy_redirects | page |
| 6782445.0 | Redirects_to_scientific_names_of_plants | page |
| 6782446.0 | Wikipedians_who_use_RC_script | page |
| 6782447.0 | 1927_births | page |
| 6782447.0 | 2005_deaths | page |
| 6782447.0 | 20th-century_Nigerian_medical_doctors | page |
| 6782447.0 | Ahmadu_Bello_University_faculty | page |
| 6782447.0 | Alumni_of_the_University_of_Liverpool | page |
| 6782447.0 | Alumni_of_the_University_of_London | page |
| 6782447.0 | CS1_maint:_url-status | page |
| 6782447.0 | Foreign_ministers_of_Nigeria | page |
| 6782447.0 | National_Party_of_Nigeria_politicians | page |
| 6782447.0 | Nigerian_Christians | page |
| 6782447.0 | Nigerian_expatriate_academics_in_the_United_States | page |
| 6782447.0 | People_from_Kaduna_State | page |
| 6782447.0 | Permanent_Representatives_of_Nigeria_to_the_United_Nations | page |
| 6782447.0 | University_of_Ibadan_alumni | page |
| 6782447.0 | University_of_Lagos_faculty | page |
| 6782447.0 | University_of_Rochester_faculty | page |
| 6782450.0 | Printworthy_redirects | page |
| 6782450.0 | Redirects_to_scientific_names_of_plants | page |
| 6782452.0 | Apache_httpd_modules | page |
| 6782452.0 | Articles_with_underscores_in_the_title | page |
| 6782457.0 | All_Wikipedia_articles_written_in_American_English | page |
| 6782457.0 | Articles_with_short_description | page |
| 6782457.0 | Coordinates_on_Wikidata | page |
| 6782457.0 | New_Jersey_District_Factor_Group_FG | page |
| 6782457.0 | School_districts_in_Monmouth_County,_New_Jersey | page |
| 6782457.0 | Short_description_matches_Wikidata | page |
| 6782457.0 | Use_American_English_from_June_2020 | page |
| 6782457.0 | Use_mdy_dates_from_June_2020 | page |
| 6782457.0 | West_Long_Branch,_New_Jersey | page |
| 6782460.0 | All_BLP_articles_lacking_sources | page |
| 6782460.0 | All_articles_covered_by_WikiProject_Wikify | page |
| 6782460.0 | All_articles_with_bare_URLs_for_citations | page |
| 6782460.0 | Articles_containing_Arabic-language_text | page |
| 6782460.0 | Articles_covered_by_WikiProject_Wikify_from_September_2022 | page |
| 6782460.0 | Articles_needing_cleanup_from_September_2022 | page |
| 6782460.0 | Articles_with_ISNI_identifiers | page |
| 6782460.0 | Articles_with_J9U_identifiers | page |
| 6782460.0 | Articles_with_LCCN_identifiers | page |
| 6782460.0 | Articles_with_VIAF_identifiers | page |
| 6782460.0 | Articles_with_WORLDCATID_identifiers | page |
| 6782460.0 | Articles_with_bare_URLs_for_citations_from_September_2022 | page |
| 6782460.0 | Articles_with_short_description | page |
| 6782460.0 | BLP_articles_lacking_sources_from_February_2014 | page |
| 6782460.0 | Living_people | page |
| 6782460.0 | Palestinian_Christians | page |
| 6782460.0 | Palestinian_activists | page |
| 6782460.0 | People_from_Beit_Sahour | page |
| 6782460.0 | Short_description_is_different_from_Wikidata | page |
| 6782460.0 | YMCA_leaders | page |
| 6782460.0 | Year_of_birth_missing_(living_people) | page |
| 6782467.0 | All_articles_lacking_reliable_references | page |
| 6782467.0 | All_stub_articles | page |
| 6782467.0 | Articles_lacking_reliable_references_from_July_2008 | page |
| 6782467.0 | Bluegrass_music | page |
| 6782467.0 | Music_festivals_in_California | page |
| 6782467.0 | Music_organization_stubs | page |
| 6782467.0 | Music_organizations_based_in_the_United_States | page |
| 6782467.0 | Organizations_based_in_San_Francisco | page |
| 6782470.0 | All_articles_needing_additional_references | page |
| 6782470.0 | All_articles_with_unsourced_statements | page |
| 6782470.0 | Articles_needing_additional_references_from_May_2013 | page |
| 6782470.0 | Articles_with_unsourced_statements_from_January_2014 | page |
| 6782470.0 | Civil_procedure | page |
| 6782470.0 | Forensic_psychology | page |
| 6782470.0 | Juries | page |
| 6782470.0 | Psychological_methodology | page |
| 6782470.0 | Sociology_of_law | page |
| 6782470.0 | Webarchive_template_wayback_links | page |
| 6782473.0 | 1981_establishments_in_Alberta | page |
| 6782473.0 | All_articles_lacking_in-text_citations | page |
| 6782473.0 | Articles_lacking_in-text_citations_from_February_2013 | page |
| 6782473.0 | Articles_with_short_description | page |
| 6782473.0 | Commons_link_is_the_pagename | page |
| 6782473.0 | Foreign_policy_and_strategy_think_tanks | page |
| 6782473.0 | Short_description_matches_Wikidata | page |
| 6782473.0 | Think_tanks_established_in_1981 | page |
| 6782473.0 | University_of_Calgary | page |
| 6782474.0 | 1929_births | page |
| 6782474.0 | 20th-century_Indian_judges | page |
| 6782474.0 | 20th-century_Indian_lawyers | page |
| 6782474.0 | AC_with_0_elements | page |
| 6782474.0 | All_Wikipedia_articles_written_in_Indian_English | page |
| 6782474.0 | Articles_with_short_description | page |
| 6782474.0 | CS1_maint:_archived_copy_as_title | page |
| 6782474.0 | Chief_justices_of_India | page |
| 6782474.0 | Judges_of_the_Karnataka_High_Court | page |
| 6782474.0 | Karnataka_politicians | page |
| 6782474.0 | Living_people | page |
| 6782474.0 | Madhva_Brahmins | page |
| 6782474.0 | Recipients_of_the_Padma_Vibhushan_in_public_affairs | page |
| 6782474.0 | Recipients_of_the_Rajyotsava_Award_2014 | page |
| 6782474.0 | Short_description_matches_Wikidata | page |
| 6782474.0 | Telugu_people | page |
| 6782474.0 | University_Law_College,_Bangalore_University_alumni | page |
| 6782474.0 | University_of_Mysore_alumni | page |
| 6782474.0 | Use_Indian_English_from_August_2015 | page |
| 6782474.0 | Use_dmy_dates_from_August_2015 | page |
| 6782474.0 | Webarchive_template_wayback_links | page |
| 6782477.0 | C-Class_Higher_education_articles | page |
| 6782477.0 | C-Class_Hospital_articles | page |
| 6782477.0 | C-Class_New_York_(state)_articles | page |
| 6782477.0 | C-Class_Western_New_York_articles | page |
| 6782477.0 | Low-importance_Western_New_York_articles | page |
| 6782477.0 | Mid-importance_Hospital_articles | page |
| 6782477.0 | Unknown-importance_New_York_(state)_articles | page |
| 6782477.0 | WikiProject_Higher_education_articles | page |
| 6782477.0 | WikiProject_Hospitals_articles | page |
| 6782477.0 | WikiProject_Western_New_York | page |
| 6782478.0 | Shared_IP_addresses_from_educational_institutions | page |
| 6782479.0 | All_stub_articles | page |
| 6782479.0 | Catholic_University_of_America | page |
| 6782479.0 | Christian_studies_book_stubs | page |
| 6782479.0 | Oriental_Orthodoxy | page |
| 6782479.0 | Oriental_Orthodoxy_stubs | page |
| 6782479.0 | Publications_of_patristic_texts | page |
| 6782479.0 | Semitic_language_stubs | page |
| 6782479.0 | Series_of_books | page |
| 6782479.0 | Texts_in_Syriac | page |
| 6782484.0 | Redirects_from_songs | page |
| 6782484.0 | Redirects_to_sections | page |
| 6782484.0 | Unprintworthy_redirects | page |
| 6782488.0 | Avoided_double_redirects | page |
| 6782488.0 | Redirects_from_unnecessary_disambiguation | page |
| 6782488.0 | Unprintworthy_redirects | page |
| 6782493.0 | Album_covers | file |
| 6782493.0 | All_non-free_media | file |
| 6782493.0 | Big_Black_album_covers | file |
| 6782493.0 | Files_with_no_machine-readable_author | file |
| 6782493.0 | Noindexed_pages | file |
| 6782493.0 | Wikipedia_non-free_files_with_NFUR_stated | file |
| 6782493.0 | Wikipedia_non-free_files_with_valid_backlink | file |
| 6782496.0 | 2000_AD_comic_strips | page |
| 6782496.0 | All_stub_articles | page |
| 6782496.0 | British_comics | page |
| 6782496.0 | British_comics_stubs | page |
| 6782496.0 | Comics_by_John_Wagner | page |
| 6782496.0 | Judge_Dredd_characters | page |
| 6782496.0 | Use_dmy_dates_from_September_2019 | page |
| 6782502.0 | All_WikiProject_Molecular_Biology_articles | page |
| 6782502.0 | Low-importance_chemicals_articles | page |
| 6782502.0 | Mid-importance_Genetics_articles | page |
| 6782502.0 | Mid-importance_MCB_articles | page |
| 6782502.0 | Stub-Class_Genetics_articles | page |
| 6782502.0 | Stub-Class_MCB_articles | page |
| 6782502.0 | Stub-Class_Molecular_Biology_articles | page |
| 6782502.0 | Stub-Class_chemicals_articles | page |
| 6782502.0 | Unknown-importance_Molecular_Biology_articles | page |
| 6782502.0 | WikiProject_Genetics_articles | page |
| 6782502.0 | WikiProject_Molecular_and_Cellular_Biology_articles | page |
| 6782503.0 | Redirects_from_songs | page |
| 6782503.0 | Unprintworthy_redirects | page |
| 6782506.0 | All_free_media | file |
| 6782506.0 | Copy_to_Wikimedia_Commons_(bot-assessed) | file |
| 6782506.0 | Files_with_no_machine-readable_author | file |
| 6782506.0 | Files_with_no_machine-readable_description | file |
| 6782506.0 | Files_with_no_machine-readable_source | file |
| 6782506.0 | Hidden_templates_using_styles | file |
| 6782506.0 | Images_in_the_public_domain_in_the_United_States | file |
| 6782506.0 | Public_domain_art | file |
| 6782506.0 | Wikipedia_orphaned_files | file |
| 6782508.0 | All_free_media | file |
| 6782508.0 | Copy_to_Wikimedia_Commons_(bot-assessed) | file |
| 6782508.0 | Creative_Commons_Attribution-ShareAlike_3.0_files | file |
| 6782508.0 | Files_with_no_machine-readable_author | file |
| 6782508.0 | Files_with_no_machine-readable_description | file |
| 6782508.0 | Files_with_no_machine-readable_source | file |
| 6782508.0 | GFDL_files_with_disclaimers | file |
| 6782508.0 | Hidden_templates_using_styles | file |
| 6782508.0 | Self-published_work | file |
| 6782508.0 | Wikipedia_license_migration_completed | file |
| 6782508.0 | Wikipedia_orphaned_files | file |
| 6782513.0 | 1969_births | page |
| 6782513.0 | All_articles_with_dead_external_links | page |
| 6782513.0 | American_football_wide_receivers | page |
| 6782513.0 | Arizona_Cardinals_players | page |
| 6782513.0 | Articles_with_dead_external_links_from_March_2021 | page |
| 6782513.0 | Articles_with_short_description | page |
| 6782513.0 | Infobox_NFL_biography_articles_with_old_NFL.com_URL | page |
| 6782513.0 | Living_people | page |
| 6782513.0 | Miami_Dolphins_players | page |
val rowsToSave = spark.sql("SELECT cl_from, cl_to, cl_type FROM categorylinks WHERE (cl_from IS NOT NULL) AND (cl_to IS NOT NULL) AND (cl_type IS NOT NULL) AND (_corrupt_record IS NULL)")
rowsToSave.write.saveAsTable("enwiki_categorylinks")
rowsToSave: org.apache.spark.sql.DataFrame = [cl_from: int, cl_to: string ... 1 more field]
Loading of the Wikipedia data
This is very nearly just a copy of the 02 notebook that loaded the pages.
As a first step, we download the .sql file:
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
FileUtils.copyURLToFile(new URL("https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-category.sql.gz"), new File("/tmp/enwiki-latest-category.sql.gz"))
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
Having done this, we first unzip the file, and then move the file from local storage to the DBFS:
gzip -d /tmp/enwiki-latest-category.sql.gz
mv file:/tmp/enwiki-latest-category.sql /enwiki-latest-category.sql
res1: Boolean = true
Having gotten the data onto the DBFS, we can now read it into Spark:
val rawSQLdump = spark.read.textFile("/enwiki-latest-category.sql")
rawSQLdump: org.apache.spark.sql.Dataset[String] = [value: string]
The first fortyone lines are setting up the database, then we get a lot of very long INSERT INTO lines with many many entries being inserted.
println(rawSQLdump.take(41).mkString("\n"))
-- MySQL dump 10.19 Distrib 10.3.34-MariaDB, for debian-linux-gnu (x86_64)
--
-- Host: db1106 Database: enwiki
-- ------------------------------------------------------
-- Server version 10.4.25-MariaDB-log
/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;
/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;
/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;
/*!40101 SET NAMES utf8mb4 */;
/*!40103 SET @OLD_TIME_ZONE=@@TIME_ZONE */;
/*!40103 SET TIME_ZONE='+00:00' */;
/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;
/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;
/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;
/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;
--
-- Table structure for table `category`
--
DROP TABLE IF EXISTS `category`;
/*!40101 SET @saved_cs_client = @@character_set_client */;
/*!40101 SET character_set_client = utf8 */;
CREATE TABLE `category` (
`cat_id` int(10) unsigned NOT NULL AUTO_INCREMENT,
`cat_title` varbinary(255) NOT NULL DEFAULT '',
`cat_pages` int(11) NOT NULL DEFAULT 0,
`cat_subcats` int(11) NOT NULL DEFAULT 0,
`cat_files` int(11) NOT NULL DEFAULT 0,
PRIMARY KEY (`cat_id`),
UNIQUE KEY `cat_title` (`cat_title`),
KEY `cat_pages` (`cat_pages`)
) ENGINE=InnoDB AUTO_INCREMENT=248914087 DEFAULT CHARSET=binary ROW_FORMAT=COMPRESSED;
/*!40101 SET character_set_client = @saved_cs_client */;
--
-- Dumping data for table `category`
--
/*!40000 ALTER TABLE `category` DISABLE KEYS */;
The remaining rows look something like this, except much much longer:
println(rawSQLdump.take(42)(41).substring(0,244) + ",...," + rawSQLdump.take(42)(41).substring(rawSQLdump.take(42)(41).length()-81, rawSQLdump.take(42)(41).length()))
INSERT INTO `category` VALUES (2,'Unprintworthy_redirects',1545623,20,0),(3,'Computer_storage_devices',89,11,0),(7,'Unknown-importance_Animation_articles',279,21,0),(8,'Low-importance_Animation_articles',14235,21,0),(9,'Vietnam_stubs',303,10,0),...,(33807,'440s',18,16,0),(33808,'440s_BC',16,13,0),(33809,'440s_BC_births',16,1,0);
Next up, let us strip out the INSERT INTO bit and the initial and final parentheses, then split at each ),(, so that we get each entry as its own string.
val pageDataRows = rawSQLdump.filter(x => x.startsWith("INSERT INTO"))
.flatMap(x => x.substring(31, x.length()-2).split("""\),\("""))
pageDataRows: org.apache.spark.sql.Dataset[String] = [value: string]
So now our data looks like this:
println(pageDataRows.take(10).mkString("\n"))
2,'Unprintworthy_redirects',1545623,20,0
3,'Computer_storage_devices',89,11,0
7,'Unknown-importance_Animation_articles',279,21,0
8,'Low-importance_Animation_articles',14235,21,0
9,'Vietnam_stubs',303,10,0
10,'Rivers_of_Vietnam',103,3,0
12,'All_articles_with_unsourced_statements',472556,0,0
14,'Wikipedia_articles_needing_clarification',195,195,0
15,'Articles_needing_additional_references_from_January_2008',1237,0,0
16,'Comedy',96,29,0
This table is of quite modest size - only 2.2 million rows.
pageDataRows.count()
res18: Long = 2207725
The above looks a whole lot like a CSV file, doesn't it? Let's write it to file as such. Note that we write it as text instead of as CSV because our data is in the format of a single string per row.
pageDataRows.toDF().write.mode("overwrite").text("/WikipediaData/enwiki-category.csv")
Now we want to read this back in, but with the right schema and column names and so on. So we start by creating the schema. In order to be sure that all the rows got parsed correctly, we add an extra column named _corrupt_record, which will get the raw CSV text whenever it couldn't be parsed right, and otherwise be set to NULL.
import org.apache.spark.sql.types._
// Start by creating a case class of a row entry:
case class WikiCategory(cat_id:Int,
cat_title:String,
cat_pages:Int,
cat_subcats:Int,
cat_files:Int)
// then we generate a schema object from the case class: (code copypasted from here: https://sparkbyexamples.com/spark/convert-case-class-to-spark-schema/)
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
val pageSchema = ScalaReflection.schemaFor[WikiCategory].dataType.asInstanceOf[StructType].add("_corrupt_record", StringType, true)
import org.apache.spark.sql.types._
defined class WikiCategory
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
pageSchema: org.apache.spark.sql.types.StructType = StructType(StructField(cat_id,IntegerType,false),StructField(cat_title,StringType,true),StructField(cat_pages,IntegerType,false),StructField(cat_subcats,IntegerType,false),StructField(cat_files,IntegerType,false),StructField(_corrupt_record,StringType,true))
Then we read it back in with the schema we just created:
val readFromCSV = spark.read
.options(Map("quote" -> "'", "mode" -> "PERMISSIVE", "columnNameOfCorruptRecord" -> "_corrupt_record"))
.schema(pageSchema)
.csv("/WikipediaData/enwiki-category.csv")
readFromCSV: org.apache.spark.sql.DataFrame = [cat_id: int, cat_title: string ... 4 more fields]
Let's have a look at what we just created:
display(readFromCSV)
| cat_id | cat_title | cat_pages | cat_subcats | cat_files | _corrupt_record |
|---|---|---|---|---|---|
| 2.0 | Unprintworthy_redirects | 1545623.0 | 20.0 | 0.0 | null |
| 3.0 | Computer_storage_devices | 89.0 | 11.0 | 0.0 | null |
| 7.0 | Unknown-importance_Animation_articles | 279.0 | 21.0 | 0.0 | null |
| 8.0 | Low-importance_Animation_articles | 14235.0 | 21.0 | 0.0 | null |
| 9.0 | Vietnam_stubs | 303.0 | 10.0 | 0.0 | null |
| 10.0 | Rivers_of_Vietnam | 103.0 | 3.0 | 0.0 | null |
| 12.0 | All_articles_with_unsourced_statements | 472556.0 | 0.0 | 0.0 | null |
| 14.0 | Wikipedia_articles_needing_clarification | 195.0 | 195.0 | 0.0 | null |
| 15.0 | Articles_needing_additional_references_from_January_2008 | 1237.0 | 0.0 | 0.0 | null |
| 16.0 | Comedy | 96.0 | 29.0 | 0.0 | null |
| 17.0 | Sociolinguistics | 255.0 | 30.0 | 0.0 | null |
| 18.0 | Figures_of_speech | 132.0 | 13.0 | 0.0 | null |
| 20.0 | NASCAR_teams | 130.0 | 3.0 | 0.0 | null |
| 21.0 | Muhammad_Ali | 19.0 | 4.0 | 0.0 | null |
| 22.0 | Politics_and_government_work_group_articles | 236015.0 | 4.0 | 0.0 | null |
| 23.0 | Wikipedia_requested_photographs_of_politicians_and_government-people | 11918.0 | 1.0 | 0.0 | null |
| 24.0 | Stub-Class_biography_(politics_and_government)_articles | 123773.0 | 0.0 | 0.0 | null |
| 26.0 | Stub-Class_biography_articles | 1039170.0 | 10.0 | 0.0 | null |
| 27.0 | Unassessed_biography_articles | 47285.0 | 10.0 | 0.0 | null |
| 29.0 | High-importance_Animation_articles | 279.0 | 21.0 | 0.0 | null |
| 31.0 | AfD_debates | 479.0 | 12.0 | 0.0 | null |
| 32.0 | Articles_with_unsourced_statements | 200.0 | 194.0 | 0.0 | null |
| 35.0 | Self-published_work | 106467.0 | 1.0 | 106465.0 | null |
| 36.0 | Geography | 102.0 | 37.0 | 0.0 | null |
| 37.0 | Images_without_source | 0.0 | 0.0 | 0.0 | null |
| 38.0 | Candidates_for_speedy_deletion | 17.0 | 2.0 | 0.0 | null |
| 40.0 | All_non-free_media | 711706.0 | 1.0 | 711705.0 | null |
| 41.0 | Wikipedia_requested_photographs_of_sportspeople | 15123.0 | 1.0 | 0.0 | null |
| 42.0 | Thirty_Years'_War | 57.0 | 9.0 | 0.0 | null |
| 44.0 | African-American_history | 80.0 | 33.0 | 1.0 | null |
| 46.0 | History_of_Alabama | 76.0 | 29.0 | 0.0 | null |
| 47.0 | Groups_of_World_War_II | 32.0 | 1.0 | 0.0 | null |
| 48.0 | Congressional_Gold_Medal_recipients | 425.0 | 3.0 | 7.0 | null |
| 49.0 | United_States_Army_officers | 3642.0 | 18.0 | 0.0 | null |
| 50.0 | Tuskegee_University | 27.0 | 7.0 | 0.0 | null |
| 51.0 | Military_units_and_formations_of_the_United_States_in_World_War_II | 15.0 | 7.0 | 0.0 | null |
| 52.0 | People_from_Tuskegee,_Alabama | 56.0 | 2.0 | 0.0 | null |
| 53.0 | Tuskegee_Airmen | 156.0 | 0.0 | 0.0 | null |
| 54.0 | Chinese_Methodists | 12.0 | 3.0 | 0.0 | null |
| 55.0 | Chinese_Protestants | 34.0 | 15.0 | 0.0 | null |
| 56.0 | Shel_Silverstein_songs | 6.0 | 0.0 | 0.0 | null |
| 57.0 | Articles_lacking_sources | 209.0 | 207.0 | 0.0 | null |
| 58.0 | All_articles_lacking_sources | 135448.0 | 0.0 | 0.0 | null |
| 59.0 | Radio_stations_in_Saskatchewan | 41.0 | 7.0 | 0.0 | null |
| 60.0 | Western_Canada_radio_station_stubs | 24.0 | 4.0 | 0.0 | null |
| 61.0 | Saskatchewan_stubs | 94.0 | 5.0 | 0.0 | null |
| 62.0 | Multi-level_marketing | 6.0 | 2.0 | 0.0 | null |
| 64.0 | Filipino_Wikipedians | 725.0 | 3.0 | 0.0 | null |
| 65.0 | Wikipedians_interested_in_mapmaking | 318.0 | 0.0 | 0.0 | null |
| 66.0 | Wikipedians_interested_in_maps | 1245.0 | 1.0 | 0.0 | null |
| 67.0 | Wikipedians_who_listen_to_world_music | 294.0 | 4.0 | 0.0 | null |
| 68.0 | Wikipedians_interested_in_architecture | 997.0 | 2.0 | 0.0 | null |
| 69.0 | Wikipedians_interested_in_art | 997.0 | 17.0 | 0.0 | null |
| 70.0 | Wikipedian_ballroom_dancers | 84.0 | 0.0 | 0.0 | null |
| 71.0 | Wikipedian_dancers | 290.0 | 5.0 | 0.0 | null |
| 72.0 | Brasília | 17.0 | 12.0 | 0.0 | null |
| 74.0 | Sligo | 0.0 | 0.0 | 0.0 | null |
| 75.0 | Puerto_Rican_people | 31.0 | 29.0 | 0.0 | null |
| 77.0 | WikiProject_Canadian_communities_articles | 2.0 | 2.0 | 0.0 | null |
| 78.0 | B-Class_Canadian_communities_articles | 139.0 | 0.0 | 0.0 | null |
| 79.0 | Mid-importance_Canadian_communities_articles | 3425.0 | 0.0 | 0.0 | null |
| 81.0 | Importance_or_significance_not_asserted_pages_for_speedy_deletion | 0.0 | 0.0 | 0.0 | null |
| 82.0 | The_Taming_of_the_Shrew | 8.0 | 1.0 | 0.0 | null |
| 83.0 | Edwards_County,_Kansas | 10.0 | 4.0 | 0.0 | null |
| 86.0 | Unknown-importance_Olympics_articles | 18779.0 | 0.0 | 0.0 | null |
| 87.0 | WikiProject_Olympics_articles | 191404.0 | 2.0 | 0.0 | null |
| 88.0 | Stub-Class_Canadian_communities_articles | 8334.0 | 0.0 | 0.0 | null |
| 89.0 | Articles_lacking_sources_from_December_2007 | 428.0 | 0.0 | 0.0 | null |
| 90.0 | Kingston,_Jamaica | 29.0 | 9.0 | 0.0 | null |
| 94.0 | Wikipedians_interested_in_Japanese_mythology | 40.0 | 1.0 | 0.0 | null |
| 95.0 | WikiProject_Japanese_mythology_members | 21.0 | 0.0 | 0.0 | null |
| 96.0 | WikiProject_Statistics_members | 111.0 | 0.0 | 0.0 | null |
| 98.0 | 1978 | 44.0 | 37.0 | 0.0 | null |
| 102.0 | Musicians_work_group_articles | 163180.0 | 4.0 | 0.0 | null |
| 103.0 | Wikipedia_requested_photographs_of_musicians | 12467.0 | 0.0 | 0.0 | null |
| 104.0 | Start-Class_biography_(musicians)_articles | 69851.0 | 0.0 | 0.0 | null |
| 106.0 | Musicians_work_group_articles_needing_infoboxes | 1277.0 | 0.0 | 0.0 | null |
| 107.0 | Biography_articles_without_infoboxes | 23294.0 | 9.0 | 0.0 | null |
| 108.0 | Start-Class_biography_articles | 667451.0 | 10.0 | 0.0 | null |
| 109.0 | Punk_song_stubs | 68.0 | 0.0 | 0.0 | null |
| 111.0 | Planetary_nebulae | 125.0 | 1.0 | 0.0 | null |
| 112.0 | Methane | 52.0 | 3.0 | 0.0 | null |
| 113.0 | Economy_of_Russia | 93.0 | 22.0 | 0.0 | null |
| 114.0 | Climate_of_Texas | 7.0 | 0.0 | 0.0 | null |
| 115.0 | Transport_in_Burma | 0.0 | 0.0 | 0.0 | null |
| 116.0 | Townships_in_Kansas | 817.0 | 2.0 | 0.0 | null |
| 117.0 | Kansas_geography_stubs | 1027.0 | 6.0 | 0.0 | null |
| 118.0 | Series_of_children's_books | 662.0 | 84.0 | 0.0 | null |
| 121.0 | List-Class_Animation_articles | 1556.0 | 8.0 | 0.0 | null |
| 122.0 | Start-Class_Animation_articles | 7387.0 | 8.0 | 0.0 | null |
| 123.0 | Wikipedia_references_cleanup | 169.0 | 166.0 | 0.0 | null |
| 125.0 | Theorists | 20.0 | 18.0 | 0.0 | null |
| 126.0 | National_lower_houses | 116.0 | 27.0 | 0.0 | null |
| 127.0 | Spoken_articles | 1665.0 | 0.0 | 0.0 | null |
| 128.0 | United_States_House_of_Representatives | 57.0 | 11.0 | 0.0 | null |
| 129.0 | People_by_nationality | 246.0 | 246.0 | 0.0 | null |
| 130.0 | WikiProject_Molecular_and_Cellular_Biology | 17.0 | 7.0 | 0.0 | null |
| 131.0 | Primate_stubs | 44.0 | 3.0 | 0.0 | null |
| 132.0 | Old_World_monkeys | 10.0 | 3.0 | 0.0 | null |
| 133.0 | Fauna_of_Thailand | 17.0 | 7.0 | 0.0 | null |
| 134.0 | Fighter_aircraft | 88.0 | 54.0 | 0.0 | null |
| 135.0 | Transport_in_Croatia | 14.0 | 12.0 | 0.0 | null |
| 136.0 | Requests_for_unblock | 93.0 | 4.0 | 0.0 | null |
| 137.0 | Volcanic_belts | 35.0 | 10.0 | 0.0 | null |
| 139.0 | Transport_in_Denmark | 25.0 | 18.0 | 0.0 | null |
| 140.0 | 2000s_single_stubs | 819.0 | 1.0 | 0.0 | null |
| 141.0 | Canaan | 60.0 | 11.0 | 0.0 | null |
| 142.0 | Women_writers | 12.0 | 11.0 | 0.0 | null |
| 143.0 | Start-Class_biography_(military)_articles | 43358.0 | 0.0 | 0.0 | null |
| 144.0 | Military_biography_work_group_articles | 83368.0 | 5.0 | 0.0 | null |
| 145.0 | Biography_articles_with_listas_parameter | 0.0 | 0.0 | 0.0 | null |
| 147.0 | Stub-Class_biography_(military)_articles | 20981.0 | 0.0 | 0.0 | null |
| 150.0 | Medieval_literature | 292.0 | 27.0 | 0.0 | null |
| 152.0 | Transport_in_Lithuania | 14.0 | 13.0 | 0.0 | null |
| 154.0 | Stub-Class_Animation_articles | 4161.0 | 8.0 | 0.0 | null |
| 156.0 | Articles_for_deletion | 551.0 | 1.0 | 0.0 | null |
| 157.0 | All_articles_to_be_expanded | 70437.0 | 0.0 | 0.0 | null |
| 159.0 | Wikipedian_college_students | 1401.0 | 1.0 | 0.0 | null |
| 160.0 | Images_lacking_a_description | 0.0 | 0.0 | 0.0 | null |
| 161.0 | Uploader_unsure_of_copyright_status | 5.0 | 1.0 | 4.0 | null |
| 164.0 | All_images_with_unknown_copyright_status | 0.0 | 0.0 | 0.0 | null |
| 165.0 | Attack_pages_for_speedy_deletion | 0.0 | 0.0 | 0.0 | null |
| 167.0 | Vietnamese_Confucianists | 41.0 | 0.0 | 0.0 | null |
| 169.0 | Transport_in_Mauritius | 11.0 | 8.0 | 0.0 | null |
| 170.0 | Mountain_biking | 49.0 | 15.0 | 0.0 | null |
| 171.0 | 1939_births | 10840.0 | 0.0 | 0.0 | null |
| 172.0 | Articles_lacking_sources_from_March_2008 | 645.0 | 0.0 | 0.0 | null |
| 173.0 | Living_people | 1049898.0 | 2.0 | 0.0 | null |
| 175.0 | Transport_in_Mozambique | 15.0 | 9.0 | 0.0 | null |
| 176.0 | Lists_of_railway_stations_in_the_United_Kingdom | 27.0 | 1.0 | 0.0 | null |
| 177.0 | North_America | 29.0 | 21.0 | 0.0 | null |
| 178.0 | Non-fiction_writers | 20.0 | 12.0 | 0.0 | null |
| 180.0 | Solid-state_computer_storage_media | 59.0 | 1.0 | 0.0 | null |
| 181.0 | USB | 95.0 | 1.0 | 0.0 | null |
| 182.0 | Subdivisions_of_Kosovo | 7.0 | 4.0 | 0.0 | null |
| 184.0 | Articles_needing_additional_references_from_June_2007 | 603.0 | 0.0 | 0.0 | null |
| 188.0 | Commonwealth_of_Nations | 65.0 | 25.0 | 0.0 | null |
| 189.0 | Political_history_of_Australia | 88.0 | 20.0 | 0.0 | null |
| 190.0 | Political_history_of_Canada | 128.0 | 31.0 | 0.0 | null |
| 191.0 | Political_history_of_the_United_Kingdom | 160.0 | 64.0 | 0.0 | null |
| 192.0 | Stub-Class_biography_(musicians)_articles | 60830.0 | 0.0 | 0.0 | null |
| 193.0 | Odonata | 17.0 | 8.0 | 0.0 | null |
| 194.0 | Companies_based_in_Vancouver | 220.0 | 9.0 | 0.0 | null |
| 196.0 | Video_game_developers | 73.0 | 14.0 | 0.0 | null |
| 197.0 | History_of_the_Kurds | 0.0 | 0.0 | 0.0 | null |
| 198.0 | Torchwood_episodes | 37.0 | 1.0 | 0.0 | null |
| 199.0 | Federal_assistance_in_the_United_States | 81.0 | 5.0 | 0.0 | null |
| 200.0 | Nutrition | 204.0 | 28.0 | 9.0 | null |
| 202.0 | United_States_Department_of_Agriculture | 304.0 | 6.0 | 0.0 | null |
| 203.0 | Transport_in_the_Cayman_Islands | 6.0 | 5.0 | 0.0 | null |
| 204.0 | All_pages_needing_cleanup | 34785.0 | 5.0 | 0.0 | null |
| 206.0 | Military_of_the_United_Kingdom | 127.0 | 60.0 | 2.0 | null |
| 207.0 | Start-Class_Canadian_communities_articles | 4008.0 | 0.0 | 0.0 | null |
| 209.0 | Food_and_drink_stubs | 14.0 | 14.0 | 0.0 | null |
| 210.0 | Moons | 39.0 | 17.0 | 1.0 | null |
| 211.0 | Miscellaneous_pages_for_deletion | 3.0 | 0.0 | 0.0 | null |
| 212.0 | 1733_establishments | 10.0 | 10.0 | 0.0 | null |
| 213.0 | 1776_disestablishments | 10.0 | 7.0 | 0.0 | null |
| 214.0 | British_North_America | 63.0 | 13.0 | 0.0 | null |
| 216.0 | Former_British_colonies | 11.0 | 8.0 | 0.0 | null |
| 217.0 | History_of_Georgia_(U.S._state) | 108.0 | 41.0 | 0.0 | null |
| 218.0 | Thirteen_Colonies | 28.0 | 8.0 | 0.0 | null |
| 219.0 | Wehrmacht | 63.0 | 12.0 | 0.0 | null |
| 220.0 | Precambrian | 14.0 | 7.0 | 0.0 | null |
| 221.0 | Start-Class_United_States_military_history_articles | 30836.0 | 0.0 | 0.0 | null |
| 222.0 | United_States_military_history_task_force_articles | 74074.0 | 3.0 | 0.0 | null |
| 223.0 | Start-Class_American_Civil_War_articles | 6567.0 | 0.0 | 0.0 | null |
| 224.0 | American_Civil_War_task_force_articles | 13078.0 | 3.0 | 0.0 | null |
| 225.0 | Start-Class_military_history_articles | 100531.0 | 2.0 | 0.0 | null |
| 226.0 | Military_history_articles_with_incomplete_B-Class_checklists | 0.0 | 0.0 | 0.0 | null |
| 227.0 | Literature_stubs | 132.0 | 24.0 | 0.0 | null |
| 228.0 | Transport_in_the_Netherlands_Antilles | 3.0 | 2.0 | 0.0 | null |
| 230.0 | Top_Gear | 39.0 | 3.0 | 0.0 | null |
| 231.0 | Wikipedia_cleanup | 41.0 | 20.0 | 0.0 | null |
| 232.0 | Stubs | 1.0 | 0.0 | 0.0 | null |
| 233.0 | Articles_needing_additional_references_from_September_2007 | 729.0 | 0.0 | 0.0 | null |
| 235.0 | Transport_in_Vanuatu | 7.0 | 5.0 | 0.0 | null |
| 236.0 | Sumer | 53.0 | 13.0 | 0.0 | null |
| 237.0 | Sumerian_cities | 33.0 | 6.0 | 0.0 | null |
| 239.0 | New_York_City_Department_of_Education | 36.0 | 3.0 | 0.0 | null |
| 240.0 | Screenshots_of_television | 16234.0 | 24.0 | 16210.0 | null |
| 241.0 | Al_Gore | 49.0 | 4.0 | 0.0 | null |
| 243.0 | American_male_singers | 1474.0 | 15.0 | 0.0 | null |
| 244.0 | Pakistani_names | 103.0 | 8.0 | 0.0 | null |
| 246.0 | Punjabi_tribes | 168.0 | 16.0 | 0.0 | null |
| 247.0 | Edina,_Minnesota | 15.0 | 3.0 | 0.0 | null |
| 249.0 | Agriculture | 140.0 | 42.0 | 0.0 | null |
| 250.0 | Molecular_physics | 90.0 | 10.0 | 0.0 | null |
| 251.0 | Wikipedia_controversial_topics | 3500.0 | 3.0 | 0.0 | null |
| 253.0 | Sony_Computer_Entertainment | 0.0 | 0.0 | 0.0 | null |
| 254.0 | Redirects_from_merges | 58346.0 | 4.0 | 0.0 | null |
| 256.0 | Leicester_City_F.C. | 28.0 | 9.0 | 2.0 | null |
| 257.0 | English_football_club_stubs | 88.0 | 2.0 | 0.0 | null |
| 258.0 | Bosnian_and_Herzegovinian_sportspeople | 0.0 | 0.0 | 0.0 | null |
| 259.0 | Bosnia_and_Herzegovina_sportspeople | 11.0 | 8.0 | 0.0 | null |
| 260.0 | Articles_that_include_images_for_deletion | 2.0 | 2.0 | 0.0 | null |
| 261.0 | Logging | 87.0 | 8.0 | 0.0 | null |
| 263.0 | Great_Western_Railway_locomotives | 191.0 | 12.0 | 0.0 | null |
| 265.0 | Chinese_Confucianists | 105.0 | 2.0 | 0.0 | null |
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| 280.0 | Novel_stubs | 100.0 | 37.0 | 0.0 | null |
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| 323.0 | Electromagnetic_spectrum | 40.0 | 10.0 | 0.0 | null |
| 324.0 | Waves | 104.0 | 12.0 | 0.0 | null |
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| 327.0 | Ancient_languages | 26.0 | 17.0 | 0.0 | null |
| 328.0 | Fusional_languages | 43.0 | 7.0 | 0.0 | null |
| 329.0 | Languages_of_Italy | 43.0 | 11.0 | 0.0 | null |
| 330.0 | Languages_of_Vatican_City | 7.0 | 2.0 | 0.0 | null |
| 331.0 | Latino-Faliscan_languages | 8.0 | 2.0 | 0.0 | null |
| 332.0 | All_That | 19.0 | 3.0 | 0.0 | null |
| 333.0 | The_War_of_the_Worlds | 9.0 | 2.0 | 0.0 | null |
| 336.0 | Anime_and_manga_characters_with_superhuman_strength | 109.0 | 1.0 | 0.0 | null |
| 337.0 | Anime_and_manga_martial_artists | 0.0 | 0.0 | 0.0 | null |
| 339.0 | English_women_writers | 414.0 | 7.0 | 0.0 | null |
| 340.0 | Semi-protected_templates | 0.0 | 0.0 | 0.0 | null |
| 341.0 | User_warning_templates | 331.0 | 8.0 | 0.0 | null |
| 342.0 | New_Zealand_Confucianists | 1.0 | 0.0 | 0.0 | null |
| 343.0 | Free-to-air | 61.0 | 0.0 | 0.0 | null |
| 344.0 | 2008_deaths | 8528.0 | 4.0 | 0.0 | null |
| 345.0 | Album_covers | 194252.0 | 4.0 | 194247.0 | null |
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| 347.0 | Valdosta,_Georgia | 18.0 | 3.0 | 0.0 | null |
| 348.0 | National_Invitation_Tournament | 92.0 | 2.0 | 0.0 | null |
| 349.0 | Ghosts | 83.0 | 4.0 | 0.0 | null |
| 350.0 | Aer_Lingus | 12.0 | 1.0 | 2.0 | null |
| 351.0 | Deuteromycota | 11.0 | 0.0 | 0.0 | null |
| 352.0 | Fascism | 87.0 | 23.0 | 0.0 | null |
| 353.0 | 2006_Atlantic_hurricane_season | 12.0 | 0.0 | 0.0 | null |
| 354.0 | New_Zealand_people_by_religion | 14.0 | 14.0 | 0.0 | null |
| 355.0 | Confucianists_by_nationality | 10.0 | 10.0 | 0.0 | null |
| 356.0 | .NET_programming_languages | 36.0 | 1.0 | 0.0 | null |
| 357.0 | English_early_modern_theatre_companies | 23.0 | 1.0 | 0.0 | null |
| 358.0 | All_articles_proposed_for_deletion | 153.0 | 0.0 | 0.0 | null |
| 360.0 | Brand_name_food_products_stubs | 419.0 | 0.0 | 0.0 | null |
| 361.0 | Brand_name_snack_foods | 183.0 | 20.0 | 0.0 | null |
| 364.0 | Certification_marks | 93.0 | 5.0 | 0.0 | null |
| 365.0 | Energy_in_the_United_States | 78.0 | 18.0 | 0.0 | null |
| 366.0 | Product_certification | 58.0 | 8.0 | 0.0 | null |
| 367.0 | United_States_Environmental_Protection_Agency | 89.0 | 4.0 | 0.0 | null |
| 368.0 | United_States_environmental_law | 0.0 | 0.0 | 0.0 | null |
| 370.0 | South_Africa_geography_stubs | 52.0 | 11.0 | 0.0 | null |
| 371.0 | Capitals_in_Europe | 107.0 | 55.0 | 0.0 | null |
| 372.0 | Capitals_in_Asia | 129.0 | 58.0 | 0.0 | null |
| 375.0 | Uncategorized_pages | 744.0 | 3.0 | 0.0 | null |
| 376.0 | Slovakia | 18.0 | 15.0 | 0.0 | null |
| 377.0 | Monroe,_Louisiana | 17.0 | 5.0 | 0.0 | null |
| 378.0 | Croatian_football_biography_stubs | 150.0 | 5.0 | 0.0 | null |
| 379.0 | 1958_births | 14421.0 | 0.0 | 0.0 | null |
| 380.0 | Croatian_footballers | 1680.0 | 4.0 | 0.0 | null |
| 382.0 | 1982_FIFA_World_Cup_players | 526.0 | 0.0 | 0.0 | null |
| 385.0 | Cercle_Brugge_K.S.V._players | 373.0 | 0.0 | 0.0 | null |
| 386.0 | All_articles_needing_copy_edit | 1925.0 | 0.0 | 0.0 | null |
| 387.0 | Articles_lacking_reliable_references_from_August_2007 | 111.0 | 0.0 | 0.0 | null |
| 388.0 | Articles_that_need_to_differentiate_between_fact_and_fiction | 194.0 | 194.0 | 0.0 | null |
| 390.0 | DC_Comics_titles | 477.0 | 27.0 | 0.0 | null |
| 391.0 | Teen_Titans | 9.0 | 4.0 | 0.0 | null |
| 393.0 | Articles_that_need_to_be_wikified | 0.0 | 0.0 | 0.0 | null |
| 394.0 | Articles_with_peacock_terms | 156.0 | 156.0 | 0.0 | null |
| 395.0 | Screen_actor_stubs | 34.0 | 10.0 | 0.0 | null |
| 396.0 | Gang_films | 4.0 | 4.0 | 0.0 | null |
| 398.0 | Elk_County,_Kansas | 11.0 | 6.0 | 0.0 | null |
| 399.0 | G8 | 0.0 | 0.0 | 0.0 | null |
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| 401.0 | Buenos_Aires | 53.0 | 19.0 | 2.0 | null |
| 402.0 | Administrative_backlog | 18.0 | 6.0 | 0.0 | null |
| 403.0 | Stub-Class_United_States_military_history_articles | 6719.0 | 0.0 | 0.0 | null |
| 404.0 | Stub-Class_American_Civil_War_articles | 1761.0 | 0.0 | 0.0 | null |
| 405.0 | Candidates_for_speedy_deletion_by_user | 1.0 | 1.0 | 0.0 | null |
| 406.0 | Star_Fleet_Universe | 14.0 | 1.0 | 0.0 | null |
| 407.0 | Star_Trek_templates | 44.0 | 1.0 | 0.0 | null |
| 408.0 | Wikipedia_templates_for_deletion | 0.0 | 0.0 | 0.0 | null |
| 410.0 | Articles_lacking_sources_from_March_2007 | 555.0 | 0.0 | 0.0 | null |
| 411.0 | Rivers_of_Ecuador | 88.0 | 2.0 | 0.0 | null |
| 412.0 | Rivers_of_Peru | 130.0 | 3.0 | 0.0 | null |
| 414.0 | Bosnian_and_Herzegovinian_martial_artists | 0.0 | 0.0 | 0.0 | null |
| 415.0 | Bosnia_and_Herzegovina_martial_artists | 11.0 | 11.0 | 0.0 | null |
| 416.0 | School_districts_in_Georgia_(U.S._state) | 188.0 | 3.0 | 3.0 | null |
| 417.0 | Columbia_County,_Georgia | 9.0 | 6.0 | 0.0 | null |
| 419.0 | Radio_stations_in_Montana | 262.0 | 8.0 | 0.0 | null |
| 420.0 | Radio_station_logos | 7732.0 | 3.0 | 7729.0 | null |
| 421.0 | Elephants | 69.0 | 16.0 | 0.0 | null |
| 422.0 | Fauna_of_Angola | 14.0 | 3.0 | 0.0 | null |
| 423.0 | Fauna_of_Burkina_Faso | 9.0 | 3.0 | 0.0 | null |
| 424.0 | Fauna_of_Ethiopia | 46.0 | 5.0 | 0.0 | null |
| 425.0 | Fauna_of_Namibia | 37.0 | 5.0 | 0.0 | null |
| 426.0 | Fauna_of_South_Africa | 72.0 | 8.0 | 0.0 | null |
| 427.0 | Fauna_of_Sudan | 11.0 | 5.0 | 0.0 | null |
| 428.0 | Fauna_of_Tanzania | 83.0 | 5.0 | 0.0 | null |
| 429.0 | Fauna_of_West_Africa | 20.0 | 20.0 | 0.0 | null |
| 430.0 | Fauna_of_Zambia | 51.0 | 4.0 | 0.0 | null |
| 431.0 | Fauna_of_the_Democratic_Republic_of_the_Congo | 28.0 | 4.0 | 0.0 | null |
| 432.0 | Fauna_of_the_Republic_of_the_Congo | 63.0 | 3.0 | 0.0 | null |
| 433.0 | Fauna_of_the_Sahara | 39.0 | 2.0 | 0.0 | null |
| 434.0 | Mammals_of_Africa | 78.0 | 23.0 | 0.0 | null |
| 435.0 | Mammals_of_Kenya | 108.0 | 0.0 | 0.0 | null |
| 437.0 | Vulnerable_species | 15.0 | 10.0 | 0.0 | null |
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| 441.0 | Economy_of_Poland | 43.0 | 24.0 | 0.0 | null |
| 442.0 | Terminator:_The_Sarah_Connor_Chronicles | 7.0 | 3.0 | 0.0 | null |
| 443.0 | All_dead-end_pages | 1.0 | 0.0 | 0.0 | null |
| 445.0 | Country_radio_stations_in_the_United_States | 1414.0 | 6.0 | 0.0 | null |
| 446.0 | Missoula_County,_Montana | 15.0 | 6.0 | 0.0 | null |
| 447.0 | Montana_radio_station_stubs | 159.0 | 0.0 | 0.0 | null |
| 449.0 | Republika_Srpska | 32.0 | 17.0 | 0.0 | null |
| 450.0 | Subdivisions_of_Bosnia_and_Herzegovina | 13.0 | 7.0 | 0.0 | null |
| 452.0 | WikiProject_Radio_Stations | 4.0 | 2.0 | 0.0 | null |
| 453.0 | Stub-Class_Montana_articles | 2752.0 | 0.0 | 0.0 | null |
| 454.0 | History_of_North_America | 74.0 | 35.0 | 0.0 | null |
| 455.0 | Flora_of_Cambodia | 102.0 | 3.0 | 0.0 | null |
| 456.0 | Post-apocalyptic_fiction | 48.0 | 16.0 | 0.0 | null |
| 458.0 | Low-importance_plant_articles | 81288.0 | 1.0 | 0.0 | null |
| 461.0 | Albinism | 18.0 | 5.0 | 0.0 | null |
| 462.0 | American_college_basketball_templates | 33.0 | 3.0 | 0.0 | null |
| 463.0 | Acronyms | 64.0 | 3.0 | 0.0 | null |
| 465.0 | Anti-communism | 143.0 | 31.0 | 0.0 | null |
| 469.0 | Cold_War_treaties | 121.0 | 39.0 | 0.0 | null |
| 470.0 | Foreign_relations_of_the_Soviet_Union | 163.0 | 36.0 | 0.0 | null |
| 471.0 | International_military_organizations | 39.0 | 6.0 | 0.0 | null |
| 473.0 | Military_alliances | 17.0 | 2.0 | 0.0 | null |
| 475.0 | NATO | 64.0 | 19.0 | 0.0 | null |
| 476.0 | Organisations_based_in_Belgium | 30.0 | 12.0 | 1.0 | null |
| 477.0 | Organizations_established_in_1949 | 155.0 | 15.0 | 0.0 | null |
| 478.0 | Supraorganizations | 101.0 | 19.0 | 0.0 | null |
| 479.0 | Wikipedia_articles_in_need_of_updating | 175.0 | 175.0 | 0.0 | null |
| 480.0 | Serial_killer_films | 21.0 | 9.0 | 0.0 | null |
| 481.0 | Country_radio_stations_in_Canada | 128.0 | 0.0 | 0.0 | null |
| 482.0 | Hentai | 28.0 | 9.0 | 0.0 | null |
| 483.0 | Mid-importance_Mountain_articles | 2455.0 | 0.0 | 0.0 | null |
| 484.0 | Low-importance_Mountain_articles | 29415.0 | 0.0 | 0.0 | null |
| 485.0 | Autobiographical_articles | 169.0 | 169.0 | 0.0 | null |
| 490.0 | Surenos | 0.0 | 0.0 | 0.0 | null |
| 491.0 | The_Bold_and_the_Beautiful | 4.0 | 2.0 | 0.0 | null |
| 492.0 | Tragulidae | 0.0 | 0.0 | 0.0 | null |
| 493.0 | Polish_language | 49.0 | 17.0 | 0.0 | null |
| 495.0 | 2007_singles | 2012.0 | 1.0 | 0.0 | null |
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| 498.0 | Thrones | 20.0 | 1.0 | 0.0 | null |
| 499.0 | Trinidadian_and_Tobagonian_martial_artists | 0.0 | 0.0 | 0.0 | null |
| 500.0 | Trinidad_and_Tobago_martial_artists | 9.0 | 9.0 | 0.0 | null |
| 501.0 | Algiers | 30.0 | 14.0 | 0.0 | null |
| 502.0 | Unassessed_biography_(musicians)_articles | 4828.0 | 0.0 | 0.0 | null |
| 504.0 | Food_ingredient_stubs | 142.0 | 1.0 | 0.0 | null |
| 505.0 | 1863_births | 3169.0 | 0.0 | 0.0 | null |
| 506.0 | 1900_deaths | 1969.0 | 2.0 | 0.0 | null |
| 507.0 | American_folklore | 286.0 | 31.0 | 0.0 | null |
| 508.0 | Disasters_in_Mississippi | 7.0 | 4.0 | 0.0 | null |
| 509.0 | People_from_Missouri | 111.0 | 13.0 | 0.0 | null |
| 510.0 | People_in_rail_transport | 22.0 | 14.0 | 0.0 | null |
| 512.0 | Railway_accidents_in_the_United_States | 0.0 | 0.0 | 0.0 | null |
| 513.0 | Canadian_indie_rock_groups | 354.0 | 0.0 | 0.0 | null |
| 516.0 | Swindon_Town_F.C. | 28.0 | 7.0 | 9.0 | null |
| 520.0 | 2006_albums | 3202.0 | 11.0 | 0.0 | null |
| 522.0 | Stillste_Stund_albums | 6.0 | 0.0 | 0.0 | null |
| 524.0 | Start-Class_language_articles | 3627.0 | 0.0 | 0.0 | null |
| 526.0 | Maximum_Ride | 10.0 | 0.0 | 0.0 | null |
| 527.0 | Biography_(musicians)_articles_by_quality | 20.0 | 18.0 | 0.0 | null |
| 530.0 | Habits | 34.0 | 5.0 | 0.0 | null |
| 531.0 | Personal_development | 112.0 | 19.0 | 0.0 | null |
| 532.0 | Self | 83.0 | 12.0 | 0.0 | null |
| 535.0 | G-Unit_Records_albums | 12.0 | 1.0 | 0.0 | null |
| 536.0 | G-Unit_albums | 7.0 | 0.0 | 0.0 | null |
| 537.0 | Carthage | 70.0 | 10.0 | 0.0 | null |
| 538.0 | 1936_births | 10149.0 | 0.0 | 0.0 | null |
| 539.0 | 2002_deaths | 6800.0 | 4.0 | 0.0 | null |
| 540.0 | American_children's_writers | 2006.0 | 12.0 | 0.0 | null |
| 541.0 | Antioch_College_alumni | 263.0 | 0.0 | 0.0 | null |
| 543.0 | Edgar_Award_winners | 395.0 | 1.0 | 0.0 | null |
| 544.0 | Laura_Ingalls_Wilder_Medal_winners | 21.0 | 0.0 | 0.0 | null |
| 545.0 | MacArthur_Fellows | 1068.0 | 0.0 | 0.0 | null |
| 546.0 | Newbery_Medal_winners | 95.0 | 0.0 | 0.0 | null |
| 547.0 | People_from_Yellow_Springs,_Ohio | 46.0 | 0.0 | 0.0 | null |
| 550.0 | Available_translators_in_Wikipedia | 175.0 | 167.0 | 0.0 | null |
| 551.0 | Translators_pt-en | 111.0 | 0.0 | 0.0 | null |
| 552.0 | University_of_Ottawa | 45.0 | 5.0 | 2.0 | null |
| 555.0 | All_orphaned_articles | 80822.0 | 0.0 | 0.0 | null |
| 556.0 | Articles_lacking_reliable_references_from_March_2008 | 352.0 | 0.0 | 0.0 | null |
| 557.0 | American_screen_actor,_1920s_birth_stubs | 67.0 | 0.0 | 0.0 | null |
| 558.0 | YouTube_videos | 5.0 | 5.0 | 0.0 | null |
| 559.0 | YouTube | 93.0 | 11.0 | 0.0 | null |
| 560.0 | Wikipedia_style_guidelines | 6.0 | 2.0 | 0.0 | null |
| 561.0 | Austrian_literature | 34.0 | 14.0 | 0.0 | null |
| 562.0 | Austrian_novels | 51.0 | 9.0 | 0.0 | null |
| 563.0 | Naruto_characters | 20.0 | 1.0 | 0.0 | null |
| 564.0 | Ancient_Roman_people_stubs | 454.0 | 2.0 | 0.0 | null |
| 565.0 | Ancient_Roman_women | 10.0 | 10.0 | 0.0 | null |
| 566.0 | Kosher_food | 68.0 | 11.0 | 0.0 | null |
| 569.0 | Companies_based_in_Redmond,_Washington | 29.0 | 1.0 | 0.0 | null |
| 570.0 | Companies_established_in_1889 | 17.0 | 15.0 | 0.0 | null |
| 572.0 | Companies_listed_on_the_Frankfurt_Stock_Exchange | 107.0 | 1.0 | 0.0 | null |
| 573.0 | Companies_listed_on_the_Tokyo_Stock_Exchange | 571.0 | 32.0 | 0.0 | null |
| 574.0 | Companies_of_Japan | 35.0 | 25.0 | 0.0 | null |
| 575.0 | Entertainment_Software_Association | 7.0 | 1.0 | 0.0 | null |
| 577.0 | Nintendo | 58.0 | 22.0 | 0.0 | null |
| 578.0 | Video_game_publishers | 627.0 | 56.0 | 0.0 | null |
| 584.0 | Coastal_cities | 0.0 | 0.0 | 0.0 | null |
| 585.0 | Comarques_of_the_Valencian_Community | 73.0 | 36.0 | 0.0 | null |
| 586.0 | Former_national_capitals | 110.0 | 26.0 | 0.0 | null |
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| 589.0 | Port_cities_of_the_Mediterranean_Sea | 31.0 | 23.0 | 0.0 | null |
| 590.0 | Roman_sites_in_Spain | 67.0 | 7.0 | 0.0 | null |
| 592.0 | Valencia | 22.0 | 13.0 | 1.0 | null |
| 593.0 | WikiProject_Geography_of_Canada_articles | 2.0 | 2.0 | 0.0 | null |
| 594.0 | B-Class_Geography_of_Canada_articles | 246.0 | 0.0 | 0.0 | null |
| 595.0 | Mid-importance_Geography_of_Canada_articles | 3851.0 | 0.0 | 0.0 | null |
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| 597.0 | Mid-importance_Canada-related_articles | 12819.0 | 0.0 | 0.0 | null |
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| 603.0 | Unassessed_rail_transport_articles | 2197.0 | 6.0 | 0.0 | null |
| 604.0 | Unknown-importance_rail_transport_articles | 8466.0 | 3.0 | 0.0 | null |
| 605.0 | Unassessed_UK_Railways_articles | 20.0 | 0.0 | 0.0 | null |
| 606.0 | Unknown-importance_UK_Railways_articles | 132.0 | 0.0 | 0.0 | null |
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| 613.0 | African-American_singers | 14.0 | 9.0 | 0.0 | null |
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| 618.0 | American_rhythm_and_blues_singers | 433.0 | 5.0 | 0.0 | null |
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| 1049.0 | Mid-importance_chemicals_articles | 2158.0 | 0.0 | 0.0 | null |
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| 1137.0 | Stub-Class_Ships_articles | 8491.0 | 0.0 | 0.0 | null |
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| 1179.0 | Unknown-importance_chemistry_articles | 0.0 | 0.0 | 0.0 | null |
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| 1184.0 | Unknown-importance_African_diaspora_articles | 3609.0 | 0.0 | 0.0 | null |
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| 1186.0 | 2008 | 61.0 | 42.0 | 0.0 | null |
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| 1192.0 | German_history_stubs | 570.0 | 6.0 | 0.0 | null |
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| 1194.0 | 1980_births | 15963.0 | 0.0 | 0.0 | null |
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| 1196.0 | Northern_Illinois_University_alumni | 213.0 | 1.0 | 0.0 | null |
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| 1200.0 | B-Class_France_articles | 2174.0 | 2.0 | 0.0 | null |
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| 1225.0 | Lacrosse | 23.0 | 19.0 | 0.0 | null |
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| 1256.0 | FA-Class_military_history_articles | 1377.0 | 2.0 | 0.0 | null |
| 1258.0 | FA-Class_weaponry_articles | 43.0 | 0.0 | 0.0 | null |
| 1259.0 | Military_history_articles_used_on_portals | 179.0 | 0.0 | 0.0 | null |
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| 1263.0 | Weaponry_task_force_articles | 11494.0 | 2.0 | 0.0 | null |
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| 1291.0 | Comics_articles_needing_issue_citations | 1030.0 | 0.0 | 0.0 | null |
| 1294.0 | Hungary | 18.0 | 16.0 | 0.0 | null |
Next, let us check that we don't have any corrupt records:
readFromCSV.createOrReplaceTempView("categories")
SELECT * FROM categories WHERE _corrupt_record IS NOT NULL
Query returned no results - all our data appears to have been read in correctly.
Let us finally write all this data to the Delta Lake instead of having it sit in a CSV.
SELECT cat_id, cat_title, cat_pages, cat_subcats, cat_files FROM categories WHERE _corrupt_record IS NULL
| cat_id | cat_title | cat_pages | cat_subcats | cat_files |
|---|---|---|---|---|
| 2.0 | Unprintworthy_redirects | 1545623.0 | 20.0 | 0.0 |
| 3.0 | Computer_storage_devices | 89.0 | 11.0 | 0.0 |
| 7.0 | Unknown-importance_Animation_articles | 279.0 | 21.0 | 0.0 |
| 8.0 | Low-importance_Animation_articles | 14235.0 | 21.0 | 0.0 |
| 9.0 | Vietnam_stubs | 303.0 | 10.0 | 0.0 |
| 10.0 | Rivers_of_Vietnam | 103.0 | 3.0 | 0.0 |
| 12.0 | All_articles_with_unsourced_statements | 472556.0 | 0.0 | 0.0 |
| 14.0 | Wikipedia_articles_needing_clarification | 195.0 | 195.0 | 0.0 |
| 15.0 | Articles_needing_additional_references_from_January_2008 | 1237.0 | 0.0 | 0.0 |
| 16.0 | Comedy | 96.0 | 29.0 | 0.0 |
| 17.0 | Sociolinguistics | 255.0 | 30.0 | 0.0 |
| 18.0 | Figures_of_speech | 132.0 | 13.0 | 0.0 |
| 20.0 | NASCAR_teams | 130.0 | 3.0 | 0.0 |
| 21.0 | Muhammad_Ali | 19.0 | 4.0 | 0.0 |
| 22.0 | Politics_and_government_work_group_articles | 236015.0 | 4.0 | 0.0 |
| 23.0 | Wikipedia_requested_photographs_of_politicians_and_government-people | 11918.0 | 1.0 | 0.0 |
| 24.0 | Stub-Class_biography_(politics_and_government)_articles | 123773.0 | 0.0 | 0.0 |
| 26.0 | Stub-Class_biography_articles | 1039170.0 | 10.0 | 0.0 |
| 27.0 | Unassessed_biography_articles | 47285.0 | 10.0 | 0.0 |
| 29.0 | High-importance_Animation_articles | 279.0 | 21.0 | 0.0 |
| 31.0 | AfD_debates | 479.0 | 12.0 | 0.0 |
| 32.0 | Articles_with_unsourced_statements | 200.0 | 194.0 | 0.0 |
| 35.0 | Self-published_work | 106467.0 | 1.0 | 106465.0 |
| 36.0 | Geography | 102.0 | 37.0 | 0.0 |
| 37.0 | Images_without_source | 0.0 | 0.0 | 0.0 |
| 38.0 | Candidates_for_speedy_deletion | 17.0 | 2.0 | 0.0 |
| 40.0 | All_non-free_media | 711706.0 | 1.0 | 711705.0 |
| 41.0 | Wikipedia_requested_photographs_of_sportspeople | 15123.0 | 1.0 | 0.0 |
| 42.0 | Thirty_Years'_War | 57.0 | 9.0 | 0.0 |
| 44.0 | African-American_history | 80.0 | 33.0 | 1.0 |
| 46.0 | History_of_Alabama | 76.0 | 29.0 | 0.0 |
| 47.0 | Groups_of_World_War_II | 32.0 | 1.0 | 0.0 |
| 48.0 | Congressional_Gold_Medal_recipients | 425.0 | 3.0 | 7.0 |
| 49.0 | United_States_Army_officers | 3642.0 | 18.0 | 0.0 |
| 50.0 | Tuskegee_University | 27.0 | 7.0 | 0.0 |
| 51.0 | Military_units_and_formations_of_the_United_States_in_World_War_II | 15.0 | 7.0 | 0.0 |
| 52.0 | People_from_Tuskegee,_Alabama | 56.0 | 2.0 | 0.0 |
| 53.0 | Tuskegee_Airmen | 156.0 | 0.0 | 0.0 |
| 54.0 | Chinese_Methodists | 12.0 | 3.0 | 0.0 |
| 55.0 | Chinese_Protestants | 34.0 | 15.0 | 0.0 |
| 56.0 | Shel_Silverstein_songs | 6.0 | 0.0 | 0.0 |
| 57.0 | Articles_lacking_sources | 209.0 | 207.0 | 0.0 |
| 58.0 | All_articles_lacking_sources | 135448.0 | 0.0 | 0.0 |
| 59.0 | Radio_stations_in_Saskatchewan | 41.0 | 7.0 | 0.0 |
| 60.0 | Western_Canada_radio_station_stubs | 24.0 | 4.0 | 0.0 |
| 61.0 | Saskatchewan_stubs | 94.0 | 5.0 | 0.0 |
| 62.0 | Multi-level_marketing | 6.0 | 2.0 | 0.0 |
| 64.0 | Filipino_Wikipedians | 725.0 | 3.0 | 0.0 |
| 65.0 | Wikipedians_interested_in_mapmaking | 318.0 | 0.0 | 0.0 |
| 66.0 | Wikipedians_interested_in_maps | 1245.0 | 1.0 | 0.0 |
| 67.0 | Wikipedians_who_listen_to_world_music | 294.0 | 4.0 | 0.0 |
| 68.0 | Wikipedians_interested_in_architecture | 997.0 | 2.0 | 0.0 |
| 69.0 | Wikipedians_interested_in_art | 997.0 | 17.0 | 0.0 |
| 70.0 | Wikipedian_ballroom_dancers | 84.0 | 0.0 | 0.0 |
| 71.0 | Wikipedian_dancers | 290.0 | 5.0 | 0.0 |
| 72.0 | Brasília | 17.0 | 12.0 | 0.0 |
| 74.0 | Sligo | 0.0 | 0.0 | 0.0 |
| 75.0 | Puerto_Rican_people | 31.0 | 29.0 | 0.0 |
| 77.0 | WikiProject_Canadian_communities_articles | 2.0 | 2.0 | 0.0 |
| 78.0 | B-Class_Canadian_communities_articles | 139.0 | 0.0 | 0.0 |
| 79.0 | Mid-importance_Canadian_communities_articles | 3425.0 | 0.0 | 0.0 |
| 81.0 | Importance_or_significance_not_asserted_pages_for_speedy_deletion | 0.0 | 0.0 | 0.0 |
| 82.0 | The_Taming_of_the_Shrew | 8.0 | 1.0 | 0.0 |
| 83.0 | Edwards_County,_Kansas | 10.0 | 4.0 | 0.0 |
| 86.0 | Unknown-importance_Olympics_articles | 18779.0 | 0.0 | 0.0 |
| 87.0 | WikiProject_Olympics_articles | 191404.0 | 2.0 | 0.0 |
| 88.0 | Stub-Class_Canadian_communities_articles | 8334.0 | 0.0 | 0.0 |
| 89.0 | Articles_lacking_sources_from_December_2007 | 428.0 | 0.0 | 0.0 |
| 90.0 | Kingston,_Jamaica | 29.0 | 9.0 | 0.0 |
| 94.0 | Wikipedians_interested_in_Japanese_mythology | 40.0 | 1.0 | 0.0 |
| 95.0 | WikiProject_Japanese_mythology_members | 21.0 | 0.0 | 0.0 |
| 96.0 | WikiProject_Statistics_members | 111.0 | 0.0 | 0.0 |
| 98.0 | 1978 | 44.0 | 37.0 | 0.0 |
| 102.0 | Musicians_work_group_articles | 163180.0 | 4.0 | 0.0 |
| 103.0 | Wikipedia_requested_photographs_of_musicians | 12467.0 | 0.0 | 0.0 |
| 104.0 | Start-Class_biography_(musicians)_articles | 69851.0 | 0.0 | 0.0 |
| 106.0 | Musicians_work_group_articles_needing_infoboxes | 1277.0 | 0.0 | 0.0 |
| 107.0 | Biography_articles_without_infoboxes | 23294.0 | 9.0 | 0.0 |
| 108.0 | Start-Class_biography_articles | 667451.0 | 10.0 | 0.0 |
| 109.0 | Punk_song_stubs | 68.0 | 0.0 | 0.0 |
| 111.0 | Planetary_nebulae | 125.0 | 1.0 | 0.0 |
| 112.0 | Methane | 52.0 | 3.0 | 0.0 |
| 113.0 | Economy_of_Russia | 93.0 | 22.0 | 0.0 |
| 114.0 | Climate_of_Texas | 7.0 | 0.0 | 0.0 |
| 115.0 | Transport_in_Burma | 0.0 | 0.0 | 0.0 |
| 116.0 | Townships_in_Kansas | 817.0 | 2.0 | 0.0 |
| 117.0 | Kansas_geography_stubs | 1027.0 | 6.0 | 0.0 |
| 118.0 | Series_of_children's_books | 662.0 | 84.0 | 0.0 |
| 121.0 | List-Class_Animation_articles | 1556.0 | 8.0 | 0.0 |
| 122.0 | Start-Class_Animation_articles | 7387.0 | 8.0 | 0.0 |
| 123.0 | Wikipedia_references_cleanup | 169.0 | 166.0 | 0.0 |
| 125.0 | Theorists | 20.0 | 18.0 | 0.0 |
| 126.0 | National_lower_houses | 116.0 | 27.0 | 0.0 |
| 127.0 | Spoken_articles | 1665.0 | 0.0 | 0.0 |
| 128.0 | United_States_House_of_Representatives | 57.0 | 11.0 | 0.0 |
| 129.0 | People_by_nationality | 246.0 | 246.0 | 0.0 |
| 130.0 | WikiProject_Molecular_and_Cellular_Biology | 17.0 | 7.0 | 0.0 |
| 131.0 | Primate_stubs | 44.0 | 3.0 | 0.0 |
| 132.0 | Old_World_monkeys | 10.0 | 3.0 | 0.0 |
| 133.0 | Fauna_of_Thailand | 17.0 | 7.0 | 0.0 |
| 134.0 | Fighter_aircraft | 88.0 | 54.0 | 0.0 |
| 135.0 | Transport_in_Croatia | 14.0 | 12.0 | 0.0 |
| 136.0 | Requests_for_unblock | 93.0 | 4.0 | 0.0 |
| 137.0 | Volcanic_belts | 35.0 | 10.0 | 0.0 |
| 139.0 | Transport_in_Denmark | 25.0 | 18.0 | 0.0 |
| 140.0 | 2000s_single_stubs | 819.0 | 1.0 | 0.0 |
| 141.0 | Canaan | 60.0 | 11.0 | 0.0 |
| 142.0 | Women_writers | 12.0 | 11.0 | 0.0 |
| 143.0 | Start-Class_biography_(military)_articles | 43358.0 | 0.0 | 0.0 |
| 144.0 | Military_biography_work_group_articles | 83368.0 | 5.0 | 0.0 |
| 145.0 | Biography_articles_with_listas_parameter | 0.0 | 0.0 | 0.0 |
| 147.0 | Stub-Class_biography_(military)_articles | 20981.0 | 0.0 | 0.0 |
| 150.0 | Medieval_literature | 292.0 | 27.0 | 0.0 |
| 152.0 | Transport_in_Lithuania | 14.0 | 13.0 | 0.0 |
| 154.0 | Stub-Class_Animation_articles | 4161.0 | 8.0 | 0.0 |
| 156.0 | Articles_for_deletion | 551.0 | 1.0 | 0.0 |
| 157.0 | All_articles_to_be_expanded | 70437.0 | 0.0 | 0.0 |
| 159.0 | Wikipedian_college_students | 1401.0 | 1.0 | 0.0 |
| 160.0 | Images_lacking_a_description | 0.0 | 0.0 | 0.0 |
| 161.0 | Uploader_unsure_of_copyright_status | 5.0 | 1.0 | 4.0 |
| 164.0 | All_images_with_unknown_copyright_status | 0.0 | 0.0 | 0.0 |
| 165.0 | Attack_pages_for_speedy_deletion | 0.0 | 0.0 | 0.0 |
| 167.0 | Vietnamese_Confucianists | 41.0 | 0.0 | 0.0 |
| 169.0 | Transport_in_Mauritius | 11.0 | 8.0 | 0.0 |
| 170.0 | Mountain_biking | 49.0 | 15.0 | 0.0 |
| 171.0 | 1939_births | 10840.0 | 0.0 | 0.0 |
| 172.0 | Articles_lacking_sources_from_March_2008 | 645.0 | 0.0 | 0.0 |
| 173.0 | Living_people | 1049898.0 | 2.0 | 0.0 |
| 175.0 | Transport_in_Mozambique | 15.0 | 9.0 | 0.0 |
| 176.0 | Lists_of_railway_stations_in_the_United_Kingdom | 27.0 | 1.0 | 0.0 |
| 177.0 | North_America | 29.0 | 21.0 | 0.0 |
| 178.0 | Non-fiction_writers | 20.0 | 12.0 | 0.0 |
| 180.0 | Solid-state_computer_storage_media | 59.0 | 1.0 | 0.0 |
| 181.0 | USB | 95.0 | 1.0 | 0.0 |
| 182.0 | Subdivisions_of_Kosovo | 7.0 | 4.0 | 0.0 |
| 184.0 | Articles_needing_additional_references_from_June_2007 | 603.0 | 0.0 | 0.0 |
| 188.0 | Commonwealth_of_Nations | 65.0 | 25.0 | 0.0 |
| 189.0 | Political_history_of_Australia | 88.0 | 20.0 | 0.0 |
| 190.0 | Political_history_of_Canada | 128.0 | 31.0 | 0.0 |
| 191.0 | Political_history_of_the_United_Kingdom | 160.0 | 64.0 | 0.0 |
| 192.0 | Stub-Class_biography_(musicians)_articles | 60830.0 | 0.0 | 0.0 |
| 193.0 | Odonata | 17.0 | 8.0 | 0.0 |
| 194.0 | Companies_based_in_Vancouver | 220.0 | 9.0 | 0.0 |
| 196.0 | Video_game_developers | 73.0 | 14.0 | 0.0 |
| 197.0 | History_of_the_Kurds | 0.0 | 0.0 | 0.0 |
| 198.0 | Torchwood_episodes | 37.0 | 1.0 | 0.0 |
| 199.0 | Federal_assistance_in_the_United_States | 81.0 | 5.0 | 0.0 |
| 200.0 | Nutrition | 204.0 | 28.0 | 9.0 |
| 202.0 | United_States_Department_of_Agriculture | 304.0 | 6.0 | 0.0 |
| 203.0 | Transport_in_the_Cayman_Islands | 6.0 | 5.0 | 0.0 |
| 204.0 | All_pages_needing_cleanup | 34785.0 | 5.0 | 0.0 |
| 206.0 | Military_of_the_United_Kingdom | 127.0 | 60.0 | 2.0 |
| 207.0 | Start-Class_Canadian_communities_articles | 4008.0 | 0.0 | 0.0 |
| 209.0 | Food_and_drink_stubs | 14.0 | 14.0 | 0.0 |
| 210.0 | Moons | 39.0 | 17.0 | 1.0 |
| 211.0 | Miscellaneous_pages_for_deletion | 3.0 | 0.0 | 0.0 |
| 212.0 | 1733_establishments | 10.0 | 10.0 | 0.0 |
| 213.0 | 1776_disestablishments | 10.0 | 7.0 | 0.0 |
| 214.0 | British_North_America | 63.0 | 13.0 | 0.0 |
| 216.0 | Former_British_colonies | 11.0 | 8.0 | 0.0 |
| 217.0 | History_of_Georgia_(U.S._state) | 108.0 | 41.0 | 0.0 |
| 218.0 | Thirteen_Colonies | 28.0 | 8.0 | 0.0 |
| 219.0 | Wehrmacht | 63.0 | 12.0 | 0.0 |
| 220.0 | Precambrian | 14.0 | 7.0 | 0.0 |
| 221.0 | Start-Class_United_States_military_history_articles | 30836.0 | 0.0 | 0.0 |
| 222.0 | United_States_military_history_task_force_articles | 74074.0 | 3.0 | 0.0 |
| 223.0 | Start-Class_American_Civil_War_articles | 6567.0 | 0.0 | 0.0 |
| 224.0 | American_Civil_War_task_force_articles | 13078.0 | 3.0 | 0.0 |
| 225.0 | Start-Class_military_history_articles | 100531.0 | 2.0 | 0.0 |
| 226.0 | Military_history_articles_with_incomplete_B-Class_checklists | 0.0 | 0.0 | 0.0 |
| 227.0 | Literature_stubs | 132.0 | 24.0 | 0.0 |
| 228.0 | Transport_in_the_Netherlands_Antilles | 3.0 | 2.0 | 0.0 |
| 230.0 | Top_Gear | 39.0 | 3.0 | 0.0 |
| 231.0 | Wikipedia_cleanup | 41.0 | 20.0 | 0.0 |
| 232.0 | Stubs | 1.0 | 0.0 | 0.0 |
| 233.0 | Articles_needing_additional_references_from_September_2007 | 729.0 | 0.0 | 0.0 |
| 235.0 | Transport_in_Vanuatu | 7.0 | 5.0 | 0.0 |
| 236.0 | Sumer | 53.0 | 13.0 | 0.0 |
| 237.0 | Sumerian_cities | 33.0 | 6.0 | 0.0 |
| 239.0 | New_York_City_Department_of_Education | 36.0 | 3.0 | 0.0 |
| 240.0 | Screenshots_of_television | 16234.0 | 24.0 | 16210.0 |
| 241.0 | Al_Gore | 49.0 | 4.0 | 0.0 |
| 243.0 | American_male_singers | 1474.0 | 15.0 | 0.0 |
| 244.0 | Pakistani_names | 103.0 | 8.0 | 0.0 |
| 246.0 | Punjabi_tribes | 168.0 | 16.0 | 0.0 |
| 247.0 | Edina,_Minnesota | 15.0 | 3.0 | 0.0 |
| 249.0 | Agriculture | 140.0 | 42.0 | 0.0 |
| 250.0 | Molecular_physics | 90.0 | 10.0 | 0.0 |
| 251.0 | Wikipedia_controversial_topics | 3500.0 | 3.0 | 0.0 |
| 253.0 | Sony_Computer_Entertainment | 0.0 | 0.0 | 0.0 |
| 254.0 | Redirects_from_merges | 58346.0 | 4.0 | 0.0 |
| 256.0 | Leicester_City_F.C. | 28.0 | 9.0 | 2.0 |
| 257.0 | English_football_club_stubs | 88.0 | 2.0 | 0.0 |
| 258.0 | Bosnian_and_Herzegovinian_sportspeople | 0.0 | 0.0 | 0.0 |
| 259.0 | Bosnia_and_Herzegovina_sportspeople | 11.0 | 8.0 | 0.0 |
| 260.0 | Articles_that_include_images_for_deletion | 2.0 | 2.0 | 0.0 |
| 261.0 | Logging | 87.0 | 8.0 | 0.0 |
| 263.0 | Great_Western_Railway_locomotives | 191.0 | 12.0 | 0.0 |
| 265.0 | Chinese_Confucianists | 105.0 | 2.0 | 0.0 |
| 266.0 | Copyright_violations_for_speedy_deletion | 0.0 | 0.0 | 0.0 |
| 267.0 | Articles_lacking_sources_from_February_2008 | 426.0 | 0.0 | 0.0 |
| 268.0 | Ben_10 | 31.0 | 2.0 | 0.0 |
| 269.0 | Dessert_stubs | 538.0 | 2.0 | 0.0 |
| 270.0 | Cookies | 134.0 | 6.0 | 0.0 |
| 271.0 | Republican_Party_(United_States)_organizations | 85.0 | 4.0 | 0.0 |
| 273.0 | Biography_articles_of_living_people | 1099347.0 | 1.0 | 0.0 |
| 274.0 | Politics_and_government_work_group_articles_needing_infoboxes | 4394.0 | 0.0 | 0.0 |
| 275.0 | Biography_articles_without_listas_parameter | 333.0 | 0.0 | 0.0 |
| 278.0 | Ceramic_materials | 130.0 | 5.0 | 0.0 |
| 279.0 | 1980s_novel_stubs | 392.0 | 10.0 | 0.0 |
| 280.0 | Novel_stubs | 100.0 | 37.0 | 0.0 |
| 281.0 | Water_parks | 31.0 | 5.0 | 0.0 |
| 282.0 | Special_Operations_Executive | 63.0 | 3.0 | 0.0 |
| 283.0 | Theories | 66.0 | 11.0 | 0.0 |
| 284.0 | Greece | 20.0 | 16.0 | 0.0 |
| 285.0 | Wikipedians_interested_in_video_games | 1772.0 | 22.0 | 0.0 |
| 287.0 | Wikipedians_interested_in_law | 1300.0 | 11.0 | 0.0 |
| 288.0 | Wikipedians_interested_in_law_enforcement | 19.0 | 3.0 | 0.0 |
| 289.0 | Wikipedians_interested_in_politics | 3351.0 | 33.0 | 0.0 |
| 290.0 | Wikipedians_with_JD_degrees | 179.0 | 0.0 | 0.0 |
| 292.0 | 2008_albums | 3063.0 | 11.0 | 0.0 |
| 293.0 | Articles_needing_additional_references_from_February_2008 | 1058.0 | 0.0 | 0.0 |
| 294.0 | Political_science_terms | 0.0 | 0.0 | 0.0 |
| 295.0 | Political_theories | 333.0 | 53.0 | 0.0 |
| 296.0 | Politics_of_Scotland | 98.0 | 25.0 | 0.0 |
| 298.0 | Articles_with_topics_of_unclear_notability | 179.0 | 179.0 | 0.0 |
| 300.0 | Arctic_Monkeys | 14.0 | 5.0 | 0.0 |
| 301.0 | American_businesspeople | 1313.0 | 20.0 | 0.0 |
| 302.0 | National_Basketball_Association_executives | 119.0 | 5.0 | 0.0 |
| 304.0 | San_Antonio_Spurs | 25.0 | 9.0 | 0.0 |
| 305.0 | Seattle_SuperSonics | 21.0 | 9.0 | 0.0 |
| 306.0 | Year_of_birth_missing_(living_people) | 148543.0 | 0.0 | 0.0 |
| 307.0 | People_from_Chaco_Province | 12.0 | 5.0 | 0.0 |
| 308.0 | Argentine_footballers | 5842.0 | 4.0 | 0.0 |
| 310.0 | Arsenal_de_Sarandí_footballers | 345.0 | 0.0 | 0.0 |
| 311.0 | Cienciano_footballers | 180.0 | 0.0 | 0.0 |
| 312.0 | Ayyubid_dynasty | 16.0 | 8.0 | 0.0 |
| 313.0 | Goldfish_breeds | 28.0 | 0.0 | 0.0 |
| 314.0 | WikiProject_Chad_articles | 2471.0 | 2.0 | 0.0 |
| 315.0 | Stub-Class_Chad_articles | 946.0 | 0.0 | 0.0 |
| 316.0 | Unknown-importance_Chad_articles | 958.0 | 0.0 | 0.0 |
| 317.0 | Stub-Class_Africa_articles | 59570.0 | 2.0 | 0.0 |
| 318.0 | Unknown-importance_Africa_articles | 40366.0 | 2.0 | 0.0 |
| 319.0 | Stub-Class_African_military_history_articles | 1052.0 | 0.0 | 0.0 |
| 320.0 | African_military_history_task_force_articles | 8328.0 | 2.0 | 0.0 |
| 321.0 | Stub-Class_military_history_articles | 25029.0 | 2.0 | 0.0 |
| 322.0 | Structure_of_the_Earth | 67.0 | 9.0 | 0.0 |
| 323.0 | Electromagnetic_spectrum | 40.0 | 10.0 | 0.0 |
| 324.0 | Waves | 104.0 | 12.0 | 0.0 |
| 326.0 | Latin_language | 80.0 | 20.0 | 0.0 |
| 327.0 | Ancient_languages | 26.0 | 17.0 | 0.0 |
| 328.0 | Fusional_languages | 43.0 | 7.0 | 0.0 |
| 329.0 | Languages_of_Italy | 43.0 | 11.0 | 0.0 |
| 330.0 | Languages_of_Vatican_City | 7.0 | 2.0 | 0.0 |
| 331.0 | Latino-Faliscan_languages | 8.0 | 2.0 | 0.0 |
| 332.0 | All_That | 19.0 | 3.0 | 0.0 |
| 333.0 | The_War_of_the_Worlds | 9.0 | 2.0 | 0.0 |
| 336.0 | Anime_and_manga_characters_with_superhuman_strength | 109.0 | 1.0 | 0.0 |
| 337.0 | Anime_and_manga_martial_artists | 0.0 | 0.0 | 0.0 |
| 339.0 | English_women_writers | 414.0 | 7.0 | 0.0 |
| 340.0 | Semi-protected_templates | 0.0 | 0.0 | 0.0 |
| 341.0 | User_warning_templates | 331.0 | 8.0 | 0.0 |
| 342.0 | New_Zealand_Confucianists | 1.0 | 0.0 | 0.0 |
| 343.0 | Free-to-air | 61.0 | 0.0 | 0.0 |
| 344.0 | 2008_deaths | 8528.0 | 4.0 | 0.0 |
| 345.0 | Album_covers | 194252.0 | 4.0 | 194247.0 |
| 346.0 | Rhine_basin | 65.0 | 48.0 | 0.0 |
| 347.0 | Valdosta,_Georgia | 18.0 | 3.0 | 0.0 |
| 348.0 | National_Invitation_Tournament | 92.0 | 2.0 | 0.0 |
| 349.0 | Ghosts | 83.0 | 4.0 | 0.0 |
| 350.0 | Aer_Lingus | 12.0 | 1.0 | 2.0 |
| 351.0 | Deuteromycota | 11.0 | 0.0 | 0.0 |
| 352.0 | Fascism | 87.0 | 23.0 | 0.0 |
| 353.0 | 2006_Atlantic_hurricane_season | 12.0 | 0.0 | 0.0 |
| 354.0 | New_Zealand_people_by_religion | 14.0 | 14.0 | 0.0 |
| 355.0 | Confucianists_by_nationality | 10.0 | 10.0 | 0.0 |
| 356.0 | .NET_programming_languages | 36.0 | 1.0 | 0.0 |
| 357.0 | English_early_modern_theatre_companies | 23.0 | 1.0 | 0.0 |
| 358.0 | All_articles_proposed_for_deletion | 153.0 | 0.0 | 0.0 |
| 360.0 | Brand_name_food_products_stubs | 419.0 | 0.0 | 0.0 |
| 361.0 | Brand_name_snack_foods | 183.0 | 20.0 | 0.0 |
| 364.0 | Certification_marks | 93.0 | 5.0 | 0.0 |
| 365.0 | Energy_in_the_United_States | 78.0 | 18.0 | 0.0 |
| 366.0 | Product_certification | 58.0 | 8.0 | 0.0 |
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| 747.0 | Villages_in_Cardiff | 12.0 | 2.0 | 0.0 |
| 748.0 | 1968_births | 14982.0 | 0.0 | 0.0 |
| 749.0 | People_from_Essex | 272.0 | 53.0 | 0.0 |
| 750.0 | American_rappers | 248.0 | 8.0 | 0.0 |
| 752.0 | Mid-importance_Animation_articles | 1100.0 | 21.0 | 0.0 |
| 754.0 | All_articles_with_dead_external_links | 259712.0 | 0.0 | 0.0 |
| 756.0 | Wikipedia_external_links_cleanup | 110.0 | 110.0 | 0.0 |
| 757.0 | Rugs_and_carpets | 92.0 | 13.0 | 0.0 |
| 759.0 | Little_Britain_characters | 3.0 | 1.0 | 0.0 |
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| 762.0 | User_en | 5066.0 | 27.0 | 0.0 |
| 763.0 | Australia | 19.0 | 15.0 | 0.0 |
| 764.0 | Radio-frequency_identification | 130.0 | 6.0 | 0.0 |
| 765.0 | Neptune | 23.0 | 5.0 | 0.0 |
| 766.0 | Cold_War_weapons_by_country | 12.0 | 11.0 | 0.0 |
| 767.0 | Weapons_of_the_United_States | 40.0 | 24.0 | 0.0 |
| 768.0 | Companies_established_in_2005 | 186.0 | 19.0 | 0.0 |
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| 770.0 | Giza_Plateau | 29.0 | 0.0 | 0.0 |
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| 772.0 | 1996_films | 1490.0 | 19.0 | 0.0 |
| 774.0 | American_films | 33.0 | 32.0 | 0.0 |
| 775.0 | English-language_films | 335.0 | 143.0 | 0.0 |
| 776.0 | Estudios_Churubusco_films | 40.0 | 0.0 | 0.0 |
| 778.0 | Romantic_drama_films | 8.0 | 7.0 | 0.0 |
| 780.0 | Teen_romance_films | 9.0 | 7.0 | 0.0 |
| 782.0 | Protected_areas_of_India | 27.0 | 12.0 | 0.0 |
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| 790.0 | Mating | 45.0 | 5.0 | 0.0 |
| 791.0 | The_Veronicas | 10.0 | 3.0 | 0.0 |
| 792.0 | PSV_Eindhoven | 25.0 | 8.0 | 3.0 |
| 793.0 | Unión_de_Santa_Fe_footballers | 349.0 | 0.0 | 0.0 |
| 795.0 | 1893_births | 5370.0 | 0.0 | 0.0 |
| 796.0 | 1967_deaths | 4630.0 | 2.0 | 0.0 |
| 798.0 | Brooklyn_Robins_players | 237.0 | 0.0 | 0.0 |
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| 800.0 | Chicago_Cubs_players | 1958.0 | 3.0 | 0.0 |
| 801.0 | Major_League_Baseball_coaches | 23.0 | 10.0 | 0.0 |
| 802.0 | Minor_league_baseball_managers | 2250.0 | 67.0 | 0.0 |
| 803.0 | Baseball_shortstop_stubs | 155.0 | 1.0 | 0.0 |
| 804.0 | Portsmouth_F.C. | 20.0 | 8.0 | 0.0 |
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| 806.0 | Skateboarding | 64.0 | 13.0 | 0.0 |
| 807.0 | Expatriate_footballers_in_Argentina | 1504.0 | 1.0 | 0.0 |
| 808.0 | Brighton_&_Hove_Albion_F.C. | 24.0 | 9.0 | 1.0 |
| 809.0 | AFC_Cup | 6.0 | 5.0 | 0.0 |
| 810.0 | Unassessed_Baseball_articles | 654.0 | 1.0 | 0.0 |
| 811.0 | Unknown-importance_Baseball_articles | 8053.0 | 1.0 | 0.0 |
| 812.0 | WikiProject_Baseball_articles | 84678.0 | 6.0 | 0.0 |
| 813.0 | Microbiology | 176.0 | 23.0 | 0.0 |
| 815.0 | Protista | 15.0 | 2.0 | 0.0 |
| 816.0 | Wikipedia_basic_information | 81.0 | 6.0 | 0.0 |
| 817.0 | Articles_with_dead_external_links | 173.0 | 171.0 | 0.0 |
| 818.0 | All_articles_with_broken_or_outdated_citations | 160.0 | 0.0 | 0.0 |
| 821.0 | My_Chemical_Romance | 10.0 | 5.0 | 0.0 |
| 822.0 | Rongorongo | 29.0 | 1.0 | 14.0 |
| 823.0 | 2000s_song_stubs | 146.0 | 7.0 | 0.0 |
| 824.0 | 1891_births | 5295.0 | 0.0 | 0.0 |
| 825.0 | 1964_deaths | 4346.0 | 2.0 | 0.0 |
| 826.0 | Chicago_White_Sox_players | 1840.0 | 1.0 | 0.0 |
| 830.0 | Baseball_center_fielder_stubs | 49.0 | 0.0 | 0.0 |
| 831.0 | Dance-pop_songs | 659.0 | 34.0 | 0.0 |
| 832.0 | Nicole_Scherzinger_songs | 30.0 | 0.0 | 0.0 |
| 835.0 | Self-reference | 29.0 | 5.0 | 0.0 |
| 836.0 | Articles_needing_additional_references | 205.0 | 204.0 | 0.0 |
| 837.0 | 1961_births | 14715.0 | 0.0 | 0.0 |
| 841.0 | Olympic_footballers_of_Yugoslavia | 172.0 | 0.0 | 0.0 |
| 842.0 | Footballers_at_the_1984_Summer_Olympics | 268.0 | 0.0 | 0.0 |
| 843.0 | Olympic_bronze_medalists_for_Yugoslavia | 90.0 | 0.0 | 0.0 |
| 844.0 | Croatian_football_managers | 307.0 | 1.0 | 0.0 |
| 845.0 | High-importance_Green_Bay_Packers_articles | 166.0 | 0.0 | 0.0 |
| 846.0 | Unknown-importance_Green_Bay_Packers_articles | 2.0 | 0.0 | 0.0 |
| 847.0 | Thompson-Nicola_Regional_District | 23.0 | 3.0 | 0.0 |
| 849.0 | American_tennis_coaches | 205.0 | 2.0 | 0.0 |
| 850.0 | Postcolonialism | 69.0 | 9.0 | 0.0 |
| 851.0 | Sexual_fetishism | 109.0 | 11.0 | 0.0 |
| 853.0 | Buildings_and_structures_in_the_San_Francisco_Bay_Area | 26.0 | 22.0 | 0.0 |
| 854.0 | Lighthouses_in_California | 20.0 | 5.0 | 0.0 |
| 856.0 | Construction_and_civil_engineering_companies_by_country | 69.0 | 69.0 | 0.0 |
| 858.0 | Trinidad_and_Tobago_sportspeople | 11.0 | 11.0 | 0.0 |
| 860.0 | Screenshots_of_films | 5083.0 | 8.0 | 5075.0 |
| 861.0 | San_Jose_Earthquakes | 28.0 | 9.0 | 1.0 |
| 862.0 | 1964_births | 15643.0 | 0.0 | 0.0 |
| 863.0 | Swedish_curlers | 7.0 | 7.0 | 0.0 |
| 864.0 | Winter_Olympics_medalists | 5.0 | 5.0 | 0.0 |
| 865.0 | Curlers_at_the_1998_Winter_Olympics | 77.0 | 0.0 | 0.0 |
| 866.0 | Olympic_bronze_medalists_for_Sweden | 466.0 | 0.0 | 0.0 |
| 867.0 | Curling_biography_stubs | 173.0 | 17.0 | 0.0 |
| 868.0 | Swedish_Olympic_medalist_stubs | 544.0 | 1.0 | 0.0 |
| 869.0 | Winter_Olympic_medalist_stubs | 234.0 | 10.0 | 0.0 |
| 870.0 | County_Meath | 31.0 | 17.0 | 0.0 |
| 871.0 | Articles_with_limited_geographic_scope | 204.0 | 204.0 | 0.0 |
| 872.0 | USA-centric | 0.0 | 0.0 | 0.0 |
| 873.0 | Father_Ted_characters | 3.0 | 0.0 | 0.0 |
| 874.0 | Universitario_de_Deportes_footballers | 0.0 | 0.0 | 0.0 |
| 876.0 | Cantonese-language_films | 83.0 | 6.0 | 0.0 |
| 877.0 | Exploitation_films | 50.0 | 17.0 | 0.0 |
| 878.0 | Erotic_thriller_films | 5.0 | 4.0 | 0.0 |
| 879.0 | Articles_lacking_sources_from_January_2008 | 492.0 | 0.0 | 0.0 |
| 880.0 | Sweeteners | 0.0 | 0.0 | 0.0 |
| 881.0 | Japanese_Confucianists | 38.0 | 0.0 | 0.0 |
| 882.0 | High-importance_constructed_language_articles | 37.0 | 0.0 | 0.0 |
| 883.0 | Mid-importance_constructed_language_articles | 127.0 | 0.0 | 0.0 |
| 884.0 | Croix_de_guerre_recipients | 0.0 | 0.0 | 0.0 |
| 885.0 | British_Army_personnel_of_World_War_I | 5783.0 | 5.0 | 0.0 |
| 887.0 | Trinidadian_and_Tobagonian_cyclists | 0.0 | 0.0 | 0.0 |
| 888.0 | Trinidad_and_Tobago_cyclists | 5.0 | 4.0 | 0.0 |
| 890.0 | Kushiel's_Legacy | 7.0 | 0.0 | 0.0 |
| 891.0 | Construction_and_civil_engineering_companies | 15.0 | 10.0 | 0.0 |
| 893.0 | Moldova | 19.0 | 16.0 | 0.0 |
| 895.0 | Wikipedians_interested_in_environmental_science | 1.0 | 0.0 | 0.0 |
| 896.0 | Novels_by_Mika_Waltari | 12.0 | 0.0 | 0.0 |
| 897.0 | Finnish_novels | 9.0 | 9.0 | 0.0 |
| 898.0 | Construction | 196.0 | 38.0 | 0.0 |
| 899.0 | Vienna | 22.0 | 18.0 | 0.0 |
| 900.0 | Palau | 21.0 | 16.0 | 0.0 |
| 903.0 | Church_stubs | 26.0 | 13.0 | 0.0 |
| 904.0 | Missouri_stubs | 188.0 | 7.0 | 0.0 |
| 908.0 | 2006_EPs | 339.0 | 1.0 | 0.0 |
| 909.0 | Albums | 42.0 | 32.0 | 0.0 |
| 912.0 | Baseball_third_baseman_stubs | 168.0 | 1.0 | 0.0 |
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| 915.0 | Companies_established_in_1971 | 19.0 | 17.0 | 0.0 |
| 916.0 | Defunct_companies_of_the_United_States | 66.0 | 8.0 | 0.0 |
| 918.0 | Viacom | 0.0 | 0.0 | 0.0 |
| 919.0 | Viacom_subsidiaries | 0.0 | 0.0 | 0.0 |
| 921.0 | Spoken_Wikipedia_requests | 748.0 | 0.0 | 0.0 |
| 922.0 | 1978_FIFA_World_Cup | 24.0 | 8.0 | 0.0 |
| 923.0 | Wikipedia_protected_edit_requests | 0.0 | 0.0 | 0.0 |
| 924.0 | Tibet | 80.0 | 28.0 | 0.0 |
| 925.0 | Nebraska_stubs | 222.0 | 5.0 | 0.0 |
| 927.0 | Delaware_State_University | 5.0 | 2.0 | 0.0 |
| 928.0 | 2008_EPs | 387.0 | 1.0 | 0.0 |
| 929.0 | Suspension_bridges | 43.0 | 5.0 | 0.0 |
| 930.0 | English_rappers | 12.0 | 5.0 | 0.0 |
| 931.0 | 1989_births | 17929.0 | 0.0 | 0.0 |
| 932.0 | Palm_stubs | 357.0 | 3.0 | 0.0 |
| 934.0 | Child_actors_by_nationality | 65.0 | 65.0 | 0.0 |
| 935.0 | Stealth_aircraft | 82.0 | 2.0 | 0.0 |
| 938.0 | Actors_and_filmmakers_work_group_articles | 92464.0 | 3.0 | 0.0 |
| 939.0 | B-Class_Oklahoma_articles | 292.0 | 1.0 | 0.0 |
| 940.0 | B-Class_biography_(actors_and_filmmakers)_articles | 1677.0 | 0.0 | 0.0 |
| 941.0 | B-Class_biography_articles | 31430.0 | 11.0 | 0.0 |
| 943.0 | Mid-importance_Oklahoma_articles | 1139.0 | 0.0 | 0.0 |
| 945.0 | Cigarette_rolling_papers | 17.0 | 0.0 | 0.0 |
| 946.0 | Brand_name_products_stubs | 433.0 | 1.0 | 0.0 |
| 948.0 | Anti-Catholicism | 44.0 | 10.0 | 0.0 |
| 949.0 | Religious_persecution | 63.0 | 25.0 | 0.0 |
| 950.0 | Comic_book_covers | 710.0 | 40.0 | 669.0 |
| 951.0 | Non-free_comic_images | 7856.0 | 30.0 | 7826.0 |
| 952.0 | Fair_use_character_artwork | 1254.0 | 3.0 | 1251.0 |
| 953.0 | Music_managers | 24.0 | 1.0 | 0.0 |
| 954.0 | Start-Class_Disney_articles | 1891.0 | 6.0 | 0.0 |
| 955.0 | Unassessed_Disney_articles | 16.0 | 6.0 | 0.0 |
| 956.0 | Egypt_stubs | 169.0 | 9.0 | 0.0 |
| 957.0 | 2007_EPs | 330.0 | 1.0 | 0.0 |
| 958.0 | Incomplete_lists | 182.0 | 182.0 | 0.0 |
| 959.0 | Christianity_and_other_religions | 34.0 | 14.0 | 0.0 |
| 960.0 | Judeo-Christian_topics | 25.0 | 3.0 | 0.0 |
| 961.0 | Judaism_and_other_religions | 22.0 | 11.0 | 0.0 |
| 962.0 | Christian_and_Jewish_interfaith_topics | 0.0 | 0.0 | 0.0 |
| 964.0 | American_rock_singers | 1407.0 | 8.0 | 0.0 |
| 965.0 | American_heavy_metal_singers | 338.0 | 1.0 | 0.0 |
| 966.0 | Novels_by_author | 0.0 | 0.0 | 0.0 |
| 967.0 | Italian_novels | 23.0 | 10.0 | 0.0 |
| 968.0 | 1965_births | 14963.0 | 0.0 | 0.0 |
| 969.0 | Accuracy_disputes_from_March_2008 | 29.0 | 1.0 | 0.0 |
| 970.0 | Chinese_Muslims | 68.0 | 9.0 | 0.0 |
| 972.0 | WikiProject_Czech_Republic_articles | 5.0 | 5.0 | 0.0 |
| 973.0 | Unassessed_Czech_Republic_articles | 9.0 | 0.0 | 0.0 |
| 974.0 | Unknown-importance_Czech_Republic_articles | 859.0 | 0.0 | 0.0 |
| 977.0 | Lighthouses_in_the_San_Francisco_Bay_Area | 13.0 | 1.0 | 0.0 |
| 979.0 | Stub-Class_New_Mexico_articles | 2322.0 | 0.0 | 0.0 |
| 980.0 | Unassessed_New_Mexico_articles | 306.0 | 0.0 | 0.0 |
| 981.0 | Man | 1.0 | 0.0 | 0.0 |
| 984.0 | 1955_births | 14266.0 | 0.0 | 0.0 |
| 985.0 | Deportivo_de_La_Coruña_players | 407.0 | 2.0 | 0.0 |
| 986.0 | Footballers_at_the_1980_Summer_Olympics | 263.0 | 0.0 | 0.0 |
| 987.0 | Judaism | 26.0 | 25.0 | 0.0 |
| 988.0 | Child_singers_by_nationality | 56.0 | 56.0 | 0.0 |
| 989.0 | Wikis | 80.0 | 8.0 | 0.0 |
| 990.0 | B-Class_Disney_articles | 188.0 | 6.0 | 0.0 |
| 991.0 | Military_aviation_task_force_articles | 32140.0 | 2.0 | 0.0 |
| 992.0 | Military_memorials_and_cemeteries_task_force_articles | 3045.0 | 2.0 | 0.0 |
| 993.0 | British_military_history_task_force_articles | 49788.0 | 2.0 | 0.0 |
| 994.0 | Unassessed_military_history_articles | 3.0 | 1.0 | 0.0 |
| 995.0 | Unassessed_aviation_articles | 144.0 | 0.0 | 0.0 |
| 996.0 | WikiProject_Aviation_articles | 75018.0 | 8.0 | 0.0 |
| 997.0 | Khmer_Rouge | 45.0 | 7.0 | 0.0 |
| 999.0 | Television_stubs | 106.0 | 19.0 | 0.0 |
| 1001.0 | Historic_preservation | 122.0 | 11.0 | 0.0 |
| 1002.0 | National_Register_of_Historic_Places_stubs | 9.0 | 7.0 | 0.0 |
| 1003.0 | Children_by_nationality | 97.0 | 97.0 | 0.0 |
| 1004.0 | Men | 70.0 | 35.0 | 0.0 |
| 1006.0 | Anarchists_by_nationality | 61.0 | 61.0 | 0.0 |
| 1007.0 | Novels_by_Kaari_Utrio | 27.0 | 0.0 | 0.0 |
| 1009.0 | Alphabetic_writing_systems | 0.0 | 0.0 | 0.0 |
| 1010.0 | Japanese_Christians | 88.0 | 14.0 | 0.0 |
| 1011.0 | Move_protected | 0.0 | 0.0 | 0.0 |
| 1013.0 | American_television_composers | 363.0 | 1.0 | 0.0 |
| 1015.0 | Upcoming_video_games | 141.0 | 2.0 | 0.0 |
| 1016.0 | Eyeshield_21_characters | 3.0 | 0.0 | 0.0 |
| 1017.0 | Irish-Americans | 0.0 | 0.0 | 0.0 |
| 1018.0 | Nickel | 26.0 | 5.0 | 0.0 |
| 1019.0 | Communists_by_nationality | 154.0 | 154.0 | 0.0 |
| 1021.0 | Mexican_Spanish | 12.0 | 2.0 | 0.0 |
| 1022.0 | 1987_births | 17411.0 | 0.0 | 0.0 |
| 1023.0 | Boston_Bruins_players | 1051.0 | 1.0 | 0.0 |
| 1024.0 | Finnish_ice_hockey_players | 69.0 | 6.0 | 0.0 |
| 1025.0 | Ilves_players | 314.0 | 1.0 | 0.0 |
| 1027.0 | Providence_Bruins_players | 518.0 | 0.0 | 0.0 |
| 1028.0 | Toronto_Maple_Leafs_draft_picks | 307.0 | 0.0 | 0.0 |
| 1030.0 | 1967_births | 14705.0 | 0.0 | 0.0 |
| 1031.0 | Ancient_Near_East_stubs | 115.0 | 4.0 | 0.0 |
| 1032.0 | Middle_Eastern_history_stubs | 320.0 | 6.0 | 0.0 |
| 1033.0 | Stub-Class_chemicals_articles | 10758.0 | 0.0 | 0.0 |
| 1034.0 | Low-importance_chemicals_articles | 17092.0 | 0.0 | 0.0 |
| 1035.0 | Unassessed_chemicals_articles | 47.0 | 0.0 | 0.0 |
| 1036.0 | Unknown-importance_chemicals_articles | 63.0 | 0.0 | 0.0 |
| 1038.0 | Tennessee_Registered_Historic_Place_stubs | 4.0 | 3.0 | 0.0 |
| 1039.0 | 1953_short_stories | 89.0 | 0.0 | 0.0 |
| 1040.0 | 1951_short_stories | 38.0 | 0.0 | 0.0 |
| 1041.0 | German_people | 46.0 | 38.0 | 0.0 |
| 1042.0 | Redundant_images_for_speedy_deletion | 0.0 | 0.0 | 0.0 |
| 1043.0 | Polynesia | 34.0 | 25.0 | 0.0 |
| 1044.0 | Invasive_species | 33.0 | 7.0 | 0.0 |
| 1045.0 | Code_Geass | 14.0 | 3.0 | 0.0 |
| 1046.0 | AfD_debates_(Biographical) | 176.0 | 0.0 | 0.0 |
| 1047.0 | Requests_for_unblock-auto | 1.0 | 0.0 | 0.0 |
| 1048.0 | Colombian_culture | 90.0 | 34.0 | 0.0 |
| 1049.0 | Mid-importance_chemicals_articles | 2158.0 | 0.0 | 0.0 |
| 1051.0 | European_composer_stubs | 378.0 | 19.0 | 0.0 |
| 1053.0 | National_Basketball_Association | 42.0 | 18.0 | 0.0 |
| 1055.0 | Pedophilia | 34.0 | 9.0 | 0.0 |
| 1056.0 | Semi-protected_against_vandalism | 0.0 | 0.0 | 0.0 |
| 1058.0 | Low-importance_Ireland_articles | 60479.0 | 16.0 | 0.0 |
| 1059.0 | Start-Class_biography_(politics_and_government)_articles | 77421.0 | 0.0 | 0.0 |
| 1061.0 | Unknown-importance_Ireland_articles | 16.0 | 14.0 | 0.0 |
| 1062.0 | Start-Class_military_memorials_and_cemeteries_articles | 1163.0 | 0.0 | 0.0 |
| 1063.0 | Lists_of_people_by_occupation | 163.0 | 52.0 | 0.0 |
| 1064.0 | Occupations | 66.0 | 17.0 | 0.0 |
| 1065.0 | English_actors | 42.0 | 14.0 | 0.0 |
| 1066.0 | Fullmetal_Alchemist_images | 11.0 | 1.0 | 10.0 |
| 1067.0 | Football_at_the_2008_Summer_Olympics | 7.0 | 4.0 | 0.0 |
| 1068.0 | Year_of_birth_unknown | 23249.0 | 2.0 | 0.0 |
| 1069.0 | 1537_deaths | 132.0 | 1.0 | 0.0 |
| 1071.0 | High-importance_chemicals_articles | 322.0 | 0.0 | 0.0 |
| 1072.0 | British_libertarians | 46.0 | 3.0 | 0.0 |
| 1073.0 | Libertarians_by_nationality | 36.0 | 36.0 | 0.0 |
| 1074.0 | U.C._Sampdoria | 13.0 | 7.0 | 0.0 |
| 1076.0 | Hayate_the_Combat_Butler | 16.0 | 1.0 | 2.0 |
| 1078.0 | Solano_County,_California | 19.0 | 12.0 | 0.0 |
| 1079.0 | The_Suite_Life_of_Zack_&_Cody | 10.0 | 1.0 | 4.0 |
| 1080.0 | South_Korean_Christians | 52.0 | 8.0 | 0.0 |
| 1081.0 | Images_of_Ukraine | 39.0 | 3.0 | 36.0 |
| 1082.0 | PD-UA-exempt | 12.0 | 0.0 | 12.0 |
| 1083.0 | American_football_coach_stubs | 255.0 | 1.0 | 0.0 |
| 1084.0 | College_football_coaches_first_appointed_in_the_2000s_stubs | 158.0 | 0.0 | 0.0 |
| 1085.0 | Gibson_County,_Indiana | 15.0 | 8.0 | 0.0 |
| 1087.0 | British_Army_personnel_of_World_War_II | 3817.0 | 10.0 | 0.0 |
| 1088.0 | British_military_personnel_killed_in_World_War_I | 1093.0 | 3.0 | 0.0 |
| 1089.0 | British_films | 41.0 | 41.0 | 0.0 |
| 1090.0 | 1980s_drama_films | 41.0 | 38.0 | 0.0 |
| 1091.0 | Romance_films | 30.0 | 20.0 | 0.0 |
| 1092.0 | Start-Class_chemicals_articles | 5594.0 | 0.0 | 0.0 |
| 1093.0 | Bosnia_and_Herzegovina_footballers | 1080.0 | 4.0 | 0.0 |
| 1094.0 | FK_Sarajevo_players | 353.0 | 0.0 | 0.0 |
| 1095.0 | VfB_Stuttgart_players | 456.0 | 1.0 | 0.0 |
| 1097.0 | Accuracy_disputes | 181.0 | 180.0 | 0.0 |
| 1098.0 | Stub-Class_Album_articles | 84178.0 | 0.0 | 0.0 |
| 1100.0 | WikiProject_Albums_articles | 380100.0 | 5.0 | 0.0 |
| 1101.0 | Contemporary_Christian_work_group_articles | 38.0 | 4.0 | 0.0 |
| 1102.0 | Stub-Class_Contemporary_Christian_articles | 9.0 | 0.0 | 0.0 |
| 1104.0 | Stub-Class_Christian_music_articles | 1904.0 | 0.0 | 0.0 |
| 1105.0 | Low-importance_Christian_music_articles | 3263.0 | 0.0 | 0.0 |
| 1106.0 | Electric_power_transmission_systems | 25.0 | 6.0 | 0.0 |
| 1107.0 | Liechtenstein | 18.0 | 15.0 | 0.0 |
| 1108.0 | Ivy_League | 35.0 | 23.0 | 0.0 |
| 1109.0 | Colombia_international_footballers | 465.0 | 3.0 | 0.0 |
| 1110.0 | América_de_Cali_footballers | 392.0 | 0.0 | 0.0 |
| 1111.0 | Independiente_Santa_Fe_footballers | 314.0 | 0.0 | 0.0 |
| 1113.0 | Piacenza_Calcio_players | 0.0 | 0.0 | 0.0 |
| 1114.0 | Sport_Boys_footballers | 227.0 | 0.0 | 0.0 |
| 1115.0 | Nazi_concentration_camps | 41.0 | 9.0 | 0.0 |
| 1121.0 | B-Class_core_topic_articles | 6.0 | 0.0 | 0.0 |
| 1122.0 | B-Class_taxonomic_articles | 34.0 | 0.0 | 0.0 |
| 1125.0 | Natural_sciences_Version_0.7_articles | 166.0 | 0.0 | 0.0 |
| 1126.0 | Top-importance_plant_articles | 79.0 | 2.0 | 0.0 |
| 1127.0 | Top-importance_taxonomic_articles | 25.0 | 0.0 | 0.0 |
| 1128.0 | Wikipedia_CD_Selection | 2405.0 | 2.0 | 0.0 |
| 1130.0 | Socialists_by_nationality | 192.0 | 192.0 | 0.0 |
| 1131.0 | French_hip_hop | 5.0 | 3.0 | 0.0 |
| 1132.0 | Drama_films | 22.0 | 17.0 | 0.0 |
| 1133.0 | Stub-Class_Museums_articles | 5634.0 | 0.0 | 0.0 |
| 1134.0 | Stub-Class_maritime_warfare_articles | 3629.0 | 0.0 | 0.0 |
| 1135.0 | Maritime_warfare_task_force_articles | 74239.0 | 2.0 | 0.0 |
| 1136.0 | Stub-Class_military_memorials_and_cemeteries_articles | 294.0 | 0.0 | 0.0 |
| 1137.0 | Stub-Class_Ships_articles | 8491.0 | 0.0 | 0.0 |
| 1143.0 | Unknown-importance_Doctor_Who_articles | 1.0 | 0.0 | 0.0 |
| 1144.0 | World | 48.0 | 29.0 | 0.0 |
| 1145.0 | Screenshots_of_music_videos | 1927.0 | 1.0 | 1926.0 |
| 1146.0 | Ayumi_Hamasaki | 10.0 | 3.0 | 0.0 |
| 1147.0 | Wales | 31.0 | 28.0 | 0.0 |
| 1148.0 | Dartmouth_College_alumni | 1604.0 | 5.0 | 0.0 |
| 1149.0 | Southern_United_States | 81.0 | 38.0 | 0.0 |
| 1150.0 | 1925_births | 8900.0 | 0.0 | 0.0 |
| 1151.0 | 1985_deaths | 5335.0 | 2.0 | 0.0 |
| 1153.0 | St._Louis_Browns_players | 773.0 | 1.0 | 0.0 |
| 1154.0 | Baltimore_Orioles_players | 1186.0 | 1.0 | 0.0 |
| 1155.0 | Cleveland_Indians_players | 1714.0 | 0.0 | 0.0 |
| 1156.0 | Philadelphia_Phillies_players | 2075.0 | 2.0 | 0.0 |
| 1158.0 | People_from_Howard_County,_Maryland | 49.0 | 11.0 | 0.0 |
| 1159.0 | Baseball_second_baseman_stubs | 127.0 | 1.0 | 0.0 |
| 1160.0 | American_Confucianists | 5.0 | 0.0 | 0.0 |
| 1161.0 | Stub-Class_football_articles | 208027.0 | 20.0 | 0.0 |
| 1163.0 | Mid-importance_football_in_Italy_articles | 3011.0 | 0.0 | 0.0 |
| 1164.0 | Stub-Class_football_in_Italy_articles | 7863.0 | 0.0 | 0.0 |
| 1166.0 | Mid-importance_football_in_Argentina_articles | 3011.0 | 0.0 | 0.0 |
| 1167.0 | Stub-Class_football_in_Argentina_articles | 5422.0 | 0.0 | 0.0 |
| 1168.0 | Mid-importance_football_articles | 51236.0 | 3.0 | 0.0 |
| 1172.0 | Articles_with_weasel_words | 172.0 | 172.0 | 0.0 |
| 1173.0 | Dungeons_&_Dragons_articles_that_need_to_differentiate_between_fact_and_fiction | 12.0 | 0.0 | 0.0 |
| 1175.0 | User_pt | 577.0 | 11.0 | 0.0 |
| 1176.0 | User_pt-5 | 79.0 | 0.0 | 0.0 |
| 1177.0 | 1974_births | 14847.0 | 0.0 | 0.0 |
| 1178.0 | Unassessed_chemistry_articles | 0.0 | 0.0 | 0.0 |
| 1179.0 | Unknown-importance_chemistry_articles | 0.0 | 0.0 | 0.0 |
| 1180.0 | Heat_waves | 10.0 | 3.0 | 0.0 |
| 1181.0 | Fullerton,_California | 23.0 | 10.0 | 0.0 |
| 1182.0 | Climate_of_Minnesota | 6.0 | 0.0 | 0.0 |
| 1183.0 | Unassessed_African_diaspora_articles | 482.0 | 0.0 | 0.0 |
| 1184.0 | Unknown-importance_African_diaspora_articles | 3609.0 | 0.0 | 0.0 |
| 1185.0 | Wikipedia_requested_photographs | 14339.0 | 4.0 | 0.0 |
| 1186.0 | 2008 | 61.0 | 42.0 | 0.0 |
| 1187.0 | AfD_debates_(Science_and_technology) | 24.0 | 0.0 | 0.0 |
| 1188.0 | Musical_film_stubs | 561.0 | 6.0 | 0.0 |
| 1190.0 | Automatically_assessed_biography_(military)_articles | 487.0 | 0.0 | 0.0 |
| 1191.0 | Automatically_assessed_biography_articles | 201629.0 | 9.0 | 0.0 |
| 1192.0 | German_history_stubs | 570.0 | 6.0 | 0.0 |
| 1193.0 | Articles_lacking_in-text_citations | 196.0 | 195.0 | 0.0 |
| 1194.0 | 1980_births | 15963.0 | 0.0 | 0.0 |
| 1195.0 | American_spree_killers | 125.0 | 0.0 | 0.0 |
| 1196.0 | Northern_Illinois_University_alumni | 213.0 | 1.0 | 0.0 |
| 1197.0 | Polish-Americans | 0.0 | 0.0 | 0.0 |
| 1198.0 | People_from_Elk_Grove_Village,_Illinois | 19.0 | 0.0 | 0.0 |
| 1199.0 | Suicides_by_firearm_in_the_United_States | 9.0 | 3.0 | 0.0 |
| 1200.0 | B-Class_France_articles | 2174.0 | 2.0 | 0.0 |
| 1202.0 | Mid-importance_Peru_articles | 642.0 | 0.0 | 0.0 |
| 1203.0 | Unknown-importance_Peru_articles | 3726.0 | 0.0 | 0.0 |
| 1204.0 | Assassinated_people_by_nationality | 151.0 | 151.0 | 0.0 |
| 1206.0 | Royal_Navy_ship_names | 2408.0 | 1.0 | 0.0 |
| 1209.0 | Delaware | 30.0 | 25.0 | 0.0 |
| 1210.0 | Slough | 31.0 | 10.0 | 0.0 |
| 1211.0 | Family_Feud | 29.0 | 0.0 | 0.0 |
| 1212.0 | WikiProject_Queen | 5.0 | 1.0 | 0.0 |
| 1217.0 | Stub-Class_Baseball_articles | 24370.0 | 1.0 | 0.0 |
| 1218.0 | Sports_and_games_work_group_articles | 647903.0 | 3.0 | 0.0 |
| 1219.0 | Unassessed_biography_(sports_and_games)_articles | 5124.0 | 0.0 | 0.0 |
| 1221.0 | Perth,_Western_Australia | 34.0 | 17.0 | 0.0 |
| 1222.0 | Wikipedia_rollback_feature | 41.0 | 2.0 | 0.0 |
| 1223.0 | Ball_games | 259.0 | 45.0 | 0.0 |
| 1224.0 | First_Nations_culture | 112.0 | 32.0 | 0.0 |
| 1225.0 | Lacrosse | 23.0 | 19.0 | 0.0 |
| 1226.0 | Sports_rules_and_regulations | 85.0 | 10.0 | 0.0 |
| 1227.0 | Team_sports | 255.0 | 69.0 | 0.0 |
| 1228.0 | Ship_articles_needing_infobox_conversion | 0.0 | 0.0 | 0.0 |
| 1229.0 | Vietnamese_Roman_Catholics | 67.0 | 6.0 | 0.0 |
| 1231.0 | Brisbane | 44.0 | 18.0 | 0.0 |
| 1232.0 | Fossils | 56.0 | 23.0 | 0.0 |
| 1233.0 | East_River | 56.0 | 4.0 | 0.0 |
| 1234.0 | New_York_City_Subway | 32.0 | 15.0 | 0.0 |
| 1235.0 | Railway_tunnels_in_New_York_City | 0.0 | 0.0 | 0.0 |
| 1236.0 | New_York_City_transportation_stubs | 22.0 | 2.0 | 0.0 |
| 1239.0 | Canadian_engineers | 150.0 | 21.0 | 0.0 |
| 1240.0 | University_of_Ottawa_alumni | 441.0 | 2.0 | 0.0 |
| 1241.0 | Royal_Military_College_of_Canada_people | 12.0 | 3.0 | 0.0 |
| 1242.0 | Pilates | 12.0 | 0.0 | 0.0 |
| 1243.0 | Articles_lacking_reliable_references_from_September_2007 | 120.0 | 0.0 | 0.0 |
| 1244.0 | Articles_lacking_sources_from_November_2007 | 327.0 | 0.0 | 0.0 |
| 1245.0 | Torchwood_characters | 19.0 | 0.0 | 0.0 |
| 1246.0 | Doctor_Who_races | 31.0 | 3.0 | 0.0 |
| 1247.0 | Stand-up_comedians | 14.0 | 1.0 | 0.0 |
| 1248.0 | Dallas,_Texas | 0.0 | 0.0 | 0.0 |
| 1249.0 | Indian_folklore | 149.0 | 19.0 | 0.0 |
| 1250.0 | Indian_monarchs | 57.0 | 31.0 | 0.0 |
| 1251.0 | Samoa_stubs | 75.0 | 4.0 | 0.0 |
| 1252.0 | United_States_history_stubs | 375.0 | 5.0 | 0.0 |
| 1253.0 | FA-Class_Firearms_articles | 1.0 | 0.0 | 0.0 |
| 1254.0 | FA-Class_Russia_articles | 86.0 | 5.0 | 0.0 |
| 1256.0 | FA-Class_military_history_articles | 1377.0 | 2.0 | 0.0 |
| 1258.0 | FA-Class_weaponry_articles | 43.0 | 0.0 | 0.0 |
| 1259.0 | Military_history_articles_used_on_portals | 179.0 | 0.0 | 0.0 |
| 1262.0 | Top-importance_Russia_articles | 1153.0 | 11.0 | 0.0 |
| 1263.0 | Weaponry_task_force_articles | 11494.0 | 2.0 | 0.0 |
| 1264.0 | WikiProject_Firearms | 23.0 | 6.0 | 0.0 |
| 1265.0 | 2000_albums | 2084.0 | 11.0 | 0.0 |
| 1266.0 | Booz_Allen_Hamilton | 7.0 | 1.0 | 0.0 |
| 1268.0 | Unassessed_Iowa_articles | 175.0 | 0.0 | 0.0 |
| 1269.0 | Unknown-importance_Iowa_articles | 765.0 | 0.0 | 0.0 |
| 1270.0 | WikiProject_Iowa | 16.0 | 5.0 | 0.0 |
| 1271.0 | Oklahoma_stubs | 134.0 | 7.0 | 0.0 |
| 1272.0 | Southern_United_States_building_and_structure_stubs | 26.0 | 24.0 | 0.0 |
| 1273.0 | Free_Software_Foundation | 32.0 | 2.0 | 0.0 |
| 1274.0 | Evolution | 95.0 | 8.0 | 0.0 |
| 1275.0 | Metabolism | 270.0 | 16.0 | 0.0 |
| 1276.0 | Origin_of_life | 77.0 | 4.0 | 0.0 |
| 1277.0 | Album_articles_with_non-standard_infoboxes | 2.0 | 0.0 | 0.0 |
| 1278.0 | 1993 | 48.0 | 39.0 | 0.0 |
| 1279.0 | Start-Class_District_of_Columbia_articles | 3463.0 | 0.0 | 0.0 |
| 1280.0 | Stub-Class_District_of_Columbia_articles | 3262.0 | 0.0 | 0.0 |
| 1281.0 | Toms_River,_New_Jersey | 36.0 | 4.0 | 0.0 |
| 1282.0 | Sports | 55.0 | 48.0 | 0.0 |
| 1283.0 | American_actors | 116.0 | 17.0 | 0.0 |
| 1284.0 | Sport_in_Indonesia | 56.0 | 28.0 | 0.0 |
| 1285.0 | Confucianism | 29.0 | 15.0 | 0.0 |
| 1286.0 | Knights_of_the_Round_Table | 45.0 | 0.0 | 0.0 |
| 1287.0 | Ambient_musicians | 237.0 | 3.0 | 0.0 |
| 1289.0 | Recent_deaths | 3.0 | 0.0 | 0.0 |
| 1290.0 | Milky_Way_Galaxy | 0.0 | 0.0 | 0.0 |
| 1291.0 | Comics_articles_needing_issue_citations | 1030.0 | 0.0 | 0.0 |
| 1294.0 | Hungary | 18.0 | 16.0 | 0.0 |
val rowsToSave = spark.sql("SELECT cat_id, cat_title, cat_pages, cat_subcats, cat_files FROM categories WHERE _corrupt_record IS NULL")
rowsToSave.write.saveAsTable("enwiki_category")
rowsToSave: org.apache.spark.sql.DataFrame = [cat_id: int, cat_title: string ... 3 more fields]
Removing redirects
In this notebook, we remove all pages marked as redirects, and replace links to redirects with direct links.
First, we just look at the structure of our data:
SELECT * FROM enwiki_graph_edges
| src | dst | src_title | dst_title |
|---|---|---|---|
| 1088.0 | 4.144269e7 | Azerbaijani_Armed_Forces | Corps_of_Drums |
| 1088.0 | 5.8693917e7 | Azerbaijani_Armed_Forces | Foreign_Intelligence_Service_(Azerbaijan) |
| 1088.0 | 2648922.0 | Azerbaijani_Armed_Forces | Hydroelectric_power_station |
| 1088.0 | 34252.0 | Azerbaijani_Armed_Forces | Republic_of_Yemen_Armed_Forces |
| 1088.0 | 1036235.0 | Azerbaijani_Armed_Forces | Zand_dynasty |
| 1088.0 | 2.0823682e7 | Azerbaijani_Armed_Forces | Rovnag_Abdullayev |
| 1088.0 | 2.3916399e7 | Azerbaijani_Armed_Forces | Sport_in_Azerbaijan |
| 1088.0 | 3.6945373e7 | Azerbaijani_Armed_Forces | Theatre_in_Azerbaijan |
| 1088.0 | 17760.0 | Azerbaijani_Armed_Forces | Lao_People's_Armed_Forces |
| 1088.0 | 6.6150419e7 | Azerbaijani_Armed_Forces | 3rd_Army_Corps_(Azerbaijan) |
| 1088.0 | 3457.0 | Azerbaijani_Armed_Forces | Belarus |
| 1088.0 | 1.1505052e7 | Azerbaijani_Armed_Forces | National_Hero_of_Azerbaijan |
| 1088.0 | 897352.0 | Azerbaijani_Armed_Forces | Singapore_Armed_Forces |
| 1088.0 | 6.5910946e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Sugovushan_Medal |
| 1088.0 | 523670.0 | Azerbaijani_Armed_Forces | List_of_states_with_limited_recognition |
| 1088.0 | 3.0314065e7 | Azerbaijani_Armed_Forces | Najmeddin_Sadikov |
| 1088.0 | 4.3202421e7 | Azerbaijani_Armed_Forces | The_Land_of_Fire |
| 1088.0 | 6.6065854e7 | Azerbaijani_Armed_Forces | Baku_Victory_Parade_of_2020 |
| 1088.0 | 3434750.0 | Azerbaijani_Armed_Forces | United_States |
| 1088.0 | 3.9140285e7 | Azerbaijani_Armed_Forces | 23rd_Guards_Motor_Rifle_Division |
| 1088.0 | 5.7562858e7 | Azerbaijani_Armed_Forces | Elta |
| 1088.0 | 5306394.0 | Azerbaijani_Armed_Forces | Haditha,_Iraq |
| 1088.0 | 1.4305018e7 | Azerbaijani_Armed_Forces | Islamic_Republic_of_Iran_Armed_Forces |
| 1088.0 | 7150805.0 | Azerbaijani_Armed_Forces | National_parks_of_Azerbaijan |
| 1088.0 | 3295318.0 | Azerbaijani_Armed_Forces | Patrol_craft |
| 1088.0 | 1340560.0 | Azerbaijani_Armed_Forces | Treaty_of_Turkmenchay |
| 1088.0 | 698454.0 | Azerbaijani_Armed_Forces | Azerbaijanis |
| 1088.0 | 9322682.0 | Azerbaijani_Armed_Forces | Karabakh |
| 1088.0 | 1908551.0 | Azerbaijani_Armed_Forces | Aid |
| 1088.0 | 5122310.0 | Azerbaijani_Armed_Forces | March_Days |
| 1088.0 | 2.3538754e7 | Azerbaijani_Armed_Forces | Wayback_Machine |
| 1088.0 | 6.1170719e7 | Azerbaijani_Armed_Forces | Azerbaijani_Army_100th_anniversary_medal |
| 1088.0 | 380320.0 | Azerbaijani_Armed_Forces | MiG-25 |
| 1088.0 | 21330.0 | Azerbaijani_Armed_Forces | Nepalese_Armed_Forces |
| 1088.0 | 25682.0 | Azerbaijani_Armed_Forces | Red_Army |
| 1088.0 | 5.2597609e7 | Azerbaijani_Armed_Forces | Swietochowski,_Tadeusz |
| 1088.0 | 1711234.0 | Azerbaijani_Armed_Forces | United_States_European_Command |
| 1088.0 | 5.0021902e7 | Azerbaijani_Armed_Forces | 2016_Nagorno-Karabakh_conflict |
| 1088.0 | 1.1288692e7 | Azerbaijani_Armed_Forces | 7th_Guards_Army |
| 1088.0 | 6.5911067e7 | Azerbaijani_Armed_Forces | Brave_Warrior_Medal |
| 1088.0 | 6922486.0 | Azerbaijani_Armed_Forces | Extreme_points_of_Azerbaijan |
| 1088.0 | 19115.0 | Azerbaijani_Armed_Forces | Malaysian_Armed_Forces |
| 1088.0 | 6.8977021e7 | Azerbaijani_Armed_Forces | Wedding_tradition_in_Azerbaijan |
| 1088.0 | 3.8429228e7 | Azerbaijani_Armed_Forces | Yevgenya_class_minesweeper |
| 1088.0 | 6.4718117e7 | Azerbaijani_Armed_Forces | List_of_modern_equipment_of_the_Azerbaijani_Air_Force |
| 1088.0 | 774820.0 | Azerbaijani_Armed_Forces | List_of_Azerbaijanis |
| 1088.0 | 6.5939927e7 | Azerbaijani_Armed_Forces | Nakhchivan_Separate_Combined_Arms_Army |
| 1088.0 | 6.8702564e7 | Azerbaijani_Armed_Forces | Non-Aligned_Movement |
| 1088.0 | 2867590.0 | Azerbaijani_Armed_Forces | Royal_Cambodian_Armed_Forces |
| 1088.0 | 1.2835793e7 | Azerbaijani_Armed_Forces | Azerbaijani_cuisine |
| 1088.0 | 2.1634642e7 | Azerbaijani_Armed_Forces | Novruz_in_Azerbaijan |
| 1088.0 | 1127085.0 | Azerbaijani_Armed_Forces | Stockholm_International_Peace_Research_Institute |
| 1088.0 | 6.7122586e7 | Azerbaijani_Armed_Forces | 777th_Special_Forces_Regiment |
| 1088.0 | 2.2576829e7 | Azerbaijani_Armed_Forces | Agriculture_in_Azerbaijan |
| 1088.0 | 1081.0 | Azerbaijani_Armed_Forces | Economy_of_Azerbaijan |
| 1088.0 | 3764215.0 | Azerbaijani_Armed_Forces | Prime_Minister_of_Azerbaijan |
| 1088.0 | 877164.0 | Azerbaijani_Armed_Forces | Arran_(Caucasus) |
| 1088.0 | 67538.0 | Azerbaijani_Armed_Forces | Australian_Defence_Force |
| 1088.0 | 8371628.0 | Azerbaijani_Armed_Forces | Battle_of_Baku |
| 1088.0 | 7427466.0 | Azerbaijani_Armed_Forces | Petya-class_frigate |
| 1088.0 | 25709.0 | Azerbaijani_Armed_Forces | Russian_Armed_Forces |
| 1088.0 | 7105996.0 | Azerbaijani_Armed_Forces | State_reserves_of_Azerbaijan |
| 1088.0 | 2.1376046e7 | Azerbaijani_Armed_Forces | Wehrmacht |
| 1088.0 | 6.1912686e7 | Azerbaijani_Armed_Forces | \"95th_Anniversary_of_the_Armed_Forces_of_Azerbaijan_(1918–2013)\"_Medal |
| 1088.0 | 6.4334706e7 | Azerbaijani_Armed_Forces | Azerbaijan_Higher_Naval_Academy |
| 1088.0 | 7077602.0 | Azerbaijani_Armed_Forces | Environment_of_Azerbaijan |
| 1088.0 | 865389.0 | Azerbaijani_Armed_Forces | International_Crisis_Group |
| 1088.0 | 7095335.0 | Azerbaijani_Armed_Forces | Climate_of_Azerbaijan |
| 1088.0 | 6.5910824e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Shusha_Medal |
| 1088.0 | 1887429.0 | Azerbaijani_Armed_Forces | IISS |
| 1088.0 | 2.5278391e7 | Azerbaijani_Armed_Forces | List_of_protected_areas_of_Azerbaijan |
| 1088.0 | 5215751.0 | Azerbaijani_Armed_Forces | Multi-National_Force_–_Iraq |
| 1088.0 | 1.4465664e7 | Azerbaijani_Armed_Forces | Absheron_Peninsula |
| 1088.0 | 3.363915e7 | Azerbaijani_Armed_Forces | Azerbaijani_tea_culture |
| 1088.0 | 31730.0 | Azerbaijani_Armed_Forces | British_Armed_Forces |
| 1088.0 | 7105894.0 | Azerbaijani_Armed_Forces | Flora_of_Azerbaijan |
| 1088.0 | 68253.0 | Azerbaijani_Armed_Forces | List_of_sovereign_states |
| 1088.0 | 1.7935711e7 | Azerbaijani_Armed_Forces | Shirvanshah |
| 1088.0 | 7.0393652e7 | Azerbaijani_Armed_Forces | 641st_Special_Warfare_Naval_Unit |
| 1088.0 | 2152685.0 | Azerbaijani_Armed_Forces | Cypriot_National_Guard |
| 1088.0 | 3.6926008e7 | Azerbaijani_Armed_Forces | For_military_services_medal |
| 1088.0 | 182664.0 | Azerbaijani_Armed_Forces | Surface-to-air_missile |
| 1088.0 | 6.6149221e7 | Azerbaijani_Armed_Forces | 1st_Army_Corps_(Azerbaijan) |
| 1088.0 | 7150649.0 | Azerbaijani_Armed_Forces | Environmental_issues_in_Azerbaijan |
| 1088.0 | 6.5910861e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Lachin_Medal |
| 1088.0 | 1.0287296e7 | Azerbaijani_Armed_Forces | Otokar_Cobra |
| 1088.0 | 3.84555e7 | Azerbaijani_Armed_Forces | Safavid_Iran |
| 1088.0 | 6.72382e7 | Azerbaijani_Armed_Forces | Chief_of_the_General_Staff_(Azerbaijan) |
| 1088.0 | 339643.0 | Azerbaijani_Armed_Forces | Flag_of_Azerbaijan |
| 1088.0 | 6.6096407e7 | Azerbaijani_Armed_Forces | Heydar_Aliyev_Military_Lyceum |
| 1088.0 | 3.5252903e7 | Azerbaijani_Armed_Forces | Nuclear_Non-Proliferation_Treaty |
| 1088.0 | 5844475.0 | Azerbaijani_Armed_Forces | Palestinian_National_Security_Forces |
| 1088.0 | 1.1125639e7 | Azerbaijani_Armed_Forces | Turkey |
| 1088.0 | 187660.0 | Azerbaijani_Armed_Forces | Yakovlev |
| 1088.0 | 3.9780666e7 | Azerbaijani_Armed_Forces | History_of_the_name_Azerbaijan |
| 1088.0 | 6.6058582e7 | Azerbaijani_Armed_Forces | Hero_of_the_Patriotic_War |
| 1088.0 | 19279.0 | Azerbaijani_Armed_Forces | Mongolian_Armed_Forces |
| 1088.0 | 6.1609086e7 | Azerbaijani_Armed_Forces | Bronze_and_Iron_Age_in_Azerbaijan |
| 1088.0 | 4.7845161e7 | Azerbaijani_Armed_Forces | Nakhchivan_Airport |
| 1088.0 | 9874605.0 | Azerbaijani_Armed_Forces | Turkish_Air_Force_Academy |
| 1088.0 | 5.6441648e7 | Azerbaijani_Armed_Forces | Mughan_culture |
| 1088.0 | 368530.0 | Azerbaijani_Armed_Forces | Partnership_for_Peace |
| 1088.0 | 16650.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Republic_of_Kazakhstan |
| 1088.0 | 2409969.0 | Azerbaijani_Armed_Forces | Azerbaijan_Democratic_Republic |
| 1088.0 | 1.1886584e7 | Azerbaijani_Armed_Forces | Baku_Air_Defence_Army |
| 1088.0 | 3.5527299e7 | Azerbaijani_Armed_Forces | For_Heroism_Medal |
| 1088.0 | 6.5910757e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Aghdam_Medal |
| 1088.0 | 6.5910908e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Zangilan_Medal |
| 1088.0 | 7171338.0 | Azerbaijani_Armed_Forces | Indian_Armed_Forces |
| 1088.0 | 5.1886693e7 | Azerbaijani_Armed_Forces | S-300_(missile) |
| 1088.0 | 2884207.0 | Azerbaijani_Armed_Forces | Advanced_Research_and_Assessment_Group |
| 1088.0 | 2.0024921e7 | Azerbaijani_Armed_Forces | Armenian-occupied_territories_surrounding_Nagorno-Karabakh |
| 1088.0 | 408283.0 | Azerbaijani_Armed_Forces | Azerbaijani_Popular_Front_Party |
| 1088.0 | 6.5910929e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Khojavend_Medal |
| 1088.0 | 1.1623685e7 | Azerbaijani_Armed_Forces | Freedom_Support_Act |
| 1088.0 | 27019.0 | Azerbaijani_Armed_Forces | South_Korea |
| 1088.0 | 6.5624452e7 | Azerbaijani_Armed_Forces | 2016_Nagorno-Karabakh_clashes |
| 1088.0 | 2.3597901e7 | Azerbaijani_Armed_Forces | Azadliq_Square,_Baku |
| 1088.0 | 6131588.0 | Azerbaijani_Armed_Forces | Petroleum_industry_in_Azerbaijan |
| 1088.0 | 6.631834e7 | Azerbaijani_Armed_Forces | Second_Karabakh_War |
| 1088.0 | 1.156279e7 | Azerbaijani_Armed_Forces | Second_World_War |
| 1088.0 | 6.5911124e7 | Azerbaijani_Armed_Forces | For_Services_in_the_Rear_in_the_Patriotic_War_Medal |
| 1088.0 | 2.1659771e7 | Azerbaijani_Armed_Forces | Military_history_of_Azerbaijan |
| 1088.0 | 3.8392125e7 | Azerbaijani_Armed_Forces | Sonya_class_minesweeper |
| 1088.0 | 5.7836785e7 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan |
| 1088.0 | 30116.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Republic_of_Tajikistan |
| 1088.0 | 612372.0 | Azerbaijani_Armed_Forces | Midget_submarine |
| 1088.0 | 2.7167856e7 | Azerbaijani_Armed_Forces | Azerbaijani_Air_Force |
| 1088.0 | 15166.0 | Azerbaijani_Armed_Forces | Infantry_fighting_vehicle |
| 1088.0 | 7015198.0 | Azerbaijani_Armed_Forces | LGBT_rights_in_Azerbaijan |
| 1088.0 | 68932.0 | Azerbaijani_Armed_Forces | Bangladesh_Armed_Forces |
| 1088.0 | 1115368.0 | Azerbaijani_Armed_Forces | Maldives_National_Defence_Force |
| 1088.0 | 8417589.0 | Azerbaijani_Armed_Forces | Sallarid_dynasty |
| 1088.0 | 5321.0 | Azerbaijani_Armed_Forces | Czech_Republic |
| 1088.0 | 67638.0 | Azerbaijani_Armed_Forces | Demographics_of_Azerbaijan |
| 1088.0 | 1.1776466e7 | Azerbaijani_Armed_Forces | Ethnic_minorities_in_Azerbaijan |
| 1088.0 | 5.3412468e7 | Azerbaijani_Armed_Forces | Military_ranks_of_Azerbaijan |
| 1088.0 | 457051.0 | Azerbaijani_Armed_Forces | National_emblem_of_Azerbaijan |
| 1088.0 | 2.3290453e7 | Azerbaijani_Armed_Forces | Peacekeeping_forces_of_Azerbaijan |
| 1088.0 | 4.4208502e7 | Azerbaijani_Armed_Forces | SA-3_Goa |
| 1088.0 | 6.6404258e7 | Azerbaijani_Armed_Forces | Azerbaijani_Red_Army |
| 1088.0 | 6.614052e7 | Azerbaijani_Armed_Forces | Karim_Valiyev |
| 1088.0 | 6.2201975e7 | Azerbaijani_Armed_Forces | Nakhchivan_culture |
| 1088.0 | 5.4147626e7 | Azerbaijani_Armed_Forces | State_Security_Service_(Azerbaijan) |
| 1088.0 | 161087.0 | Azerbaijani_Armed_Forces | Timor_Leste_Defence_Force |
| 1088.0 | 6.6016112e7 | Azerbaijani_Armed_Forces | Memorial_Day_(Azerbaijan) |
| 1088.0 | 3.1854531e7 | Azerbaijani_Armed_Forces | Namer_(vehicle) |
| 1088.0 | 30095.0 | Azerbaijani_Armed_Forces | Republic_of_China_Armed_Forces |
| 1088.0 | 4.3825422e7 | Azerbaijani_Armed_Forces | S-200_Angara/Vega/Dubna |
| 1088.0 | 2.3408142e7 | Azerbaijani_Armed_Forces | Sri_Lanka_Armed_Forces |
| 1088.0 | 6.6317677e7 | Azerbaijani_Armed_Forces | YARASA_Special_Forces |
| 1088.0 | 6.5451828e7 | Azerbaijani_Armed_Forces | 2020_Nagorno-Karabakh_War |
| 1088.0 | 6.9447715e7 | Azerbaijani_Armed_Forces | 402nd_Rifle_Division |
| 1088.0 | 5.224123e7 | Azerbaijani_Armed_Forces | Borders_of_Azerbaijan |
| 1088.0 | 6.5910916e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Fuzuli_Medal |
| 1088.0 | 27027.0 | Azerbaijani_Armed_Forces | Republic_of_Korea_Armed_Forces |
| 1088.0 | 1.0942678e7 | Azerbaijani_Armed_Forces | Samedbey_Mehmandarov |
| 1088.0 | 30205.0 | Azerbaijani_Armed_Forces | Turkish_Armed_Forces |
| 1088.0 | 1.2339349e7 | Azerbaijani_Armed_Forces | Architecture_of_Azerbaijan |
| 1088.0 | 6.6286297e7 | Azerbaijani_Armed_Forces | Karam_Mustafayev |
| 1088.0 | 26295.0 | Azerbaijani_Armed_Forces | Russian_Civil_War |
| 1088.0 | 16702.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Kyrgyz_Republic |
| 1088.0 | 539716.0 | Azerbaijani_Armed_Forces | Landing_craft |
| 1088.0 | 2.3269917e7 | Azerbaijani_Armed_Forces | Military_of_Azerbaijan |
| 1088.0 | 7193518.0 | Azerbaijani_Armed_Forces | Special_Forces_Command_(Turkey) |
| 1088.0 | 192825.0 | Azerbaijani_Armed_Forces | Azerbaijani_language |
| 1088.0 | 194200.0 | Azerbaijani_Armed_Forces | International_Security_Assistance_Force |
| 1088.0 | 2.0222257e7 | Azerbaijani_Armed_Forces | MKEK |
| 1088.0 | 21133.0 | Azerbaijani_Armed_Forces | NATO |
| 1088.0 | 2.5131731e7 | Azerbaijani_Armed_Forces | Azerbaijani_Army |
| 1088.0 | 2.9149908e7 | Azerbaijani_Armed_Forces | Coat_of_arms_of_Azerbaijan |
| 1088.0 | 1.6569312e7 | Azerbaijani_Armed_Forces | Education_in_Azerbaijan |
| 1088.0 | 22489.0 | Azerbaijani_Armed_Forces | Oklahoma |
| 1088.0 | 4059749.0 | Azerbaijani_Armed_Forces | Artsakh_Defence_Army |
| 1088.0 | 8696322.0 | Azerbaijani_Armed_Forces | Azerbaijan_National_Academy_of_Sciences |
| 1088.0 | 4941797.0 | Azerbaijani_Armed_Forces | Azerbaijani_Land_Forces |
| 1088.0 | 188675.0 | Azerbaijani_Armed_Forces | Baltic_states |
| 1088.0 | 3.0322746e7 | Azerbaijani_Armed_Forces | General_Staff_of_Azerbaijani_Armed_Forces |
| 1088.0 | 2785204.0 | Azerbaijani_Armed_Forces | Japan_Self-Defense_Forces |
| 1088.0 | 4.5541218e7 | Azerbaijani_Armed_Forces | 295th_Motor_Rifle_Division |
| 1088.0 | 1.8838818e7 | Azerbaijani_Armed_Forces | Bibiheybət |
| 1088.0 | 5.5095974e7 | Azerbaijani_Armed_Forces | Healthcare_in_Azerbaijan |
| 1088.0 | 20282.0 | Azerbaijani_Armed_Forces | Mechanized_infantry |
| 1088.0 | 3.7721373e7 | Azerbaijani_Armed_Forces | Medicine_in_Azerbaijan |
| 1088.0 | 20394.0 | Azerbaijani_Armed_Forces | Tatmadaw |
| 1088.0 | 4.2543864e7 | Azerbaijani_Armed_Forces | United_States_Air_Forces_in_Europe |
| 1088.0 | 4.0963939e7 | Azerbaijani_Armed_Forces | Zakir_Hasanov |
| 1088.0 | 2.3207828e7 | Azerbaijani_Armed_Forces | Azerbaijan_Defense_Industry |
| 1088.0 | 1.1670391e7 | Azerbaijani_Armed_Forces | Gabala_Radar_Station |
| 1088.0 | 2.8087409e7 | Azerbaijani_Armed_Forces | Khosrov_bey_Sultanov |
| 1088.0 | 343356.0 | Azerbaijani_Armed_Forces | List_of_cities_in_Azerbaijan |
| 1088.0 | 1.7967625e7 | Azerbaijani_Armed_Forces | Mineral_industry_of_Azerbaijan |
| 1088.0 | 6.59111e7 | Azerbaijani_Armed_Forces | Participant_of_the_Patriotic_War_Medal |
| 1088.0 | 66890.0 | Azerbaijani_Armed_Forces | People's_Liberation_Army |
| 1088.0 | 4247739.0 | Azerbaijani_Armed_Forces | U.S._Navy_SEALs |
| 1088.0 | 2.8017536e7 | Azerbaijani_Armed_Forces | Valeh_Barshadli |
| 1088.0 | 2563036.0 | Azerbaijani_Armed_Forces | Hazi_Aslanov |
| 1088.0 | 381496.0 | Azerbaijani_Armed_Forces | JF-17 |
| 1088.0 | 493727.0 | Azerbaijani_Armed_Forces | Aero_L-39_Albatros |
| 1088.0 | 6.7120883e7 | Azerbaijani_Armed_Forces | Armenian_Army |
| 1088.0 | 401606.0 | Azerbaijani_Armed_Forces | Index_of_Azerbaijan-related_articles |
| 1088.0 | 1.1169023e7 | Azerbaijani_Armed_Forces | Ministry_of_Defence_Industry_of_Azerbaijan |
| 1088.0 | 5.829427e7 | Azerbaijani_Armed_Forces | Mountains_of_Azerbaijan |
| 1088.0 | 638594.0 | Azerbaijani_Armed_Forces | Non-belligerent |
| 1088.0 | 3.2945088e7 | Azerbaijani_Armed_Forces | Red_Army_invasion_of_Azerbaijan |
| 1088.0 | 31750.0 | Azerbaijani_Armed_Forces | Ukraine |
| 1088.0 | 380322.0 | Azerbaijani_Armed_Forces | Il-76 |
| 1088.0 | 1492960.0 | Azerbaijani_Armed_Forces | Nakhchivan_(city) |
| 1088.0 | 6.2087908e7 | Azerbaijani_Armed_Forces | Russo-Persian_War_(1804–13) |
| 1088.0 | 6446390.0 | Azerbaijani_Armed_Forces | State_Partnership_Program |
| 1088.0 | 905795.0 | Azerbaijani_Armed_Forces | Treaty_of_Gulistan |
| 1088.0 | 5424688.0 | Azerbaijani_Armed_Forces | Jordanian_Armed_Forces |
| 1088.0 | 956689.0 | Azerbaijani_Armed_Forces | Kura–Araxes_culture |
| 1088.0 | 5024972.0 | Azerbaijani_Armed_Forces | Operation_Edelweiss |
| 1088.0 | 3.6369933e7 | Azerbaijani_Armed_Forces | Orders,_decorations,_and_medals_of_Azerbaijan |
| 1088.0 | 412390.0 | Azerbaijani_Armed_Forces | Administrative_divisions_of_Azerbaijan |
| 1088.0 | 30215.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_Turkmenistan |
| 1088.0 | 5.0716679e7 | Azerbaijani_Armed_Forces | Nasosnaya_(air_base) |
| 1088.0 | 25194.0 | Azerbaijani_Armed_Forces | Qatar_Armed_Forces |
| 1088.0 | 3206857.0 | Azerbaijani_Armed_Forces | Religion_in_Azerbaijan |
| 1088.0 | 3.5663369e7 | Azerbaijani_Armed_Forces | Sitalchay_Military_Airbase |
| 1088.0 | 3.8938602e7 | Azerbaijani_Armed_Forces | \"For_Faultless_Service\"_medal |
| 1088.0 | 2.1288922e7 | Azerbaijani_Armed_Forces | Azerbaijan_during_World_War_II |
| 1088.0 | 1.0927351e7 | Azerbaijani_Armed_Forces | Azerbaijani_National_Guard |
| 1088.0 | 4020775.0 | Azerbaijani_Armed_Forces | First_Nagorno-Karabakh_War |
| 1088.0 | 5.515162e7 | Azerbaijani_Armed_Forces | ISSN_(identifier) |
| 1088.0 | 14532.0 | Azerbaijani_Armed_Forces | Italy |
| 1088.0 | 1986639.0 | Azerbaijani_Armed_Forces | Languages_of_Azerbaijan |
| 1088.0 | 4.1471871e7 | Azerbaijani_Armed_Forces | List_of_lakes_of_Azerbaijan |
| 1088.0 | 4363966.0 | Azerbaijani_Armed_Forces | History_of_Azerbaijan |
| 1088.0 | 65220.0 | Azerbaijani_Armed_Forces | Nagorno-Karabakh |
| 1088.0 | 27276.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_Saudi_Arabia |
| 1088.0 | 4566.0 | Azerbaijani_Armed_Forces | Baku |
| 1088.0 | 40195.0 | Azerbaijani_Armed_Forces | Telecommunications_in_Azerbaijan |
| 1088.0 | 6.7538996e7 | Azerbaijani_Armed_Forces | Şəmkir |
| 1088.0 | 1151523.0 | Azerbaijani_Armed_Forces | Azerbaijani_manat |
| 1088.0 | 213497.0 | Azerbaijani_Armed_Forces | Caucasian_Albania |
| 1088.0 | 6.5910879e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Kalbajar_Medal |
| 1088.0 | 5245787.0 | Azerbaijani_Armed_Forces | GUAM |
| 1088.0 | 6.7667374e7 | Azerbaijani_Armed_Forces | Kara_Koyunlu |
| 1088.0 | 1.8933221e7 | Azerbaijani_Armed_Forces | Royal_Brunei_Armed_Forces |
| 1088.0 | 31841.0 | Azerbaijani_Armed_Forces | United_Arab_Emirates_Armed_Forces |
| 1088.0 | 1.0927815e7 | Azerbaijani_Armed_Forces | Caspian_Guard_Initiative |
| 1088.0 | 1.9653787e7 | Azerbaijani_Armed_Forces | Caspian_Sea |
| 1088.0 | 3.4048567e7 | Azerbaijani_Armed_Forces | Marauder_(vehicle) |
| 1088.0 | 5.5829912e7 | Azerbaijani_Armed_Forces | Natural_resources_of_Azerbaijan |
| 1088.0 | 7115553.0 | Azerbaijani_Armed_Forces | Barda,_Azerbaijan |
| 1088.0 | 6.7740154e7 | Azerbaijani_Armed_Forces | Jane's_Information_Group |
| 1088.0 | 3.0455197e7 | Azerbaijani_Armed_Forces | Khojaly–Gadabay_culture |
| 1088.0 | 1519005.0 | Azerbaijani_Armed_Forces | Sultan_of_Oman's_Armed_Forces |
| 1088.0 | 3.1030978e7 | Azerbaijani_Armed_Forces | Azerbaijani_mythology |
| 1088.0 | 3.0322787e7 | Azerbaijani_Armed_Forces | Chief_of_General_Staff_of_Azerbaijani_Armed_Forces |
| 1088.0 | 46530.0 | Azerbaijani_Armed_Forces | Human_Rights_Watch |
| 1088.0 | 2.1447694e7 | Azerbaijani_Armed_Forces | List_of_companies_of_Azerbaijan |
| 1088.0 | 3.7897147e7 | Azerbaijani_Armed_Forces | National_symbols_of_Azerbaijan |
| 1088.0 | 4.5061575e7 | Azerbaijani_Armed_Forces | Qajar_Iran |
| 1088.0 | 873945.0 | Azerbaijani_Armed_Forces | Soviet_Air_Defence_Forces |
| 1088.0 | 877787.0 | Azerbaijani_Armed_Forces | Azerbaijani_literature |
| 1088.0 | 3708.0 | Azerbaijani_Armed_Forces | Brussels |
| 1088.0 | 214529.0 | Azerbaijani_Armed_Forces | Dependent_territory |
| 1088.0 | 5.5049264e7 | Azerbaijani_Armed_Forces | ISBN_(identifier) |
| 1088.0 | 9282173.0 | Azerbaijani_Armed_Forces | Israel |
| 1088.0 | 1.2085342e7 | Azerbaijani_Armed_Forces | Khanates_of_the_Caucasus |
| 1088.0 | 1.9374465e7 | Azerbaijani_Armed_Forces | Xətai_raion |
| 1088.0 | 3.3949683e7 | Azerbaijani_Armed_Forces | Air_Force_Day |
| 1088.0 | 6.0544953e7 | Azerbaijani_Armed_Forces | Azerbaijan_Higher_Military_Academy |
| 1088.0 | 79745.0 | Azerbaijani_Armed_Forces | Cluster_munition |
| 1088.0 | 21263.0 | Azerbaijani_Armed_Forces | Korean_People's_Army |
| 1088.0 | 2.2462867e7 | Azerbaijani_Armed_Forces | Soviet_Ground_Forces |
| 1088.0 | 382302.0 | Azerbaijani_Armed_Forces | Su-25 |
| 1088.0 | 1322733.0 | Azerbaijani_Armed_Forces | Black_January |
| 1088.0 | 3.5450533e7 | Azerbaijani_Armed_Forces | Day_of_the_Armed_Forces_of_Azerbaijan |
| 1088.0 | 309778.0 | Azerbaijani_Armed_Forces | Music_of_Azerbaijan |
| 1088.0 | 30136.0 | Azerbaijani_Armed_Forces | Royal_Thai_Armed_Forces |
| 1088.0 | 26748.0 | Azerbaijani_Armed_Forces | Switzerland |
| 1088.0 | 7469136.0 | Azerbaijani_Armed_Forces | Vietnam_People's_Armed_Forces |
| 1088.0 | 2.3207385e7 | Azerbaijani_Armed_Forces | Azerbaijan_Navy |
| 1088.0 | 6367906.0 | Azerbaijani_Armed_Forces | Azerbaijani_dances |
| 1088.0 | 704623.0 | Azerbaijani_Armed_Forces | CIA |
| 1088.0 | 3.7265091e7 | Azerbaijani_Armed_Forces | Caspian_Sea_Flotilla |
| 1088.0 | 2071240.0 | Azerbaijani_Armed_Forces | Culture_of_Azerbaijan |
| 1088.0 | 7761715.0 | Azerbaijani_Armed_Forces | Red_Army_invasion_of_Georgia |
| 1088.0 | 6.6176862e7 | Azerbaijani_Armed_Forces | 2nd_Army_Corps_(Azerbaijan) |
| 1088.0 | 6.5787844e7 | Azerbaijani_Armed_Forces | Battle_of_Shusha_(2020) |
| 1088.0 | 16692.0 | Azerbaijani_Armed_Forces | Kuwait_Military_Forces |
| 1088.0 | 7940585.0 | Azerbaijani_Armed_Forces | Aq_Qoyunlu |
| 1088.0 | 5042916.0 | Azerbaijani_Armed_Forces | Canada |
| 1088.0 | 510603.0 | Azerbaijani_Armed_Forces | Jane's_Fighting_Ships |
| 1088.0 | 6.5431221e7 | Azerbaijani_Armed_Forces | 2020_Nagorno-Karabakh_war |
| 1088.0 | 661551.0 | Azerbaijani_Armed_Forces | Ganja,_Azerbaijan |
| 1088.0 | 2.6217562e7 | Azerbaijani_Armed_Forces | History_of_Azerbaijani_animation |
| 1088.0 | 4562230.0 | Azerbaijani_Armed_Forces | Oklahoma_National_Guard |
| 1088.0 | 6.7228635e7 | Azerbaijani_Armed_Forces | Rovshan_Akbarov |
| 1088.0 | 8486749.0 | Azerbaijani_Armed_Forces | Russian_Space_Forces |
| 1088.0 | 382305.0 | Azerbaijani_Armed_Forces | Su-24 |
| 1088.0 | 3.5482625e7 | Azerbaijani_Armed_Forces | Armavir_Radar_Station |
| 1088.0 | 404448.0 | Azerbaijani_Armed_Forces | Azerbaijan_Soviet_Socialist_Republic |
| 1088.0 | 2.1653069e7 | Azerbaijani_Armed_Forces | Geology_of_Azerbaijan |
| 1088.0 | 4.0503488e7 | Azerbaijani_Armed_Forces | List_of_equipment_of_the_Azerbaijani_Land_Forces |
| 1088.0 | 408284.0 | Azerbaijani_Armed_Forces | List_of_political_parties_in_Azerbaijan |
| 1088.0 | 2.8119649e7 | Azerbaijani_Armed_Forces | Special_Purpose_Police_Unit |
| 1088.0 | 31717.0 | Azerbaijani_Armed_Forces | United_Kingdom |
| 1088.0 | 1.1447628e7 | Azerbaijani_Armed_Forces | Abkhazian_Armed_Forces |
| 1088.0 | 5731277.0 | Azerbaijani_Armed_Forces | Fauna_of_Azerbaijan |
| 1088.0 | 2.2765442e7 | Azerbaijani_Armed_Forces | Ilham_Aliyev |
| 1088.0 | 542300.0 | Azerbaijani_Armed_Forces | Ilkhanate |
| 1088.0 | 5.5284726e7 | Azerbaijani_Armed_Forces | Judiciary_of_Azerbaijan |
| 1088.0 | 3.4024533e7 | Azerbaijani_Armed_Forces | Leyla-Tepe_culture |
| 1088.0 | 4674848.0 | Azerbaijani_Armed_Forces | Russo-Persian_War_(1826–1828) |
| 1088.0 | 2.6964606e7 | Azerbaijani_Armed_Forces | Austria |
| 1088.0 | 6.4611227e7 | Azerbaijani_Armed_Forces | Azerbaijani_Air_and_Air_Defence_Force |
| 1088.0 | 6.698864e7 | Azerbaijani_Armed_Forces | Caves_of_Azerbaijan |
| 1088.0 | 1.8846287e7 | Azerbaijani_Armed_Forces | Jabrayil |
| 1088.0 | 7.18581e7 | Azerbaijani_Armed_Forces | Kyurdamir_Air_Base |
| 1088.0 | 3.0927438e7 | Azerbaijani_Armed_Forces | Achaemenid_Empire |
| 1088.0 | 6.5910935e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Jabrayil_Medal |
| 1088.0 | 162017.0 | Azerbaijani_Armed_Forces | Rayon |
| 1088.0 | 5366487.0 | Azerbaijani_Armed_Forces | Human_rights_in_Azerbaijan |
| 1088.0 | 1.1356544e7 | Azerbaijani_Armed_Forces | Law_enforcement_in_Azerbaijan |
| 1088.0 | 9609093.0 | Azerbaijani_Armed_Forces | Beylagan_(city) |
| 1088.0 | 519489.0 | Azerbaijani_Armed_Forces | Eastern_Front_(World_War_II) |
| 1088.0 | 1087.0 | Azerbaijani_Armed_Forces | Foreign_relations_of_Azerbaijan |
| 1088.0 | 1.1197435e7 | Azerbaijani_Armed_Forces | Maciej_Sulkiewicz |
| 1088.0 | 938372.0 | Azerbaijani_Armed_Forces | President_of_Azerbaijan |
| 1088.0 | 32817.0 | Azerbaijani_Armed_Forces | Vladimir_Putin |
| 1088.0 | 2.6157272e7 | Azerbaijani_Armed_Forces | Azerbaijani_art |
| 1088.0 | 2.7172367e7 | Azerbaijani_Armed_Forces | Azerbaijani_folklore |
| 1088.0 | 385358.0 | Azerbaijani_Armed_Forces | Nakhchivan_Autonomous_Republic |
| 1088.0 | 214413.0 | Azerbaijani_Armed_Forces | Armenian_diaspora |
| 1088.0 | 6.591061e7 | Azerbaijani_Armed_Forces | Hero_of_the_Patriotic_War_Medal |
| 1088.0 | 123503.0 | Azerbaijani_Armed_Forces | MiG-21 |
| 1088.0 | 3.3570513e7 | Azerbaijani_Armed_Forces | Russians_in_Azerbaijan |
| 1088.0 | 7.1286679e7 | Azerbaijani_Armed_Forces | Shulaveri-Shomu_culture |
| 1088.0 | 3.3872653e7 | Azerbaijani_Armed_Forces | Jar-Burial_Culture |
| 1088.0 | 7174933.0 | Azerbaijani_Armed_Forces | List_of_countries_with_nuclear_weapons |
| 1088.0 | 3.0323393e7 | Azerbaijani_Armed_Forces | Minister_of_Defense_(Azerbaijan) |
| 1088.0 | 6.5804585e7 | Azerbaijani_Armed_Forces | 2020_Nagorno-Karabakh_ceasefire_agreement |
| 1088.0 | 7.03133e7 | Azerbaijani_Armed_Forces | 223rd_Rifle_Division |
| 1088.0 | 6.6185091e7 | Azerbaijani_Armed_Forces | 4th_Army_Corps_(Azerbaijan) |
| 1088.0 | 2.5137672e7 | Azerbaijani_Armed_Forces | Energy_in_Azerbaijan |
| 1088.0 | 4.1349212e7 | Azerbaijani_Armed_Forces | Minesweeper_(ship) |
| 1088.0 | 67639.0 | Azerbaijani_Armed_Forces | Politics_of_Azerbaijan |
| 1088.0 | 1.9376957e7 | Azerbaijani_Armed_Forces | Sanqacal |
| 1088.0 | 27318.0 | Azerbaijani_Armed_Forces | Singapore |
| 1088.0 | 1.7416221e7 | Azerbaijani_Armed_Forces | South_Africa |
| 1088.0 | 32927.0 | Azerbaijani_Armed_Forces | World_War_II |
| 1088.0 | 5639884.0 | Azerbaijani_Armed_Forces | Armenian–Azerbaijani_war_(1918–1920) |
| 1088.0 | 7107998.0 | Azerbaijani_Armed_Forces | Bodies_of_water_of_Azerbaijan |
| 1088.0 | 5843419.0 | Azerbaijani_Armed_Forces | France |
| 1088.0 | 877182.0 | Azerbaijani_Armed_Forces | Shirvan |
| 1088.0 | 6.0555433e7 | Azerbaijani_Armed_Forces | War_College_of_the_Azerbaijani_Armed_Forces |
| 1088.0 | 802.0 | Azerbaijani_Armed_Forces | Ankara |
| 1088.0 | 23369.0 | Azerbaijani_Armed_Forces | Pakistan_Armed_Forces |
| 1088.0 | 4501200.0 | Azerbaijani_Armed_Forces | Parthian_Empire |
| 1088.0 | 4.190231e7 | Azerbaijani_Armed_Forces | Special_Forces_of_Azerbaijan |
| 1088.0 | 3.1022059e7 | Azerbaijani_Armed_Forces | State_Oil_Company_of_Azerbaijan_Republic |
| 1088.0 | 7.1994248e7 | Azerbaijani_Armed_Forces | Defense_Forces_of_Georgia |
| 1088.0 | 2.7911049e7 | Azerbaijani_Armed_Forces | Ministry_of_Defence_(Azerbaijan) |
| 1088.0 | 1.2975707e7 | Azerbaijani_Armed_Forces | Safar_Abiyev |
| 1088.0 | 26779.0 | Azerbaijani_Armed_Forces | Soviet_Union |
| 1088.0 | 6.1362503e7 | Azerbaijani_Armed_Forces | Stone_Age_in_Azerbaijan |
| 1088.0 | 1492790.0 | Azerbaijani_Armed_Forces | Shusha |
| 1088.0 | 31975.0 | Azerbaijani_Armed_Forces | United_States_Department_of_State |
| 1088.0 | 6.6016006e7 | Azerbaijani_Armed_Forces | Victory_Day_(Azerbaijan) |
| 1088.0 | 6064651.0 | Azerbaijani_Armed_Forces | Eldiguzids |
| 1088.0 | 6.5911037e7 | Azerbaijani_Armed_Forces | For_Distinction_in_Battle_Medal |
| 1088.0 | 14939.0 | Azerbaijani_Armed_Forces | Intercontinental_ballistic_missile |
| 1088.0 | 1.9360365e7 | Azerbaijani_Armed_Forces | North_Atlantic_Treaty_Organization |
| 1088.0 | 6.4783403e7 | Azerbaijani_Armed_Forces | 396th_Rifle_Division |
| 1088.0 | 6.9019186e7 | Azerbaijani_Armed_Forces | 416th_Rifle_Division_(Soviet_Union) |
| 1088.0 | 2.2469823e7 | Azerbaijani_Armed_Forces | Azerbaijani_peacekeeping_forces |
| 1088.0 | 3.5079877e7 | Azerbaijani_Armed_Forces | Azerbaijani_traditional_clothing |
| 1088.0 | 5043324.0 | Azerbaijani_Armed_Forces | Iraq_War |
| 1088.0 | 4627429.0 | Azerbaijani_Armed_Forces | Iraqi_Armed_Forces |
| 1088.0 | 1.905571e7 | Azerbaijani_Armed_Forces | Jebrayil |
| 1088.0 | 1.3969214e7 | Azerbaijani_Armed_Forces | Main_Agency_of_Missiles_and_Artillery_of_the_Ministry_of_Defense_of_the_Russian_Federation |
| 1088.0 | 6040932.0 | Azerbaijani_Armed_Forces | Security_Forces_Command |
| 1088.0 | 31861.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Republic_of_Uzbekistan |
| 1088.0 | 7658483.0 | Azerbaijani_Armed_Forces | Section_907 |
| 1088.0 | 19076.0 | Azerbaijani_Armed_Forces | Macao_Garrison |
| 1088.0 | 6.4207973e7 | Azerbaijani_Armed_Forces | Media_of_Azerbaijan |
| 1088.0 | 182309.0 | Azerbaijani_Armed_Forces | MiG-29 |
| 1088.0 | 59510.0 | Azerbaijani_Armed_Forces | Russians |
| 1088.0 | 2.8096514e7 | Azerbaijani_Armed_Forces | Jamshid_Nakhchivanski_Military_Lyceum |
| 1088.0 | 3.2850702e7 | Azerbaijani_Armed_Forces | List_of_World_Heritage_Sites_in_Azerbaijan |
| 1088.0 | 6.3975362e7 | Azerbaijani_Armed_Forces | OC_Media |
| 1088.0 | 2.023768e7 | Azerbaijani_Armed_Forces | Russian_Ministry_of_Defence |
| 1088.0 | 6672192.0 | Azerbaijani_Armed_Forces | Sajid_dynasty |
| 1088.0 | 4941803.0 | Azerbaijani_Armed_Forces | Azerbaijani_Navy |
| 1088.0 | 5876413.0 | Azerbaijani_Armed_Forces | Sasanian_Empire |
| 1088.0 | 2.3575502e7 | Azerbaijani_Armed_Forces | Tourism_in_Azerbaijan |
| 1088.0 | 1.0934404e7 | Azerbaijani_Armed_Forces | Wildlife_of_Azerbaijan |
| 1088.0 | 1097.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_Armenia |
| 1088.0 | 23448.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Philippines |
| 1088.0 | 4380486.0 | Azerbaijani_Armed_Forces | Armenian–Tatar_massacres_of_1905–1907 |
| 1088.0 | 407062.0 | Azerbaijani_Armed_Forces | Azərbaycan_marşı |
| 1088.0 | 2.3465971e7 | Azerbaijani_Armed_Forces | Government_of_Azerbaijan |
| 1088.0 | 7877570.0 | Azerbaijani_Armed_Forces | Individual_Partnership_Action_Plan |
| 1088.0 | 5.5289023e7 | Azerbaijani_Armed_Forces | Red_Army_invasion_of_Armenia |
| 1088.0 | 4788086.0 | Azerbaijani_Armed_Forces | Azerbaijan_Medical_University |
| 1088.0 | 5.415509e7 | Azerbaijani_Armed_Forces | State_Service_for_Mobilization_and_Conscription_of_Azerbaijan |
| 1088.0 | 1.9079143e7 | Azerbaijani_Armed_Forces | Armed_Forces_of_South_Ossetia |
| 1088.0 | 2.3207406e7 | Azerbaijani_Armed_Forces | Azerbaijan_Border_Guard |
| 1088.0 | 2.1189576e7 | Azerbaijani_Armed_Forces | Azerbaijani_rug |
| 1088.0 | 5.5636355e7 | Azerbaijani_Armed_Forces | Baku_Higher_All-Arms_Command_School |
| 1088.0 | 25391.0 | Azerbaijani_Armed_Forces | Russia |
| 1088.0 | 40196.0 | Azerbaijani_Armed_Forces | Transport_in_Azerbaijan |
| 1088.0 | 4764461.0 | Azerbaijani_Armed_Forces | World_War_I |
| 1088.0 | 6.6828259e7 | Azerbaijani_Armed_Forces | Afsharid_Iran |
| 1088.0 | 6.5910891e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Gubadly_Medal |
| 1088.0 | 3249318.0 | Azerbaijani_Armed_Forces | Shaddadids |
| 1088.0 | 6.7101223e7 | Azerbaijani_Armed_Forces | Training_and_Education_Center_of_the_Armed_Forces |
| 1088.0 | 1.3427826e7 | Azerbaijani_Armed_Forces | Cabinet_of_Azerbaijan |
| 1088.0 | 1.0927665e7 | Azerbaijani_Armed_Forces | Internal_Troops_of_Azerbaijan |
| 1088.0 | 39237.0 | Azerbaijani_Armed_Forces | Israel_Defense_Forces |
| 1088.0 | 5.7994574e7 | Azerbaijani_Armed_Forces | Military_Band_Service_of_the_Armed_Forces_of_Azerbaijan |
| 1088.0 | 7.0680595e7 | Azerbaijani_Armed_Forces | 227th_Rifle_Division |
| 1088.0 | 2192452.0 | Azerbaijani_Armed_Forces | 4th_Army_(Soviet_Union) |
| 1088.0 | 2.3411067e7 | Azerbaijani_Armed_Forces | Ganja_Air_Base |
| 1088.0 | 581195.0 | Azerbaijani_Armed_Forces | Copyright_status_of_works_by_the_federal_government_of_the_United_States |
| 1088.0 | 7077806.0 | Azerbaijani_Armed_Forces | Orography_of_Azerbaijan |
| 1088.0 | 1177214.0 | Azerbaijani_Armed_Forces | Pennon |
| 1088.0 | 740508.0 | Azerbaijani_Armed_Forces | Republic_of_Azerbaijan |
| 1088.0 | 917076.0 | Azerbaijani_Armed_Forces | Ayaz_Mutallibov |
| 1088.0 | 213173.0 | Azerbaijani_Armed_Forces | Conscripts |
| 1088.0 | 1082.0 | Azerbaijani_Armed_Forces | Geography_of_Azerbaijan |
| 1088.0 | 9526432.0 | Azerbaijani_Armed_Forces | MRAP |
| 1088.0 | 386742.0 | Azerbaijani_Armed_Forces | SA-2 |
| 1088.0 | 6.4343188e7 | Azerbaijani_Armed_Forces | Azerbaijan_High_Military_Aviation_School |
| 1088.0 | 69007.0 | Azerbaijani_Armed_Forces | Military_of_Bhutan |
| 1088.0 | 1.9859966e7 | Azerbaijani_Armed_Forces | Nasosnaya_Air_Base |
| 1088.0 | 1022955.0 | Azerbaijani_Armed_Forces | Supreme_Soviet_of_the_USSR |
| 1088.0 | 6.7262853e7 | Azerbaijani_Armed_Forces | Ali-Agha_Shikhlinski |
| 1088.0 | 1351138.0 | Azerbaijani_Armed_Forces | Elections_in_Azerbaijan |
| 1088.0 | 14650.0 | Azerbaijani_Armed_Forces | Indonesian_National_Armed_Forces |
| 1088.0 | 6.6168931e7 | Azerbaijani_Armed_Forces | Marine_Infantry_of_Azerbaijan |
| 1088.0 | 1300375.0 | Azerbaijani_Armed_Forces | Treaty_on_Conventional_Armed_Forces_in_Europe |
| 1088.0 | 1.8947898e7 | Azerbaijani_Armed_Forces | Amnesty_International |
| 1088.0 | 746.0 | Azerbaijani_Armed_Forces | Azerbaijan |
| 1088.0 | 1.9859938e7 | Azerbaijani_Armed_Forces | Baku_Kala_Air_Base |
| 1088.0 | 1610018.0 | Azerbaijani_Armed_Forces | Hong_Kong_Garrison |
| 1088.0 | 1478175.0 | Azerbaijani_Armed_Forces | Public_holidays_in_Azerbaijan |
| 1088.0 | 69328.0 | Azerbaijani_Armed_Forces | United_Arab_Emirates |
| 1088.0 | 1.0928518e7 | Azerbaijani_Armed_Forces | Azerbaijani_Coast_Guard |
| 1088.0 | 4016533.0 | Azerbaijani_Armed_Forces | National_Assembly_(Azerbaijan) |
| 1088.0 | 1.9510878e7 | Azerbaijani_Armed_Forces | Sitalcay |
| 1088.0 | 7.1403487e7 | Azerbaijani_Armed_Forces | State_Border_Service_(Azerbaijan) |
| 1088.0 | 1.0254803e7 | Azerbaijani_Armed_Forces | Cinema_of_Azerbaijan |
| 1088.0 | 17779.0 | Azerbaijani_Armed_Forces | Lebanese_Armed_Forces |
| 1088.0 | 5.9921988e7 | Azerbaijani_Armed_Forces | Metallurgy_in_Azerbaijan |
| 1088.0 | 27479.0 | Azerbaijani_Armed_Forces | Syrian_Armed_Forces |
| 1088.0 | 6.7613776e7 | Azerbaijani_Armed_Forces | TecSAR |
| 1088.0 | 4116970.0 | Azerbaijani_Armed_Forces | Central_Bank_of_Azerbaijan |
| 1088.0 | 400853.0 | Azerbaijani_Armed_Forces | Hero_of_the_Soviet_Union |
| 1088.0 | 1.6278429e7 | Azerbaijani_Armed_Forces | Outline_of_Azerbaijan |
| 1088.0 | 1.3634062e7 | Azerbaijani_Armed_Forces | Constitution_of_Azerbaijan |
| 1088.0 | 7.1858151e7 | Azerbaijani_Armed_Forces | Dollyar_Air_Base |
| 1088.0 | 2.812722e7 | Azerbaijani_Armed_Forces | List_of_earthquakes_in_Azerbaijan |
| 1088.0 | 23235.0 | Azerbaijani_Armed_Forces | Pakistan |
| 1088.0 | 1049084.0 | Azerbaijani_Armed_Forces | U.S._National_Guard |
| 1088.0 | 6.1912815e7 | Azerbaijani_Armed_Forces | \"90th_Anniversary_of_the_Armed_Forces_of_Azerbaijan_(1918–2008)\"_Medal |
| 1088.0 | 67658.0 | Azerbaijani_Armed_Forces | Bahrain_Defence_Force |
| 1238.0 | 44975.0 | Atomic_bomb | Phrase |
| 1238.0 | 21785.0 | Atomic_bomb | Nuclear_weapon |
| 1342.0 | 1400.0 | A.D | Anno_Domini |
| 1580.0 | 1.8935551e7 | Alcidamas | Public_domain |
| 1580.0 | 168260.0 | Alcidamas | Isocrates |
| 1580.0 | 99665.0 | Alcidamas | Friedrich_Blass |
| 1580.0 | 6.3435015e7 | Alcidamas | JSTOR_(identifier) |
| 1580.0 | 1.5103874e7 | Alcidamas | Contest_of_Homer_and_Hesiod |
| 1580.0 | 22537.0 | Alcidamas | Odysseus |
| 1580.0 | 2093019.0 | Alcidamas | Palamedes_(mythology) |
| 1580.0 | 6.371749e7 | Alcidamas | VIAF_(identifier) |
| 1580.0 | 30059.0 | Alcidamas | Troy |
| 1580.0 | 2.6273281e7 | Alcidamas | Messenia_(ancient_region) |
| 1580.0 | 1216.0 | Alcidamas | Athens |
| 1580.0 | 6771651.0 | Alcidamas | Teubner |
| 1580.0 | 5.5049264e7 | Alcidamas | ISBN_(identifier) |
| 1580.0 | 72624.0 | Alcidamas | Encyclopædia_Britannica_Eleventh_Edition |
| 1580.0 | 6.3826803e7 | Alcidamas | ISNI_(identifier) |
| 1580.0 | 2.4392429e7 | Alcidamas | Commentaria_in_Aristotelem_Graeca |
| 1580.0 | 49646.0 | Alcidamas | Sophist |
| 1580.0 | 11887.0 | Alcidamas | Greek_language |
| 1580.0 | 6.3717472e7 | Alcidamas | SUDOC_(identifier) |
| 1580.0 | 13621.0 | Alcidamas | Hadrian |
| 1580.0 | 1692816.0 | Alcidamas | Rhetoric_(Aristotle) |
| 1580.0 | 308.0 | Alcidamas | Aristotle |
| 1580.0 | 100109.0 | Alcidamas | John_Pentland_Mahaffy |
| 1580.0 | 5.5017667e7 | Alcidamas | Muse |
| 1580.0 | 6.5715134e7 | Alcidamas | RERO_(identifier) |
| 1580.0 | 25447.0 | Alcidamas | Rhetoric |
| 1580.0 | 1715161.0 | Alcidamas | Aeolis |
| 1580.0 | 4682035.0 | Alcidamas | Martin_Litchfield_West |
| 1580.0 | 22022.0 | Alcidamas | Nietzsche |
| 1580.0 | 4633006.0 | Alcidamas | Elaea_(Aeolis) |
| 1580.0 | 66540.0 | Alcidamas | Ancient_Greece |
| 1580.0 | 98394.0 | Alcidamas | Gorgias |
| 1645.0 | 3.7091344e7 | Ibn_al-Haytham | Al-Harith_ibn_Kalada |
| 1645.0 | 3.5633616e7 | Ibn_al-Haytham | Gholamhossein_Ebrahimi_Dinani |
| 1645.0 | 3022468.0 | Ibn_al-Haytham | Qalb |
| 1645.0 | 1.6424083e7 | Ibn_al-Haytham | 59239_Alhazen |
| 1645.0 | 5089990.0 | Ibn_al-Haytham | Ahmad_al-Buni |
| 1645.0 | 482939.0 | Ibn_al-Haytham | Al-Hakim_bi-Amr_Allah |
| 1645.0 | 2.7361247e7 | Ibn_al-Haytham | Al-Kharaqī |
| 1645.0 | 4.9712277e7 | Ibn_al-Haytham | Al-Ruhawi |
| 1645.0 | 5.6447306e7 | Ibn_al-Haytham | Alam_al-Din_al-Hanafi |
| 1645.0 | 51518.0 | Ibn_al-Haytham | Dam |
| 1645.0 | 1602822.0 | Ibn_al-Haytham | Haji_Bektash_Veli |
| 1645.0 | 1467830.0 | Ibn_al-Haytham | Ibn_Hazm |
| 1645.0 | 14810.0 | Ibn_al-Haytham | Islamic_calendar |
| 1645.0 | 1.3861753e7 | Ibn_al-Haytham | Said_al-Andalusi |
| 1645.0 | 1917134.0 | Ibn_al-Haytham | Sultan_Ali_Khorasani |
| 1645.0 | 30503.0 | Ibn_al-Haytham | Theology |
| 1645.0 | 5.2932896e7 | Ibn_al-Haytham | Abd_al-Latif_al-Baghdadi_(medieval_writer) |
| 1645.0 | 1134.0 | Ibn_al-Haytham | Analysis |
| 1645.0 | 6.9094489e7 | Ibn_al-Haytham | Fakhr_al-Din_al-Akhlati |
| 1645.0 | 3.2144014e7 | Ibn_al-Haytham | Ibn_Hamza_al-Maghribi |
| 1645.0 | 272074.0 | Ibn_al-Haytham | Ibn_Taymiyyah |
| 1645.0 | 14533.0 | Ibn_al-Haytham | India |
| 1645.0 | 5.613996e7 | Ibn_al-Haytham | Khoja_Akhmet_Yassawi |
| 1645.0 | 24714.0 | Ibn_al-Haytham | Precession |
| 1645.0 | 23979.0 | Ibn_al-Haytham | Ptolemy |
| 1645.0 | 1102000.0 | Ibn_al-Haytham | Shen_Kuo |
| 1645.0 | 207547.0 | Ibn_al-Haytham | Thābit_ibn_Qurra |
| 1645.0 | 7.2112001e7 | Ibn_al-Haytham | Abdollah_ibn_Bukhtishu |
| 1645.0 | 196242.0 | Ibn_al-Haytham | Averroism |
| 1645.0 | 1864889.0 | Ibn_al-Haytham | Cosmology |
| 1645.0 | 302794.0 | Ibn_al-Haytham | Depth_perception |
| 1645.0 | 1.6770522e7 | Ibn_al-Haytham | Fathullah_Shirazi |
| 1645.0 | 2.8645073e7 | Ibn_al-Haytham | Ibn_al-Yasamin |
| 1645.0 | 142601.0 | Ibn_al-Haytham | John_Peckham |
| 1645.0 | 5762980.0 | Ibn_al-Haytham | Nisba_(onomastics) |
| 1645.0 | 6.2773262e7 | Ibn_al-Haytham | Sadr_al-Shari'a_al-Asghar |
| 1645.0 | 1741183.0 | Ibn_al-Haytham | Yaʿqūb_ibn_Ṭāriq |
| 1645.0 | 64203.0 | Ibn_al-Haytham | Zaragoza |
| 1645.0 | 1.171752e7 | Ibn_al-Haytham | Al-Khazini |
| 1645.0 | 5916.0 | Ibn_al-Haytham | Circumference |
| 1645.0 | 3.1562722e7 | Ibn_al-Haytham | Ibn_al-Tilmidh |
| 1645.0 | 86820.0 | Ibn_al-Haytham | Khalid_ibn_Abd_al‐Malik_al‐Marwarrudhi |
| 1645.0 | 4.8575985e7 | Ibn_al-Haytham | Lune_(mathematics) |
| 1645.0 | 21527.0 | Ibn_al-Haytham | Number_theory |
| 1645.0 | 1.1712287e7 | Ibn_al-Haytham | Riaz_Ahmed_Gohar_Shahi |
| 1645.0 | 145242.0 | Ibn_al-Haytham | The_Canon_of_Medicine |
| 1645.0 | 2.3797577e7 | Ibn_al-Haytham | The_Daily_Telegraph |
| 1645.0 | 3304608.0 | Ibn_al-Haytham | Astronomy_in_the_medieval_Islamic_world |
| 1645.0 | 3.9292005e7 | Ibn_al-Haytham | Ibn_Hindu |
| 1645.0 | 2.7578404e7 | Ibn_al-Haytham | Ibn_al‐Ha'im_al‐Ishbili |
| 1645.0 | 3.8205689e7 | Ibn_al-Haytham | Latinization_of_names |
| 1645.0 | 22483.0 | Ibn_al-Haytham | Optics |
| 1645.0 | 7151472.0 | Ibn_al-Haytham | Peter_M._Neumann |
| 1645.0 | 3364761.0 | Ibn_al-Haytham | Ray_(optics) |
| 1645.0 | 3144227.0 | Ibn_al-Haytham | Sufi_philosophy |
| 1645.0 | 3335703.0 | Ibn_al-Haytham | Al-Samawal_al-Maghribi |
| 1645.0 | 791.0 | Ibn_al-Haytham | Asteroid |
| 1645.0 | 1104592.0 | Ibn_al-Haytham | Ibn_Tufail |
| 1645.0 | 15513.0 | Ibn_al-Haytham | Islamic_eschatology |
| 1645.0 | 63140.0 | Ibn_al-Haytham | Jabir_ibn_Hayyan |
| 1645.0 | 80135.0 | Ibn_al-Haytham | Rutgers_University |
| 1645.0 | 2042347.0 | Ibn_al-Haytham | Zakariya_al-Qazwini |
| 1645.0 | 414271.0 | Ibn_al-Haytham | Abū_Isḥāq_Ibrāhīm_al-Zarqālī |
| 1645.0 | 4385475.0 | Ibn_al-Haytham | Ancient_Greek_astronomy |
| 1645.0 | 3.6143542e7 | Ibn_al-Haytham | Ibn_al-Majdi |
| 1645.0 | 5496025.0 | Ibn_al-Haytham | Ilm_(Arabic) |
| 1645.0 | 175040.0 | Ibn_al-Haytham | Al-Farabi |
| 1645.0 | 2.2341957e7 | Ibn_al-Haytham | Ali_al-Ridha |
| 1645.0 | 1778258.0 | Ibn_al-Haytham | Alī_ibn_Ahmad_al-Nasawī |
| 1645.0 | 4831143.0 | Ibn_al-Haytham | Ancient_Greek_medicine |
| 1645.0 | 157898.0 | Ibn_al-Haytham | Eye |
| 1645.0 | 1782358.0 | Ibn_al-Haytham | Ibn_Abi_Sadiq |
| 1645.0 | 6328738.0 | Ibn_al-Haytham | Ibn_Mu'adh_al-Jayyani |
| 1645.0 | 5.6793125e7 | Ibn_al-Haytham | Ibn_al‐Raqqam |
| 1645.0 | 23231.0 | Ibn_al-Haytham | Parabola |
| 1645.0 | 7211548.0 | Ibn_al-Haytham | Predestination_in_Islam |
| 1645.0 | 44328.0 | Ibn_al-Haytham | Ulugh_Beg |
| 1645.0 | 2.3538754e7 | Ibn_al-Haytham | Wayback_Machine |
| 1645.0 | 2.8635358e7 | Ibn_al-Haytham | Abu_Muqri_Mohammed_al-Battiwi |
| 1645.0 | 59861.0 | Ibn_al-Haytham | Experiment |
| 1645.0 | 2231772.0 | Ibn_al-Haytham | Ibn_Sahl_(mathematician) |
| 1645.0 | 1.7940058e7 | Ibn_al-Haytham | Mohsen_Fayz_Kashani |
| 1645.0 | 2160807.0 | Ibn_al-Haytham | Muhammad_ibn_Zakariya_al-Razi |
| 1645.0 | 2.5343863e7 | Ibn_al-Haytham | Nur_al-Din_Bimaristan |
| 1645.0 | 192520.0 | Ibn_al-Haytham | Tawhid |
| 1645.0 | 439770.0 | Ibn_al-Haytham | Abu_Nasr_Mansur |
| 1645.0 | 4158200.0 | Ibn_al-Haytham | Asabiyyah |
| 1645.0 | 236674.0 | Ibn_al-Haytham | Ayurveda |
| 1645.0 | 380406.0 | Ibn_al-Haytham | Comparative_psychology |
| 1645.0 | 13450.0 | Ibn_al-Haytham | Hebrew_language |
| 1645.0 | 8066479.0 | Ibn_al-Haytham | Maslaha |
| 1645.0 | 2042612.0 | Ibn_al-Haytham | Masʽud_ibn_Muhammad_Sijzi |
| 1645.0 | 19323.0 | Ibn_al-Haytham | Middle_East |
| 1645.0 | 53497.0 | Ibn_al-Haytham | Optical_illusion |
| 1645.0 | 1189485.0 | Ibn_al-Haytham | Abu_al-Wafa'_Buzjani |
| 1645.0 | 1.0879533e7 | Ibn_al-Haytham | Aja'ib_al-Makhluqat |
| 1645.0 | 355643.0 | Ibn_al-Haytham | Al-Andalus |
| 1645.0 | 4.3402124e7 | Ibn_al-Haytham | Commentary_on_Anatomy_in_Avicenna's_Canon |
| 1645.0 | 8451005.0 | Ibn_al-Haytham | Haji_Bayram_Veli |
| 1645.0 | 1.0082768e7 | Ibn_al-Haytham | Hockney–Falco_thesis |
| 1645.0 | 2.6634144e7 | Ibn_al-Haytham | Ibn_Sina_Academy_of_Medieval_Medicine_and_Sciences |
| 1645.0 | 1741520.0 | Ibn_al-Haytham | Kamāl_al-Dīn_al-Fārisī |
| 1645.0 | 3.2078146e7 | Ibn_al-Haytham | Muhammad_ibn_Aslam_Al-Ghafiqi |
| 1645.0 | 2042047.0 | Ibn_al-Haytham | Burhan-ud-din_Kermani |
| 1645.0 | 6596725.0 | Ibn_al-Haytham | Equatorium |
| 1645.0 | 5553121.0 | Ibn_al-Haytham | Latin_translations_of_the_12th_century |
| 1645.0 | 21664.0 | Ibn_al-Haytham | Nebula |
| 1645.0 | 4.7324624e7 | Ibn_al-Haytham | Sadr_ad-Din_Dashtaki |
| 1645.0 | 1.0536691e7 | Ibn_al-Haytham | Thabit_ibn_Qurra |
| 1645.0 | 2428.0 | Ibn_al-Haytham | Analog_computer |
| 1645.0 | 1.1453823e7 | Ibn_al-Haytham | Byzantine_science |
| 1645.0 | 6.7975278e7 | Ibn_al-Haytham | History_of_science_in_the_Renaissance |
| 1645.0 | 166162.0 | Ibn_al-Haytham | Islamic_philosophy |
| 1645.0 | 5290954.0 | Ibn_al-Haytham | Abu_al-Bayan_ibn_al-Mudawwar |
| 1645.0 | 2045119.0 | Ibn_al-Haytham | Abu_al-Hakam_al-Kirmani |
| 1645.0 | 2.8208073e7 | Ibn_al-Haytham | Afdal_al-Din_Kashani |
| 1645.0 | 2643686.0 | Ibn_al-Haytham | Aga_Khan_University |
| 1645.0 | 174410.0 | Ibn_al-Haytham | Armillary_sphere |
| 1645.0 | 383129.0 | Ibn_al-Haytham | Celestial_spheres |
| 1645.0 | 48167.0 | Ibn_al-Haytham | Congruence_relation |
| 1645.0 | 244588.0 | Ibn_al-Haytham | Heliocentrism |
| 1645.0 | 4.7787936e7 | Ibn_al-Haytham | Schema_for_horizontal_dials |
| 1645.0 | 1.3224789e7 | Ibn_al-Haytham | Sextant_(astronomy) |
| 1645.0 | 1253603.0 | Ibn_al-Haytham | Abu_Ma'shar_al-Balkhi |
| 1645.0 | 1782729.0 | Ibn_al-Haytham | Al-Mahani |
| 1645.0 | 1587482.0 | Ibn_al-Haytham | Al-Qabisi |
| 1645.0 | 1.1089309e7 | Ibn_al-Haytham | Al-Ḥajjāj_ibn_Yūsuf_ibn_Maṭar |
| 1645.0 | 1271962.0 | Ibn_al-Haytham | Billiard_table |
| 1645.0 | 1.1828715e7 | Ibn_al-Haytham | Book_of_Optics |
| 1645.0 | 5286621.0 | Ibn_al-Haytham | Ephraim_ibn_al-Za'faran |
| 1645.0 | 3.0864628e7 | Ibn_al-Haytham | European_science_in_the_Middle_Ages |
| 1645.0 | 719601.0 | Ibn_al-Haytham | MIT_Press |
| 1645.0 | 1.3692155e7 | Ibn_al-Haytham | Philosophy |
| 1645.0 | 3.5216988e7 | Ibn_al-Haytham | Rajab_Ali_Tabrizi |
| 1645.0 | 39420.0 | Ibn_al-Haytham | Right_triangle |
| 1645.0 | 2538627.0 | Ibn_al-Haytham | Yusuf_al-Mu'taman_ibn_Hud |
| 1645.0 | 2.8820168e7 | Ibn_al-Haytham | Abd_al-Rahman_al-Jadiri |
| 1645.0 | 1767004.0 | Ibn_al-Haytham | Abu_Mansur_Muwaffaq |
| 1645.0 | 1.9217647e7 | Ibn_al-Haytham | Abul_Qasim_ibn_Mohammed_al-Ghassani |
| 1645.0 | 1741220.0 | Ibn_al-Haytham | Bukhtishu |
| 1645.0 | 3.2142292e7 | Ibn_al-Haytham | Ibrahim_ibn_Baks |
| 1645.0 | 1782585.0 | Ibn_al-Haytham | Jabril_ibn_Bukhtishu |
| 1645.0 | 2527706.0 | Ibn_al-Haytham | Mir_Damad |
| 1645.0 | 2984836.0 | Ibn_al-Haytham | Ophthalmology_in_the_medieval_Islamic_world |
| 1645.0 | 22308.0 | Ibn_al-Haytham | Oxford |
| 1645.0 | 50585.0 | Ibn_al-Haytham | Philadelphia |
| 1645.0 | 1.9883086e7 | Ibn_al-Haytham | Philip_Sherrard |
| 1645.0 | 230250.0 | Ibn_al-Haytham | The_Ascent_of_Man |
| 1645.0 | 1964954.0 | Ibn_al-Haytham | University_of_Chicago_Press |
| 1645.0 | 3.4781942e7 | Ibn_al-Haytham | Abd_al‐Wajid |
| 1645.0 | 1766622.0 | Ibn_al-Haytham | Abolfadl_Harawi |
| 1645.0 | 6.082025e7 | Ibn_al-Haytham | Al-Hawi |
| 1645.0 | 665027.0 | Ibn_al-Haytham | Fourth_power |
| 1645.0 | 2.3568467e7 | Ibn_al-Haytham | Rashidun_al-Suri |
| 1645.0 | 4647532.0 | Ibn_al-Haytham | Shams_al-Din_Abu_Abd_Allah_al-Khalili |
| 1645.0 | 2.4712247e7 | Ibn_al-Haytham | Ya'ish_ibn_Ibrahim_al-Umawi |
| 1645.0 | 3861353.0 | Ibn_al-Haytham | Babylonian_mathematics |
| 1645.0 | 1.7365905e7 | Ibn_al-Haytham | Dawūd_al-Qayṣarī |
| 1645.0 | 6.0347068e7 | Ibn_al-Haytham | Jalaladdin_Davani |
| 1645.0 | 18836.0 | Ibn_al-Haytham | Middle_Ages |
| 1645.0 | 3022453.0 | Ibn_al-Haytham | Nafs |
| 1645.0 | 5186903.0 | Ibn_al-Haytham | Tusi_couple |
| 1645.0 | 2674.0 | Ibn_al-Haytham | Abd_al-Latif_al-Baghdadi |
| 1645.0 | 4849234.0 | Ibn_al-Haytham | Encyclopedia_of_the_Brethren_of_Purity |
| 1645.0 | 9239.0 | Ibn_al-Haytham | Europe |
| 1645.0 | 150257.0 | Ibn_al-Haytham | Feigned_madness |
| 1645.0 | 577201.0 | Ibn_al-Haytham | Frithjof_Schuon |
| 1645.0 | 2.6482067e7 | Ibn_al-Haytham | Kepler |
| 1645.0 | 17730.0 | Ibn_al-Haytham | Latin |
| 1645.0 | 145845.0 | Ibn_al-Haytham | Paraboloid |
| 1645.0 | 25525.0 | Ibn_al-Haytham | René_Descartes |
| 1645.0 | 884495.0 | Ibn_al-Haytham | Bibliothèque_nationale |
| 1645.0 | 86728.0 | Ibn_al-Haytham | Bodleian_Library |
| 1645.0 | 3515519.0 | Ibn_al-Haytham | Lambert_quadrilateral |
| 1645.0 | 3.3383114e7 | Ibn_al-Haytham | Muhammad_ibn_Abi_Bakr_al‐Farisi |
| 1645.0 | 201359.0 | Ibn_al-Haytham | Squaring_the_circle |
| 1645.0 | 1.854116e7 | Ibn_al-Haytham | Abū_Rayhān_al-Bīrūnī |
| 1645.0 | 804218.0 | Ibn_al-Haytham | Astronomical_clock |
| 1645.0 | 6.3434964e7 | Ibn_al-Haytham | CiteSeerX_(identifier) |
| 1645.0 | 316410.0 | Ibn_al-Haytham | Compass_rose |
| 1645.0 | 9417.0 | Ibn_al-Haytham | Euclidean_geometry |
| 1645.0 | 8232680.0 | Ibn_al-Haytham | Ibn_al-Jazzar |
| 1645.0 | 2742403.0 | Ibn_al-Haytham | Mathematical_Association |
| 1645.0 | 3.1327881e7 | Ibn_al-Haytham | Na'im_ibn_Musa |
| 1645.0 | 25948.0 | Ibn_al-Haytham | Refraction |
| 1645.0 | 3.2309672e7 | Ibn_al-Haytham | Abd_al-Razzaq_Lahiji |
| 1645.0 | 192230.0 | Ibn_al-Haytham | Almanac |
| 1645.0 | 2426527.0 | Ibn_al-Haytham | Ibn_al-Nafis |
| 1645.0 | 1.3433019e7 | Ibn_al-Haytham | Intromission_theory |
| 1645.0 | 1.1011952e7 | Ibn_al-Haytham | Kamal_al-Din_al-Farisi |
| 1645.0 | 94721.0 | Ibn_al-Haytham | Robert_Grosseteste |
| 1645.0 | 645208.0 | Ibn_al-Haytham | Equant |
| 1645.0 | 7.1175005e7 | Ibn_al-Haytham | Mahmud_Hudayi |
| 1645.0 | 1.5233821e7 | Ibn_al-Haytham | Psychology_in_the_medieval_Islamic_world |
| 1645.0 | 2.1691805e7 | Ibn_al-Haytham | Serapion_the_Younger |
| 1645.0 | 7627.0 | Ibn_al-Haytham | The_Canterbury_Tales |
| 1645.0 | 7.1370184e7 | Ibn_al-Haytham | Ali_ibn_Yusuf_al-Ilaqi |
| 1645.0 | 102182.0 | Ibn_al-Haytham | Celestial_mechanics |
| 1645.0 | 2695116.0 | Ibn_al-Haytham | Contemporary_Islamic_philosophy |
| 1645.0 | 6733941.0 | Ibn_al-Haytham | Friedrich_Risner |
| 1645.0 | 12326.0 | Ibn_al-Haytham | Galen |
| 1645.0 | 1232660.0 | Ibn_al-Haytham | Syed_Muhammad_Naquib_al-Attas |
| 1645.0 | 5.5495903e7 | Ibn_al-Haytham | 1001_Inventions |
| 1645.0 | 1.0730931e7 | Ibn_al-Haytham | Al-Mu'taman_ibn_Hud |
| 1645.0 | 1174529.0 | Ibn_al-Haytham | Al-Tasrif |
| 1645.0 | 4.3350725e7 | Ibn_al-Haytham | Euclid–Euler_theorem |
| 1645.0 | 360726.0 | Ibn_al-Haytham | Planisphere |
| 1645.0 | 6.0782023e7 | Ibn_al-Haytham | Shmuel_Sambursky |
| 1645.0 | 2.1800807e7 | Ibn_al-Haytham | Zakhireye_Khwarazmshahi |
| 1645.0 | 3.2111866e7 | Ibn_al-Haytham | Ibn_Abi_Ramtha_al-Tamimi |
| 1645.0 | 6.3435015e7 | Ibn_al-Haytham | JSTOR_(identifier) |
| 1645.0 | 6.1571532e7 | Ibn_al-Haytham | Lens_(optics) |
| 1645.0 | 39098.0 | Ibn_al-Haytham | Physical_law |
| 1645.0 | 7.1245601e7 | Ibn_al-Haytham | Shahab_al-Din_Yahya_ibn_Habash_Suhrawardi |
| 1645.0 | 2.4923294e7 | Ibn_al-Haytham | Ulugh_Beg_Observatory |
| 1645.0 | 1.3728826e7 | Ibn_al-Haytham | Abu_al-Hassan_al-Amiri |
| 1645.0 | 3394642.0 | Ibn_al-Haytham | Dioptra |
| 1645.0 | 4.3947436e7 | Ibn_al-Haytham | Huihui_Lifa |
| 1645.0 | 2781944.0 | Ibn_al-Haytham | Indian_astronomy |
| 1645.0 | 5.9899089e7 | Ibn_al-Haytham | Motion_(physics) |
| 1645.0 | 21244.0 | Ibn_al-Haytham | Nile |
| 1645.0 | 1.0621204e7 | Ibn_al-Haytham | Sabuncuoğlu_Şerafeddin |
| 1645.0 | 1418949.0 | Ibn_al-Haytham | Springer_Science+Business_Media |
| 1645.0 | 27680.0 | Ibn_al-Haytham | Supernova |
| 1645.0 | 60919.0 | Ibn_al-Haytham | University_of_London |
| 1645.0 | 5719662.0 | Ibn_al-Haytham | A._I._Sabra |
| 1645.0 | 2.2883647e7 | Ibn_al-Haytham | Abu_Ali_al-Khayyat |
| 1645.0 | 1759881.0 | Ibn_al-Haytham | Abu_Ja'far_al-Khazin |
| 1645.0 | 1739664.0 | Ibn_al-Haytham | Al-Karaji |
| 1645.0 | 1.3956265e7 | Ibn_al-Haytham | Ancient_Iranian_medicine |
| 1645.0 | 47474.0 | Ibn_al-Haytham | Aperture |
| 1645.0 | 221461.0 | Ibn_al-Haytham | Hevelius |
| 1645.0 | 6.7610731e7 | Ibn_al-Haytham | Hussam_al-Din_al-Jarrahi |
| 1645.0 | 2163566.0 | Ibn_al-Haytham | Nafi_ibn_al-Harith |
| 1645.0 | 1.3401485e7 | Ibn_al-Haytham | Selenographia |
| 1645.0 | 2.755431e7 | Ibn_al-Haytham | Abu_Jafar_ibn_Harun_al-Turjali |
| 1645.0 | 353215.0 | Ibn_al-Haytham | Al-Zahrawi |
| 1645.0 | 39316.0 | Ibn_al-Haytham | Compass |
| 1645.0 | 241528.0 | Ibn_al-Haytham | Jacob_Bronowski |
| 1645.0 | 3.9127918e7 | Ibn_al-Haytham | Mohammed_ibn_Abdun_al-Jabali |
| 1645.0 | 204511.0 | Ibn_al-Haytham | Scientific_skepticism |
| 1645.0 | 5.4447016e7 | Ibn_al-Haytham | Victor_J._Katz |
| 1645.0 | 2.3442952e7 | Ibn_al-Haytham | Yang_Guangxian |
| 1645.0 | 1560514.0 | Ibn_al-Haytham | Ahmad_ibn_Yusuf |
| 1645.0 | 8230922.0 | Ibn_al-Haytham | Hamid_al-Din_al-Kirmani |
| 1645.0 | 6785051.0 | Ibn_al-Haytham | History_of_trigonometry |
| 1645.0 | 5.7151342e7 | Ibn_al-Haytham | Ibn_Ishaq_al-Tunisi |
| 1645.0 | 1.5515167e7 | Ibn_al-Haytham | Ibn_al-Kattani |
| 1645.0 | 1830000.0 | Ibn_al-Haytham | Inundation |
| 1645.0 | 3035257.0 | Ibn_al-Haytham | Masarjawaih |
| 1645.0 | 6.4652504e7 | Ibn_al-Haytham | Zaynab_al-Awadiya |
| 1645.0 | 2.2848684e7 | Ibn_al-Haytham | Abu_Sulayman_Sijistani |
| 1645.0 | 2.1508913e7 | Ibn_al-Haytham | Abu_ul-Ala_Shirazi |
| 1645.0 | 3.107765e7 | Ibn_al-Haytham | G._J._Toomer |
| 1645.0 | 209717.0 | Ibn_al-Haytham | Madrasa |
| 1645.0 | 3304216.0 | Ibn_al-Haytham | Mathematics_in_the_medieval_Islamic_world |
| 1645.0 | 251713.0 | Ibn_al-Haytham | Qibla |
| 1645.0 | 25532.0 | Ibn_al-Haytham | Renaissance |
| 1645.0 | 2042154.0 | Ibn_al-Haytham | Shaykh_Muhammad_ibn_Thaleb |
| 1645.0 | 3225840.0 | Ibn_al-Haytham | Sublunary_sphere |
| 1645.0 | 7724903.0 | Ibn_al-Haytham | Ali_ibn_Ridwan |
| 1645.0 | 4396171.0 | Ibn_al-Haytham | Earth's_rotation |
| 1645.0 | 12787.0 | Ibn_al-Haytham | Geoffrey_Chaucer |
| 1645.0 | 1492381.0 | Ibn_al-Haytham | Ibn_Al-Thahabi |
| 1645.0 | 23253.0 | Ibn_al-Haytham | Parallax |
| 1645.0 | 1.9594028e7 | Ibn_al-Haytham | Theoretical_physics |
| 1645.0 | 5.3090162e7 | Ibn_al-Haytham | Yahya_ibn_Abi_Mansur |
| 1645.0 | 2.7579858e7 | Ibn_al-Haytham | Abu_al-Salt |
| 1645.0 | 3.5777337e7 | Ibn_al-Haytham | Cosmos:_A_Spacetime_Odyssey |
| 1645.0 | 4512160.0 | Ibn_al-Haytham | Flooding |
| 1645.0 | 1.4973076e7 | Ibn_al-Haytham | Medical_Renaissance |
| 1645.0 | 6.1415405e7 | Ibn_al-Haytham | Muhammad_Husayn_Tabataba'i |
| 1645.0 | 1.6593123e7 | Ibn_al-Haytham | Nader_El-Bizri |
| 1645.0 | 5290740.0 | Ibn_al-Haytham | Sa'ad_al-Dawla |
| 1645.0 | 982540.0 | Ibn_al-Haytham | Taqi_ad-Din_Muhammad_ibn_Ma'ruf |
| 1645.0 | 5.549544e7 | Ibn_al-Haytham | Alhazen_(disambiguation) |
| 1645.0 | 2.4464339e7 | Ibn_al-Haytham | Arab |
| 1645.0 | 2.8700369e7 | Ibn_al-Haytham | Ibn_Ghazi_al-Miknasi |
| 1645.0 | 199169.0 | Ibn_al-Haytham | Ibn_Khaldun |
| 1645.0 | 1.9018638e7 | Ibn_al-Haytham | Islamic_mathematics |
| 1645.0 | 18079.0 | Ibn_al-Haytham | Leonardo_da_Vinci |
| 1645.0 | 2.7405151e7 | Ibn_al-Haytham | Muhammad_al-Rudani |
| 1645.0 | 6.7427596e7 | Ibn_al-Haytham | Qadi_Mir_Husayn_al-Maybudi |
| 1645.0 | 4.0311818e7 | Ibn_al-Haytham | Roger_Highfield |
| 1645.0 | 207174.0 | Ibn_al-Haytham | Triangulation |
| 1645.0 | 1782310.0 | Ibn_al-Haytham | Abu_Said_Gorgani |
| 1645.0 | 6.4988709e7 | Ibn_al-Haytham | Buyid_Emirate |
| 1645.0 | 2227778.0 | Ibn_al-Haytham | Catoptrics |
| 1645.0 | 438004.0 | Ibn_al-Haytham | Psychophysics |
| 1645.0 | 5.3082933e7 | Ibn_al-Haytham | Abu_al-Hasan_al-Ahwazi |
| 1645.0 | 7718539.0 | Ibn_al-Haytham | Al-'Adudi_Hospital |
| 1645.0 | 3.1076646e7 | Ibn_al-Haytham | Al_Achsasi_al_Mouakket |
| 1645.0 | 1.0923902e7 | Ibn_al-Haytham | Dream_Pool_Essays |
| 1645.0 | 3467826.0 | Ibn_al-Haytham | House_of_Knowledge |
| 1645.0 | 1.327905e7 | Ibn_al-Haytham | Ibn_Butlan |
| 1645.0 | 5741464.0 | Ibn_al-Haytham | Ibn_al-Baytar |
| 1645.0 | 685895.0 | Ibn_al-Haytham | René_Guénon |
| 1645.0 | 2.3477491e7 | Ibn_al-Haytham | Sadr_al-Din_al-Qunawi |
| 1645.0 | 1768580.0 | Ibn_al-Haytham | Sharaf_al-Din_al-Tusi |
| 1645.0 | 2.4703916e7 | Ibn_al-Haytham | Sullam_al-sama' |
| 1645.0 | 1245987.0 | Ibn_al-Haytham | Ziauddin_Sardar |
| 1645.0 | 91173.0 | Ibn_al-Haytham | Axial_tilt |
| 1645.0 | 9770.0 | Ibn_al-Haytham | Eclipse |
| 1645.0 | 152827.0 | Ibn_al-Haytham | Han_Chinese |
| 1645.0 | 18365.0 | Ibn_al-Haytham | Luminance |
| 1645.0 | 1.3352174e7 | Ibn_al-Haytham | Quadrant_(instrument) |
| 1645.0 | 2.7375401e7 | Ibn_al-Haytham | Sanad_ibn_Ali |
| 1645.0 | 4391548.0 | Ibn_al-Haytham | Sinān_ibn_al-Fatḥ |
| 1645.0 | 8656923.0 | Ibn_al-Haytham | Ahmad_Fardid |
| 1645.0 | 4.9107555e7 | Ibn_al-Haytham | Al-Furqan_Islamic_Heritage_Foundation |
| 1645.0 | 5.2173672e7 | Ibn_al-Haytham | Al-Mubashshir_ibn_Fatik |
| 1645.0 | 5.3090036e7 | Ibn_al-Haytham | Al-Wabkanawi |
| 1645.0 | 2375470.0 | Ibn_al-Haytham | Cleomedes |
| 1645.0 | 1627160.0 | Ibn_al-Haytham | Linda_Hall_Library |
| 1645.0 | 1.7944118e7 | Ibn_al-Haytham | Physics_in_the_medieval_Islamic_world |
| 1645.0 | 23313.0 | Ibn_al-Haytham | Piri_Reis |
| 1645.0 | 2014775.0 | Ibn_al-Haytham | Qutb_al-Din_al-Shirazi |
| 1645.0 | 2.1786641e7 | Ibn_al-Haytham | UNESCO |
| 1645.0 | 78209.0 | Ibn_al-Haytham | Abu_Bakr_al-Razi |
| 1645.0 | 1822259.0 | Ibn_al-Haytham | Hakim-e-Gilani |
| 1645.0 | 1.0228966e7 | Ibn_al-Haytham | Jabir_ibn_Aflah |
| 1645.0 | 3335321.0 | Ibn_al-Haytham | Shams_al-Din_al-Samarqandi |
| 1645.0 | 6.8869871e7 | Ibn_al-Haytham | Ahi_Evren |
| 1645.0 | 172394.0 | Ibn_al-Haytham | Georg_von_Peuerbach |
| 1645.0 | 294211.0 | Ibn_al-Haytham | Globe |
| 1645.0 | 3302534.0 | Ibn_al-Haytham | List_of_Muslim_philosophers |
| 1645.0 | 1741105.0 | Ibn_al-Haytham | Muḥammad_ibn_Ibrāhīm_al-Fazārī |
| 1645.0 | 985414.0 | Ibn_al-Haytham | Nasir_al-Din_Nasir_Hunzai |
| 1645.0 | 6.3434832e7 | Ibn_al-Haytham | PMC_(identifier) |
| 1645.0 | 16433.0 | Ibn_al-Haytham | Rumi |
| 1645.0 | 1840548.0 | Ibn_al-Haytham | Zayn-e-Attar |
| 1645.0 | 5286542.0 | Ibn_al-Haytham | Abu_Hafsa_Yazid |
| 1645.0 | 2627738.0 | Ibn_al-Haytham | History_of_optics |
| 1645.0 | 165834.0 | Ibn_al-Haytham | Ijtihad |
| 1645.0 | 658084.0 | Ibn_al-Haytham | Magnifying_glass |
| 1645.0 | 2909851.0 | Ibn_al-Haytham | Trepidation |
| 1645.0 | 5438833.0 | Ibn_al-Haytham | 'Abd_al-Hamīd_ibn_Turk |
| 1645.0 | 1792709.0 | Ibn_al-Haytham | Abu_Zayd_al-Balkhi |
| 1645.0 | 1.8716923e7 | Ibn_al-Haytham | Algebra |
| 1645.0 | 3430980.0 | Ibn_al-Haytham | Carl_Brockelmann |
| 1645.0 | 421135.0 | Ibn_al-Haytham | Giambattista_della_Porta |
| 1645.0 | 3.2100257e7 | Ibn_al-Haytham | Ibn_Abi_al-Ashʿath |
| 1645.0 | 5.4285532e7 | Ibn_al-Haytham | Ibn_al-Samh |
| 1645.0 | 3.7487758e7 | Ibn_al-Haytham | Mitsubishi_Electric_Research_Laboratories |
| 1645.0 | 564579.0 | Ibn_al-Haytham | Rashid_al-Din_Hamadani |
| 1645.0 | 233636.0 | Ibn_al-Haytham | Spherical_Earth |
| 1645.0 | 6.371749e7 | Ibn_al-Haytham | VIAF_(identifier) |
| 1645.0 | 146607.0 | Ibn_al-Haytham | Al-Ghazali |
| 1645.0 | 8878908.0 | Ibn_al-Haytham | De_Gradibus |
| 1645.0 | 1.6847243e7 | Ibn_al-Haytham | Egyptian_astronomy |
| 1645.0 | 9550030.0 | Ibn_al-Haytham | History_of_algebra |
| 1645.0 | 7227242.0 | Ibn_al-Haytham | Ibn_Masarra |
| 1645.0 | 1.4950599e7 | Ibn_al-Haytham | Ibn_al-Khatib |
| 1645.0 | 1848052.0 | Ibn_al-Haytham | Indian_mathematics |
| 1645.0 | 6387453.0 | Ibn_al-Haytham | Reza_Davari_Ardakani |
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| 612.0 | Arithmetic_mean | 0.0 | 0.0 | 13635.0 | wikitext | NULL |
| 615.0 | American_Football_Conference | 0.0 | 0.0 | 22184.0 | wikitext | NULL |
| 617.0 | Albert_Gore | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 618.0 | AnEnquiryConcerningHumanUnderstanding | 1.0 | 0.0 | 109.0 | wikitext | NULL |
| 620.0 | Animal_Farm | 0.0 | 0.0 | 77545.0 | wikitext | NULL |
| 621.0 | Amphibian | 0.0 | 0.0 | 156204.0 | wikitext | NULL |
| 622.0 | Albert_Arnold_Gore/Criticisms | 1.0 | 0.0 | 21.0 | wikitext | NULL |
| 624.0 | Alaska | 0.0 | 0.0 | 172107.0 | wikitext | NULL |
| 626.0 | Auteur_Theory_Film | 1.0 | 0.0 | 20.0 | wikitext | NULL |
| 627.0 | Agriculture | 0.0 | 0.0 | 163082.0 | wikitext | NULL |
| 628.0 | Aldous_Huxley | 0.0 | 0.0 | 57618.0 | wikitext | NULL |
| 629.0 | Abstract_Algebra | 1.0 | 0.0 | 95.0 | wikitext | NULL |
| 630.0 | Ada | 0.0 | 0.0 | 3813.0 | wikitext | NULL |
| 632.0 | Aberdeen_(disambiguation) | 0.0 | 0.0 | 7276.0 | wikitext | NULL |
| 633.0 | Algae | 0.0 | 0.0 | 90619.0 | wikitext | NULL |
| 634.0 | Analysis_of_variance | 0.0 | 0.0 | 55132.0 | wikitext | NULL |
| 635.0 | ANOVA | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 639.0 | Alkane | 0.0 | 0.0 | 73113.0 | wikitext | NULL |
| 640.0 | Appellate_procedure_in_the_United_States | 0.0 | 0.0 | 27615.0 | wikitext | NULL |
| 642.0 | Answer_(law) | 0.0 | 0.0 | 2765.0 | wikitext | NULL |
| 643.0 | Appellate_court | 0.0 | 0.0 | 11978.0 | wikitext | NULL |
| 644.0 | Arithmetic_and_logic_unit | 1.0 | 0.0 | 35.0 | wikitext | NULL |
| 648.0 | Actress | 1.0 | 0.0 | 125.0 | wikitext | NULL |
| 649.0 | Arraignment | 0.0 | 0.0 | 10523.0 | wikitext | NULL |
| 651.0 | America_the_Beautiful | 0.0 | 0.0 | 29339.0 | wikitext | NULL |
| 653.0 | Assistive_technology | 0.0 | 0.0 | 63308.0 | wikitext | NULL |
| 654.0 | Accessible_computing | 1.0 | 0.0 | 36.0 | wikitext | NULL |
| 655.0 | Abacus | 0.0 | 0.0 | 50940.0 | wikitext | NULL |
| 656.0 | Acid | 0.0 | 0.0 | 47523.0 | wikitext | NULL |
| 657.0 | Asphalt | 0.0 | 0.0 | 95749.0 | wikitext | NULL |
| 659.0 | American_National_Standards_Institute | 0.0 | 0.0 | 18089.0 | wikitext | NULL |
| 661.0 | Argument_(disambiguation) | 0.0 | 0.0 | 1710.0 | wikitext | NULL |
| 662.0 | Apollo_11 | 0.0 | 0.0 | 184198.0 | wikitext | NULL |
| 663.0 | Apollo_8 | 0.0 | 0.0 | 95526.0 | wikitext | NULL |
| 664.0 | Astronaut | 0.0 | 0.0 | 80670.0 | wikitext | NULL |
| 665.0 | A_Modest_Proposal | 0.0 | 0.0 | 24728.0 | wikitext | NULL |
| 666.0 | Alkali_metal | 0.0 | 0.0 | 217024.0 | wikitext | NULL |
| 668.0 | Argument_form | 1.0 | 0.0 | 26.0 | wikitext | NULL |
| 669.0 | Allotrope | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 670.0 | Alphabet | 0.0 | 0.0 | 48050.0 | wikitext | NULL |
| 673.0 | Atomic_number | 0.0 | 0.0 | 14031.0 | wikitext | NULL |
| 674.0 | Anatomy | 0.0 | 0.0 | 77631.0 | wikitext | NULL |
| 675.0 | Affirming_the_consequent | 0.0 | 0.0 | 6242.0 | wikitext | NULL |
| 676.0 | Andrei_Tarkovsky | 0.0 | 0.0 | 74800.0 | wikitext | NULL |
| 677.0 | Ambiguity | 0.0 | 0.0 | 31221.0 | wikitext | NULL |
| 678.0 | Abel | 0.0 | 0.0 | 10386.0 | wikitext | NULL |
| 679.0 | Animal_(disambiguation) | 0.0 | 0.0 | 8673.0 | wikitext | NULL |
| 680.0 | Aardvark | 0.0 | 0.0 | 36644.0 | wikitext | NULL |
| 681.0 | Aardwolf | 0.0 | 0.0 | 24478.0 | wikitext | NULL |
| 682.0 | Adobe | 0.0 | 0.0 | 28066.0 | wikitext | NULL |
| 683.0 | Adventure | 0.0 | 0.0 | 9292.0 | wikitext | NULL |
| 686.0 | Amaltheia | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 687.0 | Analysis_of_Variance | 1.0 | 0.0 | 66.0 | wikitext | NULL |
| 689.0 | Asia | 0.0 | 0.0 | 119487.0 | wikitext | NULL |
| 690.0 | Aruba | 0.0 | 0.0 | 77354.0 | wikitext | NULL |
| 691.0 | Articles_of_Confederation | 0.0 | 0.0 | 73929.0 | wikitext | NULL |
| 693.0 | Archaeology/Broch | 1.0 | 0.0 | 71.0 | wikitext | NULL |
| 694.0 | Asia_Minor_(disambiguation) | 0.0 | 0.0 | 520.0 | wikitext | NULL |
| 696.0 | Aa_River | 1.0 | 0.0 | 108.0 | wikitext | NULL |
| 698.0 | Atlantic_Ocean | 0.0 | 0.0 | 114989.0 | wikitext | NULL |
| 700.0 | Arthur_Schopenhauer | 0.0 | 0.0 | 165600.0 | wikitext | NULL |
| 701.0 | Angola | 0.0 | 0.0 | 156923.0 | wikitext | NULL |
| 704.0 | Demographics_of_Angola | 0.0 | 0.0 | 33803.0 | wikitext | NULL |
| 705.0 | Politics_of_Angola | 0.0 | 0.0 | 15087.0 | wikitext | NULL |
| 706.0 | Economy_of_Angola | 0.0 | 0.0 | 45452.0 | wikitext | NULL |
| 708.0 | Transport_in_Angola | 0.0 | 0.0 | 4083.0 | wikitext | NULL |
| 709.0 | Angolan_Armed_Forces | 0.0 | 0.0 | 25218.0 | wikitext | NULL |
| 710.0 | Foreign_relations_of_Angola | 0.0 | 0.0 | 28022.0 | wikitext | NULL |
| 711.0 | Albert_Sidney_Johnston | 0.0 | 0.0 | 53655.0 | wikitext | NULL |
| 713.0 | Android_(robot) | 0.0 | 0.0 | 30791.0 | wikitext | NULL |
| 717.0 | Alberta | 0.0 | 0.0 | 165138.0 | wikitext | NULL |
| 727.0 | Astronomy/History | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 728.0 | List_of_anthropologists | 0.0 | 0.0 | 8657.0 | wikitext | NULL |
| 731.0 | Astronomy_and_Astrophysics/History | 1.0 | 1.0 | 86.0 | wikitext | NULL |
| 734.0 | Actinopterygii | 0.0 | 0.0 | 41677.0 | wikitext | NULL |
| 735.0 | Al_Gore/Criticisms | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 736.0 | Albert_Einstein | 0.0 | 0.0 | 210170.0 | wikitext | NULL |
| 737.0 | Afghanistan | 0.0 | 0.0 | 310005.0 | wikitext | NULL |
| 738.0 | Albania | 0.0 | 0.0 | 277109.0 | wikitext | NULL |
| 740.0 | Allah | 0.0 | 0.0 | 49185.0 | wikitext | NULL |
| 742.0 | Algorithms_(journal) | 0.0 | 0.0 | 3748.0 | wikitext | NULL |
| 743.0 | Antigua_And_Barbuda | 1.0 | 0.0 | 79.0 | wikitext | NULL |
| 746.0 | Azerbaijan | 0.0 | 0.0 | 236389.0 | wikitext | NULL |
| 748.0 | Amateur_astronomy | 0.0 | 0.0 | 36867.0 | wikitext | NULL |
| 749.0 | Astronomers_and_Astrophysicists | 1.0 | 0.0 | 24.0 | wikitext | NULL |
| 751.0 | Aikido | 0.0 | 0.0 | 57246.0 | wikitext | NULL |
| 752.0 | Art | 0.0 | 0.0 | 121171.0 | wikitext | NULL |
| 755.0 | Albania/History | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 758.0 | Albania/Transnational_Issues | 1.0 | 0.0 | 134.0 | wikitext | NULL |
| 759.0 | Albania/People | 1.0 | 0.0 | 89.0 | wikitext | NULL |
| 763.0 | Albania/Foreign_relations | 1.0 | 0.0 | 134.0 | wikitext | NULL |
| 764.0 | Agnostida | 0.0 | 0.0 | 8134.0 | wikitext | NULL |
| 765.0 | Abortion | 0.0 | 0.0 | 194359.0 | wikitext | NULL |
| 766.0 | Abstract_(law) | 0.0 | 0.0 | 2292.0 | wikitext | NULL |
| 767.0 | A.E._van_Vogt | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 771.0 | American_Revolutionary_War | 0.0 | 0.0 | 308703.0 | wikitext | NULL |
| 772.0 | Ampere | 0.0 | 0.0 | 15813.0 | wikitext | NULL |
| 775.0 | Algorithm | 0.0 | 0.0 | 113862.0 | wikitext | NULL |
| 777.0 | Annual_plant | 0.0 | 0.0 | 6058.0 | wikitext | NULL |
| 779.0 | Anthophyta | 0.0 | 0.0 | 3135.0 | wikitext | NULL |
| 780.0 | Atlas_(disambiguation) | 0.0 | 0.0 | 11221.0 | wikitext | NULL |
| 782.0 | Mouthwash | 0.0 | 0.0 | 65648.0 | wikitext | NULL |
| 783.0 | Alexander_the_Great | 0.0 | 0.0 | 227366.0 | wikitext | NULL |
| 784.0 | Alfred_Korzybski | 0.0 | 0.0 | 14772.0 | wikitext | NULL |
| 785.0 | Asteroids_(video_game) | 0.0 | 0.0 | 48246.0 | wikitext | NULL |
| 786.0 | Asparagales | 0.0 | 0.0 | 89674.0 | wikitext | NULL |
| 787.0 | Alismatales | 0.0 | 0.0 | 13634.0 | wikitext | NULL |
| 788.0 | Apiales | 0.0 | 0.0 | 7627.0 | wikitext | NULL |
| 789.0 | Asterales | 0.0 | 0.0 | 11669.0 | wikitext | NULL |
| 791.0 | Asteroid | 0.0 | 0.0 | 155686.0 | wikitext | NULL |
| 794.0 | Allocution | 0.0 | 0.0 | 3884.0 | wikitext | NULL |
| 795.0 | Affidavit | 0.0 | 0.0 | 10052.0 | wikitext | NULL |
| 798.0 | Aries_(constellation) | 0.0 | 0.0 | 50994.0 | wikitext | NULL |
| 799.0 | Aquarius_(constellation) | 0.0 | 0.0 | 36019.0 | wikitext | NULL |
| 800.0 | Anime | 0.0 | 0.0 | 104726.0 | wikitext | NULL |
| 801.0 | Asterism | 0.0 | 0.0 | 357.0 | wikitext | NULL |
| 802.0 | Ankara | 0.0 | 0.0 | 125716.0 | wikitext | NULL |
| 803.0 | Arabic | 0.0 | 0.0 | 174116.0 | wikitext | NULL |
| 807.0 | AlbaniaCommunications | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 808.0 | Alfred_Hitchcock | 0.0 | 0.0 | 179231.0 | wikitext | NULL |
| 809.0 | Anaconda | 0.0 | 0.0 | 8537.0 | wikitext | NULL |
| 813.0 | Afghanistan/History | 1.0 | 0.0 | 88.0 | wikitext | NULL |
| 814.0 | Afghanistan/Geography | 1.0 | 0.0 | 90.0 | wikitext | NULL |
| 815.0 | Afghanistan/Government | 1.0 | 0.0 | 114.0 | wikitext | NULL |
| 816.0 | Afghanistan/People | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 817.0 | Afghanistan/Economy | 1.0 | 0.0 | 88.0 | wikitext | NULL |
| 818.0 | Afghanistan/Communications | 1.0 | 0.0 | 114.0 | wikitext | NULL |
| 820.0 | Afghanistan/Military | 1.0 | 0.0 | 155.0 | wikitext | NULL |
| 821.0 | Afghanistan/Transnational_Issues | 1.0 | 0.0 | 98.0 | wikitext | NULL |
| 822.0 | Afghanistan_(1911_Encyclopedia) | 1.0 | 0.0 | 25.0 | wikitext | NULL |
| 824.0 | Altaic_languages | 0.0 | 0.0 | 63972.0 | wikitext | NULL |
| 825.0 | Austrian_German | 0.0 | 0.0 | 21521.0 | wikitext | NULL |
| 832.0 | Austria/Transnational_issues | 1.0 | 0.0 | 94.0 | wikitext | NULL |
| 839.0 | Anglican_Church | 1.0 | 0.0 | 25.0 | wikitext | NULL |
| 840.0 | Axiom_of_choice | 0.0 | 0.0 | 58996.0 | wikitext | NULL |
| 841.0 | Attila | 0.0 | 0.0 | 65626.0 | wikitext | NULL |
| 842.0 | Aegean_Sea | 0.0 | 0.0 | 47828.0 | wikitext | NULL |
| 843.0 | A_Clockwork_Orange_(novel) | 0.0 | 0.0 | 55097.0 | wikitext | NULL |
| 844.0 | Amsterdam | 0.0 | 0.0 | 196002.0 | wikitext | NULL |
| 846.0 | Museum_of_Work | 0.0 | 0.0 | 7122.0 | wikitext | NULL |
| 848.0 | Audi | 0.0 | 0.0 | 147456.0 | wikitext | NULL |
| 849.0 | Aircraft | 0.0 | 0.0 | 63371.0 | wikitext | NULL |
| 851.0 | Alfred_Nobel | 0.0 | 0.0 | 33680.0 | wikitext | NULL |
| 852.0 | Alexander_Graham_Bell | 0.0 | 0.0 | 143315.0 | wikitext | NULL |
| 854.0 | Anatolia | 0.0 | 0.0 | 72850.0 | wikitext | NULL |
| 855.0 | Abiotic_factors | 1.0 | 0.0 | 31.0 | wikitext | NULL |
| 856.0 | Apple_Inc. | 0.0 | 0.0 | 296242.0 | wikitext | NULL |
| 857.0 | Aberdeenshire | 0.0 | 0.0 | 33434.0 | wikitext | NULL |
| 858.0 | AU | 1.0 | 0.0 | 127.0 | wikitext | NULL |
| 859.0 | Aztlan_Underground | 0.0 | 0.0 | 7876.0 | wikitext | NULL |
| 860.0 | Aland | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 863.0 | American_Civil_War | 0.0 | 0.0 | 252499.0 | wikitext | NULL |
| 864.0 | Andy_Warhol | 0.0 | 0.0 | 159393.0 | wikitext | NULL |
| 868.0 | Alp_Arslan | 0.0 | 0.0 | 27066.0 | wikitext | NULL |
| 869.0 | American_Film_Institute | 0.0 | 0.0 | 23405.0 | wikitext | NULL |
| 872.0 | Akira_Kurosawa | 0.0 | 0.0 | 108667.0 | wikitext | NULL |
| 873.0 | Ancient_civilization | 1.0 | 0.0 | 95.0 | wikitext | NULL |
| 874.0 | Ancient_Egypt | 0.0 | 0.0 | 141823.0 | wikitext | NULL |
| 875.0 | Analog_Brothers | 0.0 | 0.0 | 3787.0 | wikitext | NULL |
| 876.0 | Motor_neuron_disease | 0.0 | 0.0 | 22719.0 | wikitext | NULL |
| 877.0 | Abjad | 0.0 | 0.0 | 22953.0 | wikitext | NULL |
| 878.0 | Abugida | 0.0 | 0.0 | 44096.0 | wikitext | NULL |
| 880.0 | ABBA | 0.0 | 0.0 | 143023.0 | wikitext | NULL |
| 881.0 | Allegiance | 0.0 | 0.0 | 15801.0 | wikitext | NULL |
| 882.0 | Absolute_majority | 1.0 | 0.0 | 121.0 | wikitext | NULL |
| 885.0 | Altenberg | 0.0 | 0.0 | 1824.0 | wikitext | NULL |
| 887.0 | MessagePad | 0.0 | 0.0 | 47725.0 | wikitext | NULL |
| 888.0 | A._E._van_Vogt | 0.0 | 0.0 | 51988.0 | wikitext | NULL |
| 890.0 | Anna_Kournikova | 0.0 | 0.0 | 55901.0 | wikitext | NULL |
| 891.0 | Accountancy | 1.0 | 0.0 | 24.0 | wikitext | NULL |
| 892.0 | Alfons_Maria_Jakob | 0.0 | 0.0 | 5267.0 | wikitext | NULL |
| 894.0 | Agnosticism | 0.0 | 0.0 | 72756.0 | wikitext | NULL |
| 896.0 | Argon | 0.0 | 0.0 | 40086.0 | wikitext | NULL |
| 897.0 | Arsenic | 0.0 | 0.0 | 127483.0 | wikitext | NULL |
| 898.0 | Antimony | 0.0 | 0.0 | 60686.0 | wikitext | NULL |
| 899.0 | Actinium | 0.0 | 0.0 | 39951.0 | wikitext | NULL |
| 900.0 | Americium | 0.0 | 0.0 | 77374.0 | wikitext | NULL |
| 901.0 | Astatine | 0.0 | 0.0 | 81700.0 | wikitext | NULL |
| 902.0 | Atom | 0.0 | 0.0 | 125779.0 | wikitext | NULL |
| 903.0 | Arable_land | 0.0 | 0.0 | 17047.0 | wikitext | NULL |
| 904.0 | Aluminium | 0.0 | 0.0 | 138626.0 | wikitext | NULL |
| 905.0 | Advanced_Chemistry | 0.0 | 0.0 | 12704.0 | wikitext | NULL |
| 907.0 | Awk | 1.0 | 0.0 | 82.0 | wikitext | NULL |
| 908.0 | AgoraNomic | 1.0 | 0.0 | 19.0 | wikitext | NULL |
| 909.0 | Anglican_Communion | 0.0 | 0.0 | 67308.0 | wikitext | NULL |
| 910.0 | Arne_Kaijser | 0.0 | 0.0 | 2754.0 | wikitext | NULL |
| 911.0 | Archipelago | 0.0 | 0.0 | 7267.0 | wikitext | NULL |
| 914.0 | Author | 0.0 | 0.0 | 20404.0 | wikitext | NULL |
| 915.0 | Andrey_Markov | 0.0 | 0.0 | 10528.0 | wikitext | NULL |
| 918.0 | Anti-semitism | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 919.0 | Anti-semitic | 1.0 | 0.0 | 47.0 | wikitext | NULL |
| 921.0 | Angst | 0.0 | 0.0 | 7030.0 | wikitext | NULL |
| 922.0 | Anxiety | 0.0 | 0.0 | 92522.0 | wikitext | NULL |
| 923.0 | A.A._Milne | 1.0 | 0.0 | 25.0 | wikitext | NULL |
| 924.0 | A._A._Milne | 0.0 | 0.0 | 43901.0 | wikitext | NULL |
| 925.0 | Asociación_Alumni | 0.0 | 0.0 | 5890.0 | wikitext | NULL |
| 926.0 | Alumna | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 928.0 | Axiom | 0.0 | 0.0 | 35579.0 | wikitext | NULL |
| 929.0 | Alpha | 0.0 | 0.0 | 11696.0 | wikitext | NULL |
| 930.0 | Alvin_Toffler | 0.0 | 0.0 | 31422.0 | wikitext | NULL |
| 931.0 | The_Amazing_Spider-Man | 0.0 | 0.0 | 86345.0 | wikitext | NULL |
| 933.0 | AM | 0.0 | 0.0 | 4055.0 | wikitext | NULL |
| 935.0 | Automated_Alice/XII | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 936.0 | Automated_Alice/XI | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 937.0 | Automated_Alice/X | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 938.0 | Automated_Alice/IX | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 939.0 | Automated_Alice/VIII | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 940.0 | Automated_Alice/VI | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 941.0 | Automated_Alice/VII | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 942.0 | Automated_Alice/V | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 943.0 | Automated_Alice/IV | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 944.0 | Automated_Alice/II | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 945.0 | Automated_Alice/I | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 946.0 | Automated_Alice/III | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 951.0 | Antigua_and_Barbuda | 0.0 | 0.0 | 69608.0 | wikitext | NULL |
| 953.0 | Azincourt | 0.0 | 0.0 | 7304.0 | wikitext | NULL |
| 954.0 | Albert_Speer | 0.0 | 0.0 | 74955.0 | wikitext | NULL |
| 956.0 | Asteraceae | 0.0 | 0.0 | 52348.0 | wikitext | NULL |
| 957.0 | Apiaceae | 0.0 | 0.0 | 19443.0 | wikitext | NULL |
| 958.0 | Axon | 0.0 | 0.0 | 56358.0 | wikitext | NULL |
| 959.0 | Agma | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 960.0 | Aramaic_alphabet | 0.0 | 0.0 | 39545.0 | wikitext | NULL |
| 963.0 | Arguments_for_the_existence_of_God | 1.0 | 0.0 | 30.0 | wikitext | NULL |
| 966.0 | American_shot | 0.0 | 0.0 | 2475.0 | wikitext | NULL |
| 967.0 | Acute_disseminated_encephalomyelitis | 0.0 | 0.0 | 49156.0 | wikitext | NULL |
| 969.0 | Ataxia | 0.0 | 0.0 | 51374.0 | wikitext | NULL |
| 970.0 | AmbientCalculusOnline | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 972.0 | Abdul_Alhazred | 1.0 | 0.0 | 453.0 | wikitext | NULL |
| 973.0 | A_priori_and_a_posterior_knowledge | 1.0 | 0.0 | 39.0 | wikitext | NULL |
| 974.0 | Ada_Lovelace | 0.0 | 0.0 | 81872.0 | wikitext | NULL |
| 975.0 | AmbientCalculiOnline | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 980.0 | August_Derleth | 0.0 | 0.0 | 36081.0 | wikitext | NULL |
| 981.0 | Alps | 0.0 | 0.0 | 97011.0 | wikitext | NULL |
| 982.0 | A_priori_and_a_posteriori_knowledge | 1.0 | 0.0 | 39.0 | wikitext | NULL |
| 983.0 | Albert_Camus | 0.0 | 0.0 | 60082.0 | wikitext | NULL |
| 984.0 | Agatha_Christie | 0.0 | 0.0 | 157622.0 | wikitext | NULL |
| 986.0 | The_Plague_(novel) | 0.0 | 0.0 | 33756.0 | wikitext | NULL |
| 988.0 | Applied_ethics | 0.0 | 0.0 | 10125.0 | wikitext | NULL |
| 991.0 | Absolute_value | 0.0 | 0.0 | 25672.0 | wikitext | NULL |
| 993.0 | Analog_signal | 0.0 | 0.0 | 4898.0 | wikitext | NULL |
| 994.0 | Arecales | 0.0 | 0.0 | 3408.0 | wikitext | NULL |
| 1000.0 | Hercule_Poirot | 0.0 | 0.0 | 70455.0 | wikitext | NULL |
| 1002.0 | Miss_Marple | 0.0 | 0.0 | 31513.0 | wikitext | NULL |
| 1004.0 | April | 0.0 | 0.0 | 32330.0 | wikitext | NULL |
| 1005.0 | August | 0.0 | 0.0 | 29903.0 | wikitext | NULL |
| 1006.0 | Aaron | 0.0 | 0.0 | 45188.0 | wikitext | NULL |
| 1008.0 | April_6 | 0.0 | 0.0 | 53142.0 | wikitext | NULL |
| 1009.0 | April_12 | 0.0 | 0.0 | 52633.0 | wikitext | NULL |
| 1010.0 | April_15 | 0.0 | 0.0 | 50663.0 | wikitext | NULL |
| 1011.0 | April_30 | 0.0 | 0.0 | 48202.0 | wikitext | NULL |
| 1012.0 | August_22 | 0.0 | 0.0 | 44190.0 | wikitext | NULL |
| 1013.0 | August_27 | 0.0 | 0.0 | 47372.0 | wikitext | NULL |
| 1014.0 | Alcohol_(chemistry) | 0.0 | 0.0 | 34841.0 | wikitext | NULL |
| 1016.0 | Achill_Island | 0.0 | 0.0 | 39863.0 | wikitext | NULL |
| 1017.0 | Allen_Ginsberg | 0.0 | 0.0 | 108507.0 | wikitext | NULL |
| 1018.0 | Algebraically_closed_field | 0.0 | 0.0 | 12639.0 | wikitext | NULL |
| 1019.0 | August_6 | 0.0 | 0.0 | 44883.0 | wikitext | NULL |
| 1020.0 | Anatoly_Karpov | 0.0 | 0.0 | 44732.0 | wikitext | NULL |
| 1021.0 | Aspect_ratio | 0.0 | 0.0 | 5699.0 | wikitext | NULL |
| 1022.0 | Auto_racing | 0.0 | 0.0 | 49738.0 | wikitext | NULL |
| 1023.0 | Anarcho-capitalism | 0.0 | 0.0 | 135375.0 | wikitext | NULL |
| 1026.0 | Anarcho-capitalists | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1027.0 | August_9 | 0.0 | 0.0 | 48531.0 | wikitext | NULL |
| 1028.0 | Aristophanes | 0.0 | 0.0 | 68860.0 | wikitext | NULL |
| 1029.0 | Albert_Schweitzer | 0.0 | 0.0 | 80267.0 | wikitext | NULL |
| 1030.0 | Austrian_School | 0.0 | 0.0 | 71838.0 | wikitext | NULL |
| 1032.0 | Abscess | 0.0 | 0.0 | 32103.0 | wikitext | NULL |
| 1035.0 | Aal | 1.0 | 0.0 | 94.0 | wikitext | NULL |
| 1036.0 | Aalborg_Municipality | 0.0 | 0.0 | 13463.0 | wikitext | NULL |
| 1038.0 | Aarhus | 0.0 | 0.0 | 205789.0 | wikitext | NULL |
| 1043.0 | Northern_cavefish | 0.0 | 0.0 | 2625.0 | wikitext | NULL |
| 1046.0 | Abatement | 0.0 | 0.0 | 1133.0 | wikitext | NULL |
| 1049.0 | Amateur | 0.0 | 0.0 | 15459.0 | wikitext | NULL |
| 1051.0 | Alexis_Carrel | 0.0 | 0.0 | 38802.0 | wikitext | NULL |
| 1055.0 | All_Souls'_Day | 0.0 | 0.0 | 36190.0 | wikitext | NULL |
| 1057.0 | Anatole_France | 0.0 | 0.0 | 16387.0 | wikitext | NULL |
| 1058.0 | André_Gide | 0.0 | 0.0 | 32483.0 | wikitext | NULL |
| 1059.0 | Applied_statistics | 1.0 | 0.0 | 192.0 | wikitext | NULL |
| 1061.0 | Analysis_of_variance/Random_effects_models | 1.0 | 0.0 | 123.0 | wikitext | NULL |
| 1062.0 | Analysis_of_variance/Degrees_of_freedom | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 1063.0 | Algorithms_for_calculating_variance | 0.0 | 0.0 | 30844.0 | wikitext | NULL |
| 1064.0 | Almond | 0.0 | 0.0 | 65298.0 | wikitext | NULL |
| 1069.0 | Demographics_of_Antigua_and_Barbuda | 0.0 | 0.0 | 15988.0 | wikitext | NULL |
| 1070.0 | Politics_of_Antigua_and_Barbuda | 0.0 | 0.0 | 10381.0 | wikitext | NULL |
| 1072.0 | Telecommunications_in_Antigua_and_Barbuda | 0.0 | 0.0 | 5634.0 | wikitext | NULL |
| 1074.0 | Antigua_and_Barbuda_Defence_Force | 0.0 | 0.0 | 6978.0 | wikitext | NULL |
| 1075.0 | Antigua_and_Barbuda/Transnational_issues | 1.0 | 0.0 | 106.0 | wikitext | NULL |
| 1078.0 | Antisemitism | 0.0 | 0.0 | 146605.0 | wikitext | NULL |
| 1081.0 | Economy_of_Azerbaijan | 0.0 | 0.0 | 60281.0 | wikitext | NULL |
| 1082.0 | Geography_of_Azerbaijan | 0.0 | 0.0 | 14609.0 | wikitext | NULL |
| 1083.0 | Azerbaijan/People | 1.0 | 0.0 | 92.0 | wikitext | NULL |
| 1085.0 | Azerbaijan/Communications | 1.0 | 0.0 | 98.0 | wikitext | NULL |
| 1087.0 | Foreign_relations_of_Azerbaijan | 0.0 | 0.0 | 106467.0 | wikitext | NULL |
| 1088.0 | Azerbaijani_Armed_Forces | 0.0 | 0.0 | 86941.0 | wikitext | NULL |
| 1089.0 | Azerbaijan/Foreign_relations | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 1091.0 | Geography_of_Armenia | 0.0 | 0.0 | 9701.0 | wikitext | NULL |
| 1092.0 | Demographics_of_Armenia | 0.0 | 0.0 | 53608.0 | wikitext | NULL |
| 1093.0 | Politics_of_Armenia | 0.0 | 0.0 | 22632.0 | wikitext | NULL |
| 1094.0 | Economy_of_Armenia | 0.0 | 0.0 | 139777.0 | wikitext | NULL |
| 1096.0 | Transport_in_Armenia | 0.0 | 0.0 | 17734.0 | wikitext | NULL |
| 1097.0 | Armed_Forces_of_Armenia | 0.0 | 0.0 | 65462.0 | wikitext | NULL |
| 1098.0 | Foreign_relations_of_Armenia | 0.0 | 0.0 | 166725.0 | wikitext | NULL |
| 1105.0 | Argentina/Transnational_issues | 1.0 | 0.0 | 138.0 | wikitext | NULL |
| 1108.0 | Argentina/Foreign_relations | 1.0 | 0.0 | 138.0 | wikitext | NULL |
| 1109.0 | Geography_of_American_Samoa | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 1110.0 | Demographics_of_American_Samoa | 0.0 | 0.0 | 13354.0 | wikitext | NULL |
| 1111.0 | Politics_of_American_Samoa | 0.0 | 0.0 | 5605.0 | wikitext | NULL |
| 1112.0 | Economy_of_American_Samoa | 0.0 | 0.0 | 6915.0 | wikitext | NULL |
| 1114.0 | Transportation_in_American_Samoa | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 1116.0 | American_Samoa/Military | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 1123.0 | Australia/Transnational_issues | 1.0 | 0.0 | 96.0 | wikitext | NULL |
| 1129.0 | August_13 | 0.0 | 0.0 | 47062.0 | wikitext | NULL |
| 1130.0 | Avicenna | 0.0 | 0.0 | 114907.0 | wikitext | NULL |
| 1132.0 | The_Ashes | 0.0 | 0.0 | 92557.0 | wikitext | NULL |
| 1134.0 | Analysis | 0.0 | 0.0 | 21855.0 | wikitext | NULL |
| 1135.0 | Abner_Doubleday | 0.0 | 0.0 | 28685.0 | wikitext | NULL |
| 1136.0 | America's_National_Game | 0.0 | 0.0 | 1519.0 | wikitext | NULL |
| 1140.0 | Amplitude_modulation | 0.0 | 0.0 | 33937.0 | wikitext | NULL |
| 1141.0 | Augustin-Jean_Fresnel | 0.0 | 0.0 | 207403.0 | wikitext | NULL |
| 1143.0 | Abbot | 0.0 | 0.0 | 34498.0 | wikitext | NULL |
| 1144.0 | Ardipithecus | 0.0 | 0.0 | 31777.0 | wikitext | NULL |
| 1146.0 | Assembly_line | 0.0 | 0.0 | 34686.0 | wikitext | NULL |
| 1148.0 | Adelaide | 0.0 | 0.0 | 165131.0 | wikitext | NULL |
| 1151.0 | AK47 | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 1152.0 | Alan_Garner | 0.0 | 0.0 | 41348.0 | wikitext | NULL |
| 1153.0 | Amhrann_na_bhFiann | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1154.0 | August_2 | 0.0 | 0.0 | 49532.0 | wikitext | NULL |
| 1155.0 | Atlantic_(disambiguation) | 0.0 | 0.0 | 4980.0 | wikitext | NULL |
| 1158.0 | Algebraic_number | 0.0 | 0.0 | 12611.0 | wikitext | NULL |
| 1160.0 | Automorphism | 0.0 | 0.0 | 11771.0 | wikitext | NULL |
| 1162.0 | Accordion | 0.0 | 0.0 | 66013.0 | wikitext | NULL |
| 1164.0 | Artificial_intelligence | 0.0 | 0.0 | 220426.0 | wikitext | NULL |
| 1166.0 | Afro_Celt_Sound_System | 0.0 | 0.0 | 22290.0 | wikitext | NULL |
| 1167.0 | Ancient_philosophy | 0.0 | 0.0 | 29750.0 | wikitext | NULL |
| 1168.0 | Anaximander | 0.0 | 0.0 | 56067.0 | wikitext | NULL |
| 1169.0 | APL | 0.0 | 0.0 | 2536.0 | wikitext | NULL |
| 1170.0 | Architect | 0.0 | 0.0 | 27793.0 | wikitext | NULL |
| 1171.0 | Abbreviation | 0.0 | 0.0 | 32641.0 | wikitext | NULL |
| 1174.0 | Aphrodite | 0.0 | 0.0 | 141174.0 | wikitext | NULL |
| 1175.0 | April_1 | 0.0 | 0.0 | 49325.0 | wikitext | NULL |
| 1176.0 | Antisymmetric_relation | 0.0 | 0.0 | 4327.0 | wikitext | NULL |
| 1177.0 | Aleister_Crowley | 0.0 | 0.0 | 128082.0 | wikitext | NULL |
| 1178.0 | Afterlife | 0.0 | 0.0 | 114450.0 | wikitext | NULL |
| 1181.0 | Astrometry | 0.0 | 0.0 | 18156.0 | wikitext | NULL |
| 1182.0 | Athena | 0.0 | 0.0 | 117909.0 | wikitext | NULL |
| 1183.0 | Amber_Diceless_Roleplaying_Game | 0.0 | 0.0 | 22788.0 | wikitext | NULL |
| 1184.0 | Athene_(disambiguation) | 0.0 | 0.0 | 1038.0 | wikitext | NULL |
| 1186.0 | AphexTwin | 1.0 | 0.0 | 78.0 | wikitext | NULL |
| 1187.0 | Alloy | 0.0 | 0.0 | 39789.0 | wikitext | NULL |
| 1189.0 | Articles_of_Faith | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 1190.0 | Alternative_history | 1.0 | 0.0 | 31.0 | wikitext | NULL |
| 1192.0 | Artistic_revolution | 0.0 | 0.0 | 9302.0 | wikitext | NULL |
| 1193.0 | Agrarianism | 0.0 | 0.0 | 45044.0 | wikitext | NULL |
| 1194.0 | Atomic | 0.0 | 0.0 | 1655.0 | wikitext | NULL |
| 1195.0 | Allotropes | 1.0 | 0.0 | 42.0 | wikitext | NULL |
| 1196.0 | Angle | 0.0 | 0.0 | 50252.0 | wikitext | NULL |
| 1197.0 | Asa | 0.0 | 0.0 | 1718.0 | wikitext | NULL |
| 1198.0 | Acoustics | 0.0 | 0.0 | 38399.0 | wikitext | NULL |
| 1199.0 | Angle_tribe | 1.0 | 0.0 | 20.0 | wikitext | NULL |
| 1200.0 | Atomic_physics | 0.0 | 0.0 | 9168.0 | wikitext | NULL |
| 1201.0 | American_Sign_Language | 0.0 | 0.0 | 66042.0 | wikitext | NULL |
| 1202.0 | Applet | 0.0 | 0.0 | 8698.0 | wikitext | NULL |
| 1203.0 | Alternate_history | 0.0 | 0.0 | 72917.0 | wikitext | NULL |
| 1205.0 | Atomic_orbitals | 1.0 | 0.0 | 79.0 | wikitext | NULL |
| 1206.0 | Atomic_orbital | 0.0 | 0.0 | 83171.0 | wikitext | NULL |
| 1207.0 | Amino_acid | 0.0 | 0.0 | 105700.0 | wikitext | NULL |
| 1208.0 | Alan_Turing | 0.0 | 0.0 | 139444.0 | wikitext | NULL |
| 1209.0 | Area | 0.0 | 0.0 | 45136.0 | wikitext | NULL |
| 1210.0 | Astronomical_unit | 0.0 | 0.0 | 54620.0 | wikitext | NULL |
| 1212.0 | Artist | 0.0 | 0.0 | 7688.0 | wikitext | NULL |
| 1213.0 | Actaeon | 0.0 | 0.0 | 27501.0 | wikitext | NULL |
| 1214.0 | Anglicanism | 0.0 | 0.0 | 144236.0 | wikitext | NULL |
| 1216.0 | Athens | 0.0 | 0.0 | 181240.0 | wikitext | NULL |
| 1217.0 | Anguilla | 0.0 | 0.0 | 60587.0 | wikitext | NULL |
| 1220.0 | Anguilla/Transnational_issues | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 1221.0 | Anguilla/Military | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 1223.0 | Telecommunications_in_Anguilla | 0.0 | 0.0 | 4827.0 | wikitext | NULL |
| 1227.0 | Ashmore_and_Cartier_Islands | 0.0 | 0.0 | 17896.0 | wikitext | NULL |
| 1228.0 | Ashmore_and_Cartier_Islands/Geography | 1.0 | 0.0 | 118.0 | wikitext | NULL |
| 1229.0 | Ashmore_and_Cartier_Islands/People | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1230.0 | Ashmore_and_Cartier_Islands/Government | 1.0 | 0.0 | 119.0 | wikitext | NULL |
| 1231.0 | Ashmore_and_Cartier_Islands/Transportation | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1232.0 | Ashmore_and_Cartier_Islands/Economy | 1.0 | 0.0 | 130.0 | wikitext | NULL |
| 1233.0 | Ashmore_and_Cartier_Islands/Military | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1234.0 | Acoustic_theory | 0.0 | 0.0 | 11785.0 | wikitext | NULL |
| 1235.0 | Alexander_Mackenzie_(politician) | 0.0 | 0.0 | 31828.0 | wikitext | NULL |
| 1238.0 | Atomic_bomb | 1.0 | 0.0 | 103.0 | wikitext | NULL |
| 1239.0 | Ashoka | 0.0 | 0.0 | 145168.0 | wikitext | NULL |
| 1241.0 | American_(word) | 0.0 | 0.0 | 45428.0 | wikitext | NULL |
| 1242.0 | Ada_(programming_language) | 0.0 | 0.0 | 57549.0 | wikitext | NULL |
| 1245.0 | Alpha_ray | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 1246.0 | Alfonso_Aráu | 1.0 | 0.0 | 26.0 | wikitext | NULL |
| 1247.0 | Alfonso_Cuarón | 0.0 | 0.0 | 27492.0 | wikitext | NULL |
| 1252.0 | Arianism | 0.0 | 0.0 | 80978.0 | wikitext | NULL |
| 1254.0 | August_1 | 0.0 | 0.0 | 52683.0 | wikitext | NULL |
| 1255.0 | Astronomical_Units | 1.0 | 0.0 | 130.0 | wikitext | NULL |
| 1256.0 | Antoninus_Pius | 0.0 | 0.0 | 71848.0 | wikitext | NULL |
| 1259.0 | August_3 | 0.0 | 0.0 | 42085.0 | wikitext | NULL |
| 1260.0 | Advanced_Encryption_Standard | 0.0 | 0.0 | 48743.0 | wikitext | NULL |
| 1261.0 | April_26 | 0.0 | 0.0 | 46939.0 | wikitext | NULL |
| 1262.0 | Argot | 1.0 | 0.0 | 181.0 | wikitext | NULL |
| 1264.0 | Anisotropy | 0.0 | 0.0 | 20704.0 | wikitext | NULL |
| 1267.0 | Alpha_decay | 0.0 | 0.0 | 18823.0 | wikitext | NULL |
| 1268.0 | AI | 1.0 | 0.0 | 157.0 | wikitext | NULL |
| 1270.0 | Extreme_poverty | 0.0 | 0.0 | 59250.0 | wikitext | NULL |
| 1271.0 | Analytical_Engine | 0.0 | 0.0 | 39177.0 | wikitext | NULL |
| 1273.0 | Augustus | 0.0 | 0.0 | 144918.0 | wikitext | NULL |
| 1274.0 | Geography_of_Antarctica | 0.0 | 0.0 | 22878.0 | wikitext | NULL |
| 1276.0 | Economy_of_Antarctica | 1.0 | 0.0 | 243.0 | wikitext | NULL |
| 1277.0 | Government_of_Antarctica | 1.0 | 0.0 | 37.0 | wikitext | NULL |
| 1279.0 | Transport_in_Antarctica | 0.0 | 0.0 | 11873.0 | wikitext | NULL |
| 1280.0 | Military_of_Antarctica | 1.0 | 0.0 | 48.0 | wikitext | NULL |
| 1285.0 | Geography_of_Alabama | 0.0 | 0.0 | 15547.0 | wikitext | NULL |
| 1286.0 | List_of_governors_of_Alabama | 0.0 | 0.0 | 60829.0 | wikitext | NULL |
| 1288.0 | Apocrypha | 0.0 | 0.0 | 60465.0 | wikitext | NULL |
| 1290.0 | Antartic_Treaty | 1.0 | 0.0 | 129.0 | wikitext | NULL |
| 1291.0 | Antarctic_Treaty_System | 0.0 | 0.0 | 42723.0 | wikitext | NULL |
| 1292.0 | Algernon_Swinburne | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1293.0 | Alfred_Lawson | 0.0 | 0.0 | 16942.0 | wikitext | NULL |
| 1295.0 | ALCS | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 1297.0 | Apocrypha/Tanakh | 1.0 | 0.0 | 78.0 | wikitext | NULL |
| 1298.0 | Ames,_Iowa | 0.0 | 0.0 | 55406.0 | wikitext | NULL |
| 1299.0 | Abbadides | 1.0 | 0.0 | 29.0 | wikitext | NULL |
| 1300.0 | Abalone | 0.0 | 0.0 | 63093.0 | wikitext | NULL |
| 1301.0 | Abbess | 0.0 | 0.0 | 13449.0 | wikitext | NULL |
| 1302.0 | Human_abdomen | 1.0 | 0.0 | 90.0 | wikitext | NULL |
| 1303.0 | Abdominal_surgery | 0.0 | 0.0 | 7650.0 | wikitext | NULL |
| 1304.0 | Abduction | 0.0 | 0.0 | 2669.0 | wikitext | NULL |
| 1305.0 | Abensberg | 0.0 | 0.0 | 16290.0 | wikitext | NULL |
| 1306.0 | Arminianism | 0.0 | 0.0 | 82187.0 | wikitext | NULL |
| 1307.0 | The_Alan_Parsons_Project | 0.0 | 0.0 | 21560.0 | wikitext | NULL |
| 1309.0 | Almost_all | 0.0 | 0.0 | 25415.0 | wikitext | NULL |
| 1311.0 | Ada_Byron's_notes_on_the_analytical_engine | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 1312.0 | Augustine | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1313.0 | Aromatic_compound | 0.0 | 0.0 | 12131.0 | wikitext | NULL |
| 1315.0 | Abbey | 0.0 | 0.0 | 30916.0 | wikitext | NULL |
| 1316.0 | Annales_school | 0.0 | 0.0 | 37725.0 | wikitext | NULL |
| 1317.0 | Antimatter | 0.0 | 0.0 | 74559.0 | wikitext | NULL |
| 1321.0 | Antonio_Gaudi/Sagrada_Familia | 1.0 | 0.0 | 82.0 | wikitext | NULL |
| 1322.0 | Casa_Batlló | 0.0 | 0.0 | 23318.0 | wikitext | NULL |
| 1324.0 | Park_Güell | 0.0 | 0.0 | 15495.0 | wikitext | NULL |
| 1325.0 | Casa_Milà | 0.0 | 0.0 | 39346.0 | wikitext | NULL |
| 1327.0 | Antiparticle | 0.0 | 0.0 | 20321.0 | wikitext | NULL |
| 1328.0 | A.D. | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 1331.0 | Arabian_Prince | 0.0 | 0.0 | 12794.0 | wikitext | NULL |
| 1332.0 | August_7 | 0.0 | 0.0 | 55009.0 | wikitext | NULL |
| 1333.0 | August_8 | 0.0 | 0.0 | 49211.0 | wikitext | NULL |
| 1334.0 | April_16 | 0.0 | 0.0 | 54925.0 | wikitext | NULL |
| 1335.0 | Associative_property | 0.0 | 0.0 | 25928.0 | wikitext | NULL |
| 1336.0 | The_Apache_Software_Foundation | 0.0 | 0.0 | 11890.0 | wikitext | NULL |
| 1338.0 | Americans_with_Disabilities_Act_of_1990 | 0.0 | 0.0 | 89286.0 | wikitext | NULL |
| 1339.0 | Americans_with_Disabilities_Act_of_1990/Findings_and_Purposes | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 1340.0 | Americans_with_Disabilities_Act_of_1990/Definitions | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 1341.0 | Americans_with_Disabilities_Act_of_1990/Title_III | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 1342.0 | A.D | 1.0 | 0.0 | 82.0 | wikitext | NULL |
| 1344.0 | Apple_I | 0.0 | 0.0 | 44379.0 | wikitext | NULL |
| 1345.0 | Apache_webserver | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1346.0 | Apatosaurus | 0.0 | 0.0 | 90670.0 | wikitext | NULL |
| 1347.0 | Allosaurus | 0.0 | 0.0 | 121497.0 | wikitext | NULL |
| 1348.0 | AK-47 | 0.0 | 0.0 | 141766.0 | wikitext | NULL |
| 1349.0 | Atanasoff–Berry_computer | 0.0 | 0.0 | 23497.0 | wikitext | NULL |
| 1354.0 | Andes | 0.0 | 0.0 | 54780.0 | wikitext | NULL |
| 1355.0 | Anderida | 1.0 | 0.0 | 23.0 | wikitext | NULL |
| 1356.0 | Ancylopoda | 0.0 | 0.0 | 2358.0 | wikitext | NULL |
| 1358.0 | Anchor | 0.0 | 0.0 | 52859.0 | wikitext | NULL |
| 1359.0 | Anbar_(town) | 0.0 | 0.0 | 12631.0 | wikitext | NULL |
| 1360.0 | Anazarbus | 0.0 | 0.0 | 17119.0 | wikitext | NULL |
| 1361.0 | Anagram | 0.0 | 0.0 | 33706.0 | wikitext | NULL |
| 1362.0 | Anadyr_(river) | 0.0 | 0.0 | 7337.0 | wikitext | NULL |
| 1363.0 | André-Marie_Ampère | 0.0 | 0.0 | 20216.0 | wikitext | NULL |
| 1365.0 | Ammonia | 0.0 | 0.0 | 148235.0 | wikitext | NULL |
| 1366.0 | Amethyst | 0.0 | 0.0 | 27267.0 | wikitext | NULL |
| 1367.0 | Albertosaurus | 0.0 | 0.0 | 58698.0 | wikitext | NULL |
| 1368.0 | Assembly_language | 0.0 | 0.0 | 90003.0 | wikitext | NULL |
| 1369.0 | Ambrosia | 0.0 | 0.0 | 12915.0 | wikitext | NULL |
| 1370.0 | Ambrose | 0.0 | 0.0 | 103100.0 | wikitext | NULL |
| 1371.0 | Ambracia | 0.0 | 0.0 | 6319.0 | wikitext | NULL |
| 1372.0 | Amber | 0.0 | 0.0 | 59547.0 | wikitext | NULL |
| 1373.0 | Amalaric | 0.0 | 0.0 | 5878.0 | wikitext | NULL |
| 1374.0 | Alphorn | 0.0 | 0.0 | 12956.0 | wikitext | NULL |
| 1376.0 | Army | 0.0 | 0.0 | 30058.0 | wikitext | NULL |
| 1380.0 | Alligatoridae | 0.0 | 0.0 | 20628.0 | wikitext | NULL |
| 1383.0 | Alder | 0.0 | 0.0 | 23813.0 | wikitext | NULL |
| 1384.0 | Amos_Bronson_Alcott | 0.0 | 0.0 | 51959.0 | wikitext | NULL |
| 1386.0 | Arachnophobia | 0.0 | 0.0 | 16131.0 | wikitext | NULL |
| 1387.0 | Alabaster | 0.0 | 0.0 | 31341.0 | wikitext | NULL |
| 1389.0 | Ahab | 0.0 | 0.0 | 16568.0 | wikitext | NULL |
| 1391.0 | ASIC_(disambiguation) | 0.0 | 0.0 | 1189.0 | wikitext | NULL |
| 1392.0 | Dasyproctidae | 0.0 | 0.0 | 4787.0 | wikitext | NULL |
| 1394.0 | Algol | 0.0 | 0.0 | 32666.0 | wikitext | NULL |
| 1395.0 | Amazing_Grace | 0.0 | 0.0 | 64133.0 | wikitext | NULL |
| 1397.0 | AOL | 0.0 | 0.0 | 104064.0 | wikitext | NULL |
| 1399.0 | ADHD | 1.0 | 0.0 | 154.0 | wikitext | NULL |
| 1400.0 | Anno_Domini | 0.0 | 0.0 | 31355.0 | wikitext | NULL |
| 1404.0 | AV | 0.0 | 0.0 | 3210.0 | wikitext | NULL |
| 1406.0 | Amino_group | 1.0 | 0.0 | 19.0 | wikitext | NULL |
| 1407.0 | Antony_van_Leeuwenhook | 1.0 | 0.0 | 98.0 | wikitext | NULL |
| 1408.0 | Alcuin | 0.0 | 0.0 | 41674.0 | wikitext | NULL |
| 1409.0 | Angilbert | 0.0 | 0.0 | 7855.0 | wikitext | NULL |
| 1410.0 | Antony_van_Leeuwenhoek | 1.0 | 0.0 | 102.0 | wikitext | NULL |
| 1412.0 | Amine | 0.0 | 0.0 | 32725.0 | wikitext | NULL |
| 1415.0 | Adrian_I | 1.0 | 0.0 | 27.0 | wikitext | NULL |
| 1416.0 | April_29 | 0.0 | 0.0 | 52049.0 | wikitext | NULL |
| 1417.0 | August_14 | 0.0 | 0.0 | 94093.0 | wikitext | NULL |
| 1418.0 | Absolute_zero | 0.0 | 0.0 | 36868.0 | wikitext | NULL |
| 1419.0 | Adiabatic_process | 0.0 | 0.0 | 40636.0 | wikitext | NULL |
| 1422.0 | Amide | 0.0 | 0.0 | 21607.0 | wikitext | NULL |
| 1423.0 | Animism | 0.0 | 0.0 | 68318.0 | wikitext | NULL |
| 1425.0 | Antonio_Vivaldi | 0.0 | 0.0 | 42116.0 | wikitext | NULL |
| 1426.0 | Adrian_II | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 1428.0 | Adrian | 0.0 | 0.0 | 45416.0 | wikitext | NULL |
| 1429.0 | Adrian_IV | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 1433.0 | Aare | 0.0 | 0.0 | 13942.0 | wikitext | NULL |
| 1434.0 | Abgar | 1.0 | 0.0 | 21.0 | wikitext | NULL |
| 1435.0 | Abbotsford,_Scottish_Borders | 0.0 | 0.0 | 15773.0 | wikitext | NULL |
| 1436.0 | Abraham | 0.0 | 0.0 | 73358.0 | wikitext | NULL |
| 1437.0 | Abraxas | 0.0 | 0.0 | 46069.0 | wikitext | NULL |
| 1438.0 | Absalom | 0.0 | 0.0 | 32027.0 | wikitext | NULL |
| 1439.0 | Abydos | 0.0 | 0.0 | 534.0 | wikitext | NULL |
| 1440.0 | Abydos,_Egypt | 0.0 | 0.0 | 30139.0 | wikitext | NULL |
| 1441.0 | Abydos_(Hellespont) | 0.0 | 0.0 | 33933.0 | wikitext | NULL |
| 1442.0 | August_15 | 0.0 | 0.0 | 58362.0 | wikitext | NULL |
| 1445.0 | Acacia_sensu_lato | 0.0 | 0.0 | 37833.0 | wikitext | NULL |
| 1446.0 | Acapulco | 0.0 | 0.0 | 93594.0 | wikitext | NULL |
| 1448.0 | August_16 | 0.0 | 0.0 | 51549.0 | wikitext | NULL |
| 1449.0 | Alan_Kay | 0.0 | 0.0 | 23914.0 | wikitext | NULL |
| 1451.0 | APL_(programming_language) | 0.0 | 0.0 | 97258.0 | wikitext | NULL |
| 1453.0 | ALGOL | 0.0 | 0.0 | 37077.0 | wikitext | NULL |
| 1456.0 | AWK | 0.0 | 0.0 | 39479.0 | wikitext | NULL |
| 1457.0 | Alzheimers_disease | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 1459.0 | Ascorbic_Acid | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 1460.0 | Asgard | 0.0 | 0.0 | 16979.0 | wikitext | NULL |
| 1461.0 | Apollo_program | 0.0 | 0.0 | 151235.0 | wikitext | NULL |
| 1466.0 | Assault | 0.0 | 0.0 | 47559.0 | wikitext | NULL |
| 1476.0 | Australian_Prime_Ministers | 1.0 | 0.0 | 41.0 | wikitext | NULL |
| 1478.0 | Álfheimr | 0.0 | 0.0 | 2831.0 | wikitext | NULL |
| 1482.0 | Ask_and_Embla | 0.0 | 0.0 | 12669.0 | wikitext | NULL |
| 1484.0 | Alabama_River | 0.0 | 0.0 | 7724.0 | wikitext | NULL |
| 1485.0 | Alain_de_Lille | 0.0 | 0.0 | 15501.0 | wikitext | NULL |
| 1486.0 | Alemanni | 0.0 | 0.0 | 45699.0 | wikitext | NULL |
| 1488.0 | NYSE_American | 0.0 | 0.0 | 28351.0 | wikitext | NULL |
| 1490.0 | August_17 | 0.0 | 0.0 | 50297.0 | wikitext | NULL |
| 1491.0 | August_12 | 0.0 | 0.0 | 49007.0 | wikitext | NULL |
| 1494.0 | Alfred_Russel_Wallace | 0.0 | 0.0 | 116378.0 | wikitext | NULL |
| 1495.0 | Australian_Labor_Party | 0.0 | 0.0 | 97028.0 | wikitext | NULL |
| 1496.0 | August_18 | 0.0 | 0.0 | 46431.0 | wikitext | NULL |
| 1497.0 | August_19 | 0.0 | 0.0 | 52053.0 | wikitext | NULL |
| 1499.0 | August_21 | 0.0 | 0.0 | 42670.0 | wikitext | NULL |
| 1500.0 | Dodo_(Alice's_Adventures_in_Wonderland) | 0.0 | 0.0 | 7678.0 | wikitext | NULL |
| 1501.0 | Lory_(disambiguation) | 0.0 | 0.0 | 773.0 | wikitext | NULL |
| 1502.0 | Eaglet_(Alice's_Adventures_in_Wonderland) | 1.0 | 0.0 | 170.0 | wikitext | NULL |
| 1504.0 | Albert | 0.0 | 0.0 | 3010.0 | wikitext | NULL |
| 1505.0 | Albert_I | 0.0 | 0.0 | 1247.0 | wikitext | NULL |
| 1506.0 | Albert_II | 0.0 | 0.0 | 1483.0 | wikitext | NULL |
| 1507.0 | Albert_III | 0.0 | 0.0 | 653.0 | wikitext | NULL |
| 1508.0 | Albert_Alcibiades,_Margrave_of_Brandenburg-Kulmbach | 0.0 | 0.0 | 6485.0 | wikitext | NULL |
| 1509.0 | Albert_the_Bear | 0.0 | 0.0 | 10108.0 | wikitext | NULL |
| 1511.0 | Albert_I_of_Hapsburg | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1513.0 | Albert_of_Brandenburg | 0.0 | 0.0 | 11903.0 | wikitext | NULL |
| 1514.0 | Albert,_Duke_of_Prussia | 0.0 | 0.0 | 21034.0 | wikitext | NULL |
| 1515.0 | Albert_III,_Elector_of_Saxony | 1.0 | 0.0 | 40.0 | wikitext | NULL |
| 1516.0 | Albert_the_Degenerate | 1.0 | 0.0 | 44.0 | wikitext | NULL |
| 1517.0 | Albert_Of_Aix | 1.0 | 0.0 | 92.0 | wikitext | NULL |
| 1519.0 | August_25 | 0.0 | 0.0 | 50492.0 | wikitext | NULL |
| 1520.0 | Aachen | 0.0 | 0.0 | 98165.0 | wikitext | NULL |
| 1523.0 | Agate | 0.0 | 0.0 | 18500.0 | wikitext | NULL |
| 1525.0 | Aspirin | 0.0 | 0.0 | 148374.0 | wikitext | NULL |
| 1526.0 | Abner | 0.0 | 0.0 | 19935.0 | wikitext | NULL |
| 1527.0 | Ahmed_I | 0.0 | 0.0 | 30959.0 | wikitext | NULL |
| 1528.0 | Ahmed_II | 0.0 | 0.0 | 11022.0 | wikitext | NULL |
| 1529.0 | Ahmed_III | 0.0 | 0.0 | 36489.0 | wikitext | NULL |
| 1530.0 | Ainu_people | 0.0 | 0.0 | 160302.0 | wikitext | NULL |
| 1533.0 | Aix-la-Chapelle | 1.0 | 0.0 | 81.0 | wikitext | NULL |
| 1535.0 | Acorn_(fruit_of_the_oak_tree) | 1.0 | 0.0 | 19.0 | wikitext | NULL |
| 1536.0 | Acropolis | 0.0 | 0.0 | 14773.0 | wikitext | NULL |
| 1537.0 | Acupuncture | 0.0 | 0.0 | 198975.0 | wikitext | NULL |
| 1538.0 | Adder | 0.0 | 0.0 | 760.0 | wikitext | NULL |
| 1539.0 | Adirondacks | 1.0 | 0.0 | 95.0 | wikitext | NULL |
| 1540.0 | Aeneas | 0.0 | 0.0 | 34834.0 | wikitext | NULL |
| 1541.0 | April_13 | 0.0 | 0.0 | 43196.0 | wikitext | NULL |
| 1542.0 | Amaranth | 0.0 | 0.0 | 49948.0 | wikitext | NULL |
| 1543.0 | Agapanthus_africanus | 0.0 | 0.0 | 7739.0 | wikitext | NULL |
| 1544.0 | Agamemnon | 0.0 | 0.0 | 42460.0 | wikitext | NULL |
| 1545.0 | Aga_Khan_I | 0.0 | 0.0 | 15317.0 | wikitext | NULL |
| 1546.0 | Aga_Khan_III | 0.0 | 0.0 | 32478.0 | wikitext | NULL |
| 1547.0 | Agasias | 0.0 | 0.0 | 391.0 | wikitext | NULL |
| 1548.0 | Alexander_Agassiz | 0.0 | 0.0 | 17766.0 | wikitext | NULL |
| 1549.0 | Agathon | 0.0 | 0.0 | 8125.0 | wikitext | NULL |
| 1550.0 | Agesilaus_II | 0.0 | 0.0 | 42002.0 | wikitext | NULL |
| 1551.0 | Agis | 0.0 | 0.0 | 953.0 | wikitext | NULL |
| 1552.0 | Antonio_Agliardi | 0.0 | 0.0 | 6867.0 | wikitext | NULL |
| 1553.0 | Agnes_of_Merania | 0.0 | 0.0 | 3839.0 | wikitext | NULL |
| 1556.0 | Agrippina_the_Elder | 0.0 | 0.0 | 43683.0 | wikitext | NULL |
| 1557.0 | Agrippina_the_Younger | 0.0 | 0.0 | 44097.0 | wikitext | NULL |
| 1558.0 | American_Chinese_cuisine | 0.0 | 0.0 | 54573.0 | wikitext | NULL |
| 1559.0 | Ahenobarbus | 0.0 | 0.0 | 526.0 | wikitext | NULL |
| 1560.0 | Ahmad_Shah_Durrani | 0.0 | 0.0 | 51488.0 | wikitext | NULL |
| 1561.0 | Aidan_of_Dalriada | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 1563.0 | Arthur_Aikin | 0.0 | 0.0 | 5886.0 | wikitext | NULL |
| 1564.0 | Ailanthus | 0.0 | 0.0 | 4778.0 | wikitext | NULL |
| 1565.0 | Aimoin | 0.0 | 0.0 | 2661.0 | wikitext | NULL |
| 1566.0 | Akkadian_Empire | 0.0 | 0.0 | 83572.0 | wikitext | NULL |
| 1567.0 | Ajax_the_Lesser | 0.0 | 0.0 | 15739.0 | wikitext | NULL |
| 1568.0 | Ajax_the_Great | 0.0 | 0.0 | 18066.0 | wikitext | NULL |
| 1569.0 | Ajax | 0.0 | 0.0 | 5793.0 | wikitext | NULL |
| 1570.0 | Alaric_I | 0.0 | 0.0 | 47986.0 | wikitext | NULL |
| 1571.0 | Alaric_II | 0.0 | 0.0 | 9417.0 | wikitext | NULL |
| 1572.0 | Albategnius | 1.0 | 0.0 | 24.0 | wikitext | NULL |
| 1573.0 | Albertus_Magnus | 0.0 | 0.0 | 44055.0 | wikitext | NULL |
| 1575.0 | Alboin | 0.0 | 0.0 | 53199.0 | wikitext | NULL |
| 1576.0 | Afonso_de_Albuquerque | 0.0 | 0.0 | 62412.0 | wikitext | NULL |
| 1577.0 | Alcaeus_of_Mytilene | 0.0 | 0.0 | 29351.0 | wikitext | NULL |
| 1578.0 | Alcamenes | 0.0 | 0.0 | 3848.0 | wikitext | NULL |
| 1579.0 | Alcmene | 0.0 | 0.0 | 13642.0 | wikitext | NULL |
| 1580.0 | Alcidamas | 0.0 | 0.0 | 5568.0 | wikitext | NULL |
| 1581.0 | Aldine_Press | 0.0 | 0.0 | 22393.0 | wikitext | NULL |
| 1583.0 | Ealdred_(archbishop_of_York) | 0.0 | 0.0 | 42133.0 | wikitext | NULL |
| 1585.0 | Alexander_I_of_Epirus | 0.0 | 0.0 | 5238.0 | wikitext | NULL |
| 1586.0 | Alexander_Balas | 0.0 | 0.0 | 21296.0 | wikitext | NULL |
| 1587.0 | Alexander_of_Pherae | 0.0 | 0.0 | 10046.0 | wikitext | NULL |
| 1588.0 | Alexander_II_of_Epirus | 0.0 | 0.0 | 5666.0 | wikitext | NULL |
| 1589.0 | Alexander_Jagiellon | 0.0 | 0.0 | 9403.0 | wikitext | NULL |
| 1592.0 | Alexander_III_of_Russia | 0.0 | 0.0 | 67769.0 | wikitext | NULL |
| 1593.0 | Alexander_I_of_Scotland | 0.0 | 0.0 | 10986.0 | wikitext | NULL |
| 1594.0 | Alexander_II_of_Scotland | 0.0 | 0.0 | 12643.0 | wikitext | NULL |
| 1595.0 | Alexander_I_of_Serbia | 0.0 | 0.0 | 15334.0 | wikitext | NULL |
| 1596.0 | Alexander_III_of_Scotland | 0.0 | 0.0 | 19966.0 | wikitext | NULL |
| 1597.0 | Alexander_of_Greece_(disambiguation) | 0.0 | 0.0 | 444.0 | wikitext | NULL |
| 1599.0 | Alexander_of_Aphrodisias | 0.0 | 0.0 | 23192.0 | wikitext | NULL |
| 1600.0 | Severus_Alexander | 0.0 | 0.0 | 38183.0 | wikitext | NULL |
| 1601.0 | Alexander | 0.0 | 0.0 | 29504.0 | wikitext | NULL |
| 1602.0 | Alexander_I | 0.0 | 0.0 | 1105.0 | wikitext | NULL |
| 1603.0 | Alexander_II | 0.0 | 0.0 | 901.0 | wikitext | NULL |
| 1604.0 | Alexander_III | 0.0 | 0.0 | 948.0 | wikitext | NULL |
| 1605.0 | Alexander_Aetolus | 0.0 | 0.0 | 4109.0 | wikitext | NULL |
| 1606.0 | Alexander_Jannaeus | 0.0 | 0.0 | 19806.0 | wikitext | NULL |
| 1607.0 | Alexander_IV | 0.0 | 0.0 | 367.0 | wikitext | NULL |
| 1608.0 | Alexander_V | 0.0 | 0.0 | 223.0 | wikitext | NULL |
| 1609.0 | Alexander_VI | 1.0 | 0.0 | 31.0 | wikitext | NULL |
| 1610.0 | Alexander_VII | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1611.0 | Alexander_VIII | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1612.0 | Alexandrists | 0.0 | 0.0 | 1609.0 | wikitext | NULL |
| 1613.0 | Alexios_I_Komnenos | 0.0 | 0.0 | 38469.0 | wikitext | NULL |
| 1614.0 | Alexis_(poet) | 0.0 | 0.0 | 10392.0 | wikitext | NULL |
| 1615.0 | Alexios_II_Komnenos | 0.0 | 0.0 | 9228.0 | wikitext | NULL |
| 1616.0 | Alexios_III_Angelos | 0.0 | 0.0 | 13836.0 | wikitext | NULL |
| 1617.0 | Alexios_V_Doukas | 0.0 | 0.0 | 17897.0 | wikitext | NULL |
| 1620.0 | Alexei_Petrovich,_Tsarevich_of_Russia | 0.0 | 0.0 | 15686.0 | wikitext | NULL |
| 1623.0 | Andrew_Jackson | 0.0 | 0.0 | 179696.0 | wikitext | NULL |
| 1624.0 | Andrew_Johnson | 0.0 | 0.0 | 124793.0 | wikitext | NULL |
| 1625.0 | Aleksandr_Solzhenitsyn | 0.0 | 0.0 | 118674.0 | wikitext | NULL |
| 1626.0 | Aleksandr_Isaevich_Solzhenitsyn | 1.0 | 0.0 | 36.0 | wikitext | NULL |
| 1627.0 | Aberdeen | 0.0 | 0.0 | 147083.0 | wikitext | NULL |
| 1628.0 | August_23 | 0.0 | 0.0 | 49176.0 | wikitext | NULL |
| 1629.0 | August_24 | 0.0 | 0.0 | 54501.0 | wikitext | NULL |
| 1633.0 | Antipope | 0.0 | 0.0 | 32370.0 | wikitext | NULL |
| 1634.0 | Aquaculture | 0.0 | 0.0 | 125421.0 | wikitext | NULL |
| 1635.0 | Kolmogorov_complexity | 0.0 | 0.0 | 41353.0 | wikitext | NULL |
| 1636.0 | Antoine_de_Saint-Exupery | 1.0 | 0.0 | 125.0 | wikitext | NULL |
| 1637.0 | Hymn_to_Proserpine | 0.0 | 0.0 | 2710.0 | wikitext | NULL |
| 1638.0 | The_Triumph_of_Time | 0.0 | 0.0 | 1751.0 | wikitext | NULL |
| 1639.0 | April_28 | 0.0 | 0.0 | 42485.0 | wikitext | NULL |
| 1640.0 | Alfred_the_Great | 0.0 | 0.0 | 121065.0 | wikitext | NULL |
| 1641.0 | Alfred_Ernest_Albert | 1.0 | 0.0 | 51.0 | wikitext | NULL |
| 1642.0 | Alessandro_Algardi | 0.0 | 0.0 | 14639.0 | wikitext | NULL |
| 1643.0 | Alger_of_Liège | 0.0 | 0.0 | 3139.0 | wikitext | NULL |
| 1644.0 | Algiers | 0.0 | 0.0 | 70559.0 | wikitext | NULL |
| 1645.0 | Ibn_al-Haytham | 0.0 | 0.0 | 120924.0 | wikitext | NULL |
| 1647.0 | Alessandro_Allori | 0.0 | 0.0 | 9650.0 | wikitext | NULL |
| 1649.0 | Almoravid_dynasty | 0.0 | 0.0 | 83925.0 | wikitext | NULL |
| 1650.0 | Aloe | 0.0 | 0.0 | 21387.0 | wikitext | NULL |
| 1651.0 | Alured_of_Berkeley | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1652.0 | Alyattes | 0.0 | 0.0 | 40930.0 | wikitext | NULL |
| 1653.0 | Age_of_consent | 0.0 | 0.0 | 56722.0 | wikitext | NULL |
| 1654.0 | Alypius_of_Antioch | 0.0 | 0.0 | 1756.0 | wikitext | NULL |
| 1655.0 | Amalasuintha | 0.0 | 0.0 | 11115.0 | wikitext | NULL |
| 1656.0 | Amalric_of_Bena | 0.0 | 0.0 | 6659.0 | wikitext | NULL |
| 1657.0 | Afonso_I_of_Portugal | 0.0 | 0.0 | 32525.0 | wikitext | NULL |
| 1658.0 | Afonso_II_of_Portugal | 0.0 | 0.0 | 9807.0 | wikitext | NULL |
| 1659.0 | Afonso_III_of_Portugal | 0.0 | 0.0 | 12744.0 | wikitext | NULL |
| 1660.0 | Afonso_IV_of_Portugal | 0.0 | 0.0 | 14233.0 | wikitext | NULL |
| 1661.0 | Afonso_V_of_Portugal | 0.0 | 0.0 | 19540.0 | wikitext | NULL |
| 1662.0 | Afonso_VI_of_Portugal | 0.0 | 0.0 | 8372.0 | wikitext | NULL |
| 1663.0 | Alphonso_I_of_Spain | 0.0 | 0.0 | 539.0 | wikitext | NULL |
| 1664.0 | Alfonso_II_of_Asturias | 0.0 | 0.0 | 5949.0 | wikitext | NULL |
| 1669.0 | Amarasimha | 0.0 | 0.0 | 3546.0 | wikitext | NULL |
| 1672.0 | Alphonso_VIII_of_Spain | 1.0 | 0.0 | 37.0 | wikitext | NULL |
| 1673.0 | Alfonso_IX_of_Spain | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1676.0 | Alfonso_XII | 0.0 | 0.0 | 27559.0 | wikitext | NULL |
| 1677.0 | Alfonso_XIII | 0.0 | 0.0 | 67834.0 | wikitext | NULL |
| 1678.0 | Alphonsus_a_Sancta_Maria | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 1679.0 | Alfonso_the_Battler | 0.0 | 0.0 | 27719.0 | wikitext | NULL |
| 1680.0 | Amaryllis | 0.0 | 0.0 | 17681.0 | wikitext | NULL |
| 1682.0 | Amasis_I | 1.0 | 0.0 | 22.0 | wikitext | NULL |
| 1683.0 | Alfonso_III_of_Aragon | 0.0 | 0.0 | 5951.0 | wikitext | NULL |
| 1684.0 | Alfonso_IV_of_Aragon | 0.0 | 0.0 | 9985.0 | wikitext | NULL |
| 1685.0 | Amasis_II | 0.0 | 0.0 | 17642.0 | wikitext | NULL |
| 1686.0 | Alfonso_V_of_Aragon | 0.0 | 0.0 | 22331.0 | wikitext | NULL |
| 1687.0 | Amathus | 0.0 | 0.0 | 17228.0 | wikitext | NULL |
| 1688.0 | Alphons | 0.0 | 0.0 | 11520.0 | wikitext | NULL |
| 1689.0 | Alfonso_I | 0.0 | 0.0 | 620.0 | wikitext | NULL |
| 1690.0 | Amati | 0.0 | 0.0 | 9132.0 | wikitext | NULL |
| 1691.0 | Alfonso_II | 0.0 | 0.0 | 504.0 | wikitext | NULL |
| 1692.0 | Alfonso_III | 0.0 | 0.0 | 320.0 | wikitext | NULL |
| 1694.0 | Alfonso_IV | 0.0 | 0.0 | 232.0 | wikitext | NULL |
| 1695.0 | Amazons | 0.0 | 0.0 | 72183.0 | wikitext | NULL |
| 1696.0 | Alfonso_V | 0.0 | 0.0 | 200.0 | wikitext | NULL |
| 1697.0 | Ambergris | 0.0 | 0.0 | 20295.0 | wikitext | NULL |
| 1698.0 | Ambiorix | 0.0 | 0.0 | 11792.0 | wikitext | NULL |
| 1699.0 | Alfonso_VI | 1.0 | 0.0 | 128.0 | wikitext | NULL |
| 1700.0 | August_Wilhelm_Ambros | 0.0 | 0.0 | 3510.0 | wikitext | NULL |
| 1701.0 | Amazon_River | 0.0 | 0.0 | 101421.0 | wikitext | NULL |
| 1702.0 | Alfred_of_Beverley | 0.0 | 0.0 | 3400.0 | wikitext | NULL |
| 1703.0 | Alphonso_VII | 1.0 | 0.0 | 46.0 | wikitext | NULL |
| 1704.0 | Alphonso_VIII | 1.0 | 0.0 | 37.0 | wikitext | NULL |
| 1705.0 | Alphonso_IX | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1706.0 | Alphonso_X | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 1707.0 | Alphonso_XI | 1.0 | 0.0 | 35.0 | wikitext | NULL |
| 1708.0 | Alphonso_XII | 1.0 | 0.0 | 25.0 | wikitext | NULL |
| 1709.0 | Alphonso_XIII | 1.0 | 0.0 | 26.0 | wikitext | NULL |
| 1710.0 | April_22 | 0.0 | 0.0 | 35182.0 | wikitext | NULL |
| 1711.0 | August_31 | 0.0 | 0.0 | 45180.0 | wikitext | NULL |
| 1714.0 | Autpert_Ambrose | 0.0 | 0.0 | 1669.0 | wikitext | NULL |
| 1715.0 | Abu_Bakr | 0.0 | 0.0 | 70130.0 | wikitext | NULL |
| 1716.0 | Ambrose_Traversari | 0.0 | 0.0 | 8920.0 | wikitext | NULL |
| 1717.0 | Ambrosians | 0.0 | 0.0 | 7217.0 | wikitext | NULL |
| 1718.0 | Ambrosiaster | 0.0 | 0.0 | 12639.0 | wikitext | NULL |
| 1719.0 | Ambrosius_Aurelianus | 0.0 | 0.0 | 47081.0 | wikitext | NULL |
| 1722.0 | Ammon | 0.0 | 0.0 | 28089.0 | wikitext | NULL |
| 1723.0 | Ammonius_Hermiae | 0.0 | 0.0 | 10918.0 | wikitext | NULL |
| 1724.0 | Ammonius_Saccas | 0.0 | 0.0 | 19454.0 | wikitext | NULL |
| 1726.0 | Book_of_Amos | 0.0 | 0.0 | 14545.0 | wikitext | NULL |
| 1727.0 | Amphipolis | 0.0 | 0.0 | 25676.0 | wikitext | NULL |
| 1728.0 | Amram | 0.0 | 0.0 | 10144.0 | wikitext | NULL |
| 1729.0 | Amyntas_I_of_Macedon | 0.0 | 0.0 | 5010.0 | wikitext | NULL |
| 1730.0 | Amyntas_III_of_Macedon | 0.0 | 0.0 | 8817.0 | wikitext | NULL |
| 1732.0 | Anacharsis | 0.0 | 0.0 | 10183.0 | wikitext | NULL |
| 1733.0 | Anacreon_(poet) | 1.0 | 0.0 | 22.0 | wikitext | NULL |
| 1734.0 | Anah | 0.0 | 0.0 | 16082.0 | wikitext | NULL |
| 1735.0 | Ānanda | 0.0 | 0.0 | 126619.0 | wikitext | NULL |
| 1737.0 | Anaxagoras | 0.0 | 0.0 | 25323.0 | wikitext | NULL |
| 1738.0 | Anaxarchus | 0.0 | 0.0 | 4932.0 | wikitext | NULL |
| 1740.0 | Ancyra_(planthopper) | 0.0 | 0.0 | 3357.0 | wikitext | NULL |
| 1742.0 | Anastasius_I | 0.0 | 0.0 | 271.0 | wikitext | NULL |
| 1743.0 | Anastasius_II | 0.0 | 0.0 | 271.0 | wikitext | NULL |
| 1744.0 | Anastasius_III | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1745.0 | Anastasius_IV | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1746.0 | Anaximenes_of_Lampsacus | 0.0 | 0.0 | 9465.0 | wikitext | NULL |
| 1747.0 | Anastasius | 0.0 | 0.0 | 4795.0 | wikitext | NULL |
| 1748.0 | Anaximenes_of_Miletus | 0.0 | 0.0 | 24822.0 | wikitext | NULL |
| 1749.0 | Ancus_Marcius | 0.0 | 0.0 | 12201.0 | wikitext | NULL |
| 1750.0 | Andaman_Islands | 0.0 | 0.0 | 51900.0 | wikitext | NULL |
| 1751.0 | Alexander_Anderson_(mathematician) | 0.0 | 0.0 | 6103.0 | wikitext | NULL |
| 1752.0 | Andocides | 0.0 | 0.0 | 12142.0 | wikitext | NULL |
| 1754.0 | Andrea_Andreani | 0.0 | 0.0 | 7733.0 | wikitext | NULL |
| 1755.0 | Andrew_II_of_Hungary | 0.0 | 0.0 | 60429.0 | wikitext | NULL |
| 1756.0 | An_Enquiry_Concerning_Human_Understanding | 0.0 | 0.0 | 24073.0 | wikitext | NULL |
| 1758.0 | André_de_Longjumeau | 0.0 | 0.0 | 8241.0 | wikitext | NULL |
| 1759.0 | Andriscus | 0.0 | 0.0 | 25446.0 | wikitext | NULL |
| 1760.0 | Andronikos_III_Palaiologos | 0.0 | 0.0 | 15960.0 | wikitext | NULL |
| 1761.0 | Andronikos_II_Palaiologos | 0.0 | 0.0 | 21319.0 | wikitext | NULL |
| 1762.0 | Andronikos_I_Komnenos | 0.0 | 0.0 | 26966.0 | wikitext | NULL |
| 1763.0 | Andronicus_of_Cyrrhus | 0.0 | 0.0 | 2105.0 | wikitext | NULL |
| 1764.0 | Andronicus_of_Rhodes | 0.0 | 0.0 | 3687.0 | wikitext | NULL |
| 1765.0 | Andronicus | 0.0 | 0.0 | 2282.0 | wikitext | NULL |
| 1766.0 | Asteroid_Belt | 1.0 | 0.0 | 92.0 | wikitext | NULL |
| 1767.0 | Ammianus_Marcellinus | 0.0 | 0.0 | 22026.0 | wikitext | NULL |
| 1768.0 | ALICE | 1.0 | 0.0 | 171.0 | wikitext | NULL |
| 1769.0 | An_Enquiry_Concerning_Human_Understanding/Text | 1.0 | 0.0 | 55.0 | wikitext | NULL |
| 1770.0 | Apollo_13 | 0.0 | 0.0 | 116154.0 | wikitext | NULL |
| 1771.0 | Apollo_Program | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1772.0 | Arthritus | 1.0 | 0.0 | 23.0 | wikitext | NULL |
| 1773.0 | Apollo_7 | 0.0 | 0.0 | 59737.0 | wikitext | NULL |
| 1774.0 | Apollo_9 | 0.0 | 0.0 | 59547.0 | wikitext | NULL |
| 1775.0 | Applied_discrete_math | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 1776.0 | Arthritis | 0.0 | 0.0 | 60256.0 | wikitext | NULL |
| 1777.0 | April_2 | 0.0 | 0.0 | 50691.0 | wikitext | NULL |
| 1778.0 | Acetylene | 0.0 | 0.0 | 43280.0 | wikitext | NULL |
| 1779.0 | Alfred | 0.0 | 0.0 | 1890.0 | wikitext | NULL |
| 1781.0 | August_28 | 0.0 | 0.0 | 46125.0 | wikitext | NULL |
| 1786.0 | Arabic_numerals | 0.0 | 0.0 | 31303.0 | wikitext | NULL |
| 1787.0 | April_9 | 0.0 | 0.0 | 55129.0 | wikitext | NULL |
| 1788.0 | ABM | 0.0 | 0.0 | 1563.0 | wikitext | NULL |
| 1789.0 | Apuleius | 0.0 | 0.0 | 21943.0 | wikitext | NULL |
| 1790.0 | Alexander_Selkirk | 0.0 | 0.0 | 30796.0 | wikitext | NULL |
| 1791.0 | Anti-ballistic_missile | 0.0 | 0.0 | 88548.0 | wikitext | NULL |
| 1793.0 | August_29 | 0.0 | 0.0 | 47528.0 | wikitext | NULL |
| 1794.0 | August_30 | 0.0 | 0.0 | 44669.0 | wikitext | NULL |
| 1797.0 | Acre | 0.0 | 0.0 | 35055.0 | wikitext | NULL |
| 1799.0 | ATP | 0.0 | 0.0 | 2186.0 | wikitext | NULL |
| 1800.0 | Adenosine_triphosphate | 0.0 | 0.0 | 44099.0 | wikitext | NULL |
| 1802.0 | Ægir | 0.0 | 0.0 | 19706.0 | wikitext | NULL |
| 1805.0 | Antibiotic | 0.0 | 0.0 | 142427.0 | wikitext | NULL |
| 1806.0 | Arnold_Schwarzenegger | 0.0 | 0.0 | 225011.0 | wikitext | NULL |
| 1807.0 | ASA | 0.0 | 0.0 | 4995.0 | wikitext | NULL |
| 1809.0 | Aquinas | 1.0 | 0.0 | 99.0 | wikitext | NULL |
| 1810.0 | Actium | 0.0 | 0.0 | 3562.0 | wikitext | NULL |
| 1811.0 | Amide_hydrolysis | 1.0 | 0.0 | 68.0 | wikitext | NULL |
| 1812.0 | Amway | 0.0 | 0.0 | 106066.0 | wikitext | NULL |
| 1814.0 | Adam_Smith | 0.0 | 0.0 | 107560.0 | wikitext | NULL |
| 1821.0 | Antoine_Laurent_Lavoisier | 1.0 | 0.0 | 85.0 | wikitext | NULL |
| 1822.0 | Antoine_Lavoisier | 0.0 | 0.0 | 75434.0 | wikitext | NULL |
| 1824.0 | A_roll | 1.0 | 0.0 | 21.0 | wikitext | NULL |
| 1825.0 | Hermann_Kolbe | 0.0 | 0.0 | 16697.0 | wikitext | NULL |
| 1826.0 | April_18 | 0.0 | 0.0 | 33597.0 | wikitext | NULL |
| 1827.0 | April_23 | 0.0 | 0.0 | 46616.0 | wikitext | NULL |
| 1828.0 | Amitabh_Bachchan | 0.0 | 0.0 | 127861.0 | wikitext | NULL |
| 1830.0 | Air_Pollution | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 1831.0 | Antarctic-Environmental_Protocol | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 1832.0 | Allomorph | 0.0 | 0.0 | 8722.0 | wikitext | NULL |
| 1833.0 | American_bias | 1.0 | 0.0 | 27.0 | wikitext | NULL |
| 1834.0 | Allophone | 0.0 | 0.0 | 24419.0 | wikitext | NULL |
| 1835.0 | Affix | 0.0 | 0.0 | 11897.0 | wikitext | NULL |
| 1837.0 | Allegory | 0.0 | 0.0 | 28072.0 | wikitext | NULL |
| 1838.0 | Amazon_river | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 1839.0 | Allotropy | 0.0 | 0.0 | 23378.0 | wikitext | NULL |
| 1840.0 | Agathocles_of_Syracuse | 0.0 | 0.0 | 14651.0 | wikitext | NULL |
| 1841.0 | Economy_of_Alberta | 0.0 | 0.0 | 96497.0 | wikitext | NULL |
| 1842.0 | Augustin-Louis_Cauchy | 0.0 | 0.0 | 42923.0 | wikitext | NULL |
| 1844.0 | Archimedes | 0.0 | 0.0 | 99429.0 | wikitext | NULL |
| 1845.0 | Alternative_medicine | 0.0 | 0.0 | 202195.0 | wikitext | NULL |
| 1847.0 | Archimedean_solid | 0.0 | 0.0 | 26171.0 | wikitext | NULL |
| 1851.0 | Antiprism | 0.0 | 0.0 | 18676.0 | wikitext | NULL |
| 1852.0 | Ancient_Greeks | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 1853.0 | Natural_history_of_Africa | 0.0 | 0.0 | 7885.0 | wikitext | NULL |
| 1854.0 | Geography_of_Africa | 0.0 | 0.0 | 37335.0 | wikitext | NULL |
| 1855.0 | Africa/History | 1.0 | 0.0 | 31.0 | wikitext | NULL |
| 1857.0 | Approval_voting | 0.0 | 0.0 | 67712.0 | wikitext | NULL |
| 1858.0 | Aromatic_hydrocarbon | 1.0 | 0.0 | 183.0 | wikitext | NULL |
| 1859.0 | Arizona_State_University | 0.0 | 0.0 | 190519.0 | wikitext | NULL |
| 1862.0 | April_14 | 0.0 | 0.0 | 60593.0 | wikitext | NULL |
| 1864.0 | Astoria,_Oregon | 0.0 | 0.0 | 71881.0 | wikitext | NULL |
| 1866.0 | Alarums_and_Excursions | 0.0 | 0.0 | 8592.0 | wikitext | NULL |
| 1869.0 | Alfred_Jarry | 0.0 | 0.0 | 18108.0 | wikitext | NULL |
| 1870.0 | Amalric | 0.0 | 0.0 | 3036.0 | wikitext | NULL |
| 1871.0 | Amalric_of_Jerusalem | 0.0 | 0.0 | 18148.0 | wikitext | NULL |
| 1872.0 | Aimery_of_Cyprus | 0.0 | 0.0 | 30136.0 | wikitext | NULL |
| 1873.0 | Anthemius_of_Tralles | 0.0 | 0.0 | 5750.0 | wikitext | NULL |
| 1874.0 | Absalon | 0.0 | 0.0 | 16050.0 | wikitext | NULL |
| 1875.0 | Adhemar_of_Le_Puy | 0.0 | 0.0 | 10074.0 | wikitext | NULL |
| 1876.0 | Adhemar_de_Chabannes | 1.0 | 0.0 | 103.0 | wikitext | NULL |
| 1877.0 | Albigenses | 1.0 | 0.0 | 23.0 | wikitext | NULL |
| 1878.0 | Alphonse,_Count_of_Poitiers | 0.0 | 0.0 | 9075.0 | wikitext | NULL |
| 1879.0 | Alfonso_Jordan | 0.0 | 0.0 | 9688.0 | wikitext | NULL |
| 1880.0 | Ambroise | 0.0 | 0.0 | 3356.0 | wikitext | NULL |
| 1881.0 | Art_Deco | 0.0 | 0.0 | 148950.0 | wikitext | NULL |
| 1884.0 | ASCII_art | 0.0 | 0.0 | 53155.0 | wikitext | NULL |
| 1885.0 | Autoerotic_asphyxiation | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1887.0 | Alexius | 0.0 | 0.0 | 2739.0 | wikitext | NULL |
| 1889.0 | Ban_on_assault_rifles | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 1890.0 | American_English | 0.0 | 0.0 | 78621.0 | wikitext | NULL |
| 1893.0 | Albert_Spalding | 0.0 | 0.0 | 22801.0 | wikitext | NULL |
| 1894.0 | Africa_Alphabet | 0.0 | 0.0 | 3512.0 | wikitext | NULL |
| 1896.0 | Acquire | 0.0 | 0.0 | 8701.0 | wikitext | NULL |
| 1897.0 | Australian_English | 0.0 | 0.0 | 70859.0 | wikitext | NULL |
| 1902.0 | American_Airlines_Flight_77 | 0.0 | 0.0 | 85249.0 | wikitext | NULL |
| 1903.0 | American_Airlines_flight_77 | 1.0 | 0.0 | 106.0 | wikitext | NULL |
| 1904.0 | American_Airlines_flight_11 | 1.0 | 0.0 | 106.0 | wikitext | NULL |
| 1905.0 | Ambush | 0.0 | 0.0 | 16289.0 | wikitext | NULL |
| 1906.0 | Astronomical_aberration | 1.0 | 0.0 | 36.0 | wikitext | NULL |
| 1908.0 | Abzyme | 0.0 | 0.0 | 6959.0 | wikitext | NULL |
| 1909.0 | Adaptive_radiation | 0.0 | 0.0 | 37579.0 | wikitext | NULL |
| 1910.0 | Agarose_gel_electrophoresis | 0.0 | 0.0 | 34925.0 | wikitext | NULL |
| 1911.0 | Allele | 0.0 | 0.0 | 16991.0 | wikitext | NULL |
| 1912.0 | Ampicillin | 0.0 | 0.0 | 35148.0 | wikitext | NULL |
| 1913.0 | Annealing | 0.0 | 0.0 | 460.0 | wikitext | NULL |
| 1914.0 | Antimicrobial_resistance | 0.0 | 0.0 | 150266.0 | wikitext | NULL |
| 1915.0 | Antigen | 0.0 | 0.0 | 19203.0 | wikitext | NULL |
| 1916.0 | Autosome | 0.0 | 0.0 | 11003.0 | wikitext | NULL |
| 1919.0 | Antwerp_(disambiguation) | 0.0 | 0.0 | 651.0 | wikitext | NULL |
| 1920.0 | Aquila | 0.0 | 0.0 | 3896.0 | wikitext | NULL |
| 1921.0 | Al-Qaeda | 0.0 | 0.0 | 284997.0 | wikitext | NULL |
| 1923.0 | Alessandro_Volta | 0.0 | 0.0 | 26430.0 | wikitext | NULL |
| 1924.0 | Argo_Navis | 0.0 | 0.0 | 13465.0 | wikitext | NULL |
| 1925.0 | Andromeda_(mythology) | 0.0 | 0.0 | 43392.0 | wikitext | NULL |
| 1926.0 | Antlia | 0.0 | 0.0 | 32732.0 | wikitext | NULL |
| 1927.0 | Ara_(constellation) | 0.0 | 0.0 | 29562.0 | wikitext | NULL |
| 1928.0 | Auriga | 0.0 | 0.0 | 754.0 | wikitext | NULL |
| 1930.0 | Arkansas | 0.0 | 0.0 | 153605.0 | wikitext | NULL |
| 1931.0 | Atmosphere_(disambiguation) | 0.0 | 0.0 | 2260.0 | wikitext | NULL |
| 1933.0 | Apus | 0.0 | 0.0 | 28135.0 | wikitext | NULL |
| 1934.0 | Abadan,_Iran | 0.0 | 0.0 | 36915.0 | wikitext | NULL |
| 1935.0 | Attorney | 0.0 | 0.0 | 508.0 | wikitext | NULL |
| 1936.0 | Astronomical_Unit | 1.0 | 0.0 | 96.0 | wikitext | NULL |
| 1937.0 | Alexander_Fleming | 0.0 | 0.0 | 69600.0 | wikitext | NULL |
| 1938.0 | Andrew_Carnegie | 0.0 | 0.0 | 113066.0 | wikitext | NULL |
| 1939.0 | Approximant | 0.0 | 0.0 | 27181.0 | wikitext | NULL |
| 1940.0 | Astronomer_Royal | 0.0 | 0.0 | 7100.0 | wikitext | NULL |
| 1941.0 | Aeon | 0.0 | 0.0 | 7544.0 | wikitext | NULL |
| 1942.0 | Airline | 0.0 | 0.0 | 102615.0 | wikitext | NULL |
| 1943.0 | Australian_Democrats | 0.0 | 0.0 | 58049.0 | wikitext | NULL |
| 1944.0 | Australian_Capital_Territory | 0.0 | 0.0 | 106817.0 | wikitext | NULL |
| 1946.0 | Unit_of_alcohol | 0.0 | 0.0 | 20027.0 | wikitext | NULL |
| 1947.0 | Aotus | 0.0 | 0.0 | 506.0 | wikitext | NULL |
SELECT * FROM enwiki_redirect
| rd_from | rd_title |
|---|---|
| 10.0 | Computer_accessibility |
| 13.0 | History_of_Afghanistan |
| 14.0 | Geography_of_Afghanistan |
| 15.0 | Demographics_of_Afghanistan |
| 18.0 | Communications_in_Afghanistan |
| 19.0 | Transport_in_Afghanistan |
| 20.0 | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan |
| 21.0 | Foreign_relations_of_Afghanistan |
| 23.0 | Assistive_technology |
| 24.0 | Amoeba |
| 25.0 | Autism_spectrum |
| 27.0 | History_of_Albania |
| 29.0 | Demographics_of_Albania |
| 30.0 | As_We_May_Think |
| 35.0 | Politics_of_Albania |
| 36.0 | Economy_of_Albania |
| 40.0 | Afroasiatic_languages |
| 42.0 | Constructed_language |
| 46.0 | Abacus |
| 47.0 | Abalone |
| 48.0 | Abbadid_dynasty |
| 49.0 | Abbess |
| 50.0 | Abbeville |
| 51.0 | Abbey |
| 52.0 | Abbot |
| 53.0 | Abbreviation |
| 54.0 | Atlas_Shrugged |
| 56.0 | Constructed_language |
| 58.0 | List_of_Atlas_Shrugged_characters |
| 59.0 | Atlas_Shrugged |
| 60.0 | Atlas_Shrugged |
| 241.0 | African_Americans |
| 242.0 | Adolf_Hitler |
| 247.0 | Abecedarian |
| 248.0 | Cain_and_Abel |
| 249.0 | Abensberg |
| 251.0 | Aberdeen,_South_Dakota |
| 254.0 | Arthur_Koestler |
| 255.0 | Ayn_Rand |
| 256.0 | Alexander_the_Great |
| 258.0 | Anchorage,_Alaska |
| 259.0 | Logical_form |
| 260.0 | Existence_of_God |
| 263.0 | Anarchy |
| 264.0 | ASCII_art |
| 269.0 | Academy_Awards |
| 270.0 | Academy_Award_for_Best_Picture |
| 271.0 | Austrian_German |
| 272.0 | Elitism |
| 274.0 | Axiom_of_choice |
| 276.0 | American_football |
| 278.0 | United_States |
| 279.0 | Anna_Kournikova |
| 280.0 | Andorra |
| 287.0 | Austroasiatic_languages |
| 289.0 | Lists_of_actors |
| 291.0 | Anarcho-capitalism |
| 293.0 | Anarcho-capitalism |
| 296.0 | Lists_of_actors |
| 299.0 | An_American_in_Paris |
| 301.0 | Automorphism |
| 302.0 | Action_film |
| 304.0 | Africa |
| 306.0 | Statistics |
| 325.0 | Action_film |
| 338.0 | Auto_racing |
| 347.0 | Demographics_of_Algeria |
| 353.0 | Foreign_relations_of_Algeria |
| 369.0 | Atlas_Shrugged |
| 583.0 | Amoeba |
| 589.0 | Ashmore_and_Cartier_Islands |
| 596.0 | Artificial_language |
| 598.0 | Afroasiatic_languages |
| 609.0 | Foreign_relations_of_Andorra |
| 617.0 | Al_Gore |
| 618.0 | An_Enquiry_Concerning_Human_Understanding |
| 622.0 | Al_Gore |
| 626.0 | Auteur |
| 629.0 | Abstract_algebra |
| 635.0 | Analysis_of_variance |
| 644.0 | Arithmetic_logic_unit |
| 648.0 | Actor |
| 654.0 | Computer_accessibility |
| 668.0 | Logical_form |
| 669.0 | Allotropy |
| 686.0 | Amalthea_(mythology) |
| 687.0 | Analysis_of_variance |
| 693.0 | Broch |
| 696.0 | AA |
| 727.0 | History_of_astronomy |
| 731.0 | History_of_astronomy |
| 735.0 | Al_Gore |
| 743.0 | Antigua_and_Barbuda |
| 749.0 | Astronomer |
| 755.0 | History_of_Albania |
| 758.0 | Foreign_relations_of_Albania |
| 759.0 | Demographics_of_Albania |
| 763.0 | Foreign_relations_of_Albania |
| 767.0 | A._E._van_Vogt |
| 807.0 | Telecommunications_in_Albania |
| 813.0 | History_of_Afghanistan |
| 814.0 | Geography_of_Afghanistan |
| 815.0 | Government_of_the_Islamic_Emirate_of_Afghanistan |
| 816.0 | Demographics_of_Afghanistan |
| 817.0 | Economy_of_Afghanistan |
| 818.0 | Communications_in_Afghanistan |
| 820.0 | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan |
| 821.0 | Foreign_relations_of_Afghanistan |
| 822.0 | Afghanistan |
| 832.0 | Foreign_relations_of_Austria |
| 839.0 | Anglicanism |
| 855.0 | Abiotic_component |
| 858.0 | Au |
| 860.0 | Åland |
| 873.0 | Civilization |
| 882.0 | Supermajority |
| 891.0 | Accounting |
| 907.0 | AWK |
| 908.0 | Nomic |
| 918.0 | Antisemitism |
| 919.0 | Antisemitism |
| 923.0 | A._A._Milne |
| 926.0 | Alumni |
| 935.0 | Automated_Alice |
| 936.0 | Automated_Alice |
| 937.0 | Automated_Alice |
| 938.0 | Automated_Alice |
| 939.0 | Automated_Alice |
| 940.0 | Automated_Alice |
| 941.0 | Automated_Alice |
| 942.0 | Automated_Alice |
| 943.0 | Automated_Alice |
| 944.0 | Automated_Alice |
| 945.0 | Automated_Alice |
| 946.0 | Automated_Alice |
| 959.0 | Voiced_velar_nasal |
| 963.0 | Existence_of_God |
| 970.0 | Ambient_calculus |
| 972.0 | Necronomicon |
| 973.0 | A_priori_and_a_posteriori |
| 975.0 | Ambient_calculus |
| 982.0 | A_priori_and_a_posteriori |
| 1026.0 | Anarcho-capitalism |
| 1035.0 | AAL |
| 1059.0 | Statistics |
| 1061.0 | Analysis_of_variance |
| 1062.0 | Analysis_of_variance |
| 1075.0 | Foreign_relations_of_Antigua_and_Barbuda |
| 1083.0 | Demographics_of_Azerbaijan |
| 1085.0 | Telecommunications_in_Azerbaijan |
| 1089.0 | Foreign_relations_of_Azerbaijan |
| 1105.0 | Foreign_relations_of_Argentina |
| 1108.0 | Foreign_relations_of_Argentina |
| 1109.0 | American_Samoa |
| 1114.0 | American_Samoa |
| 1116.0 | American_Samoa |
| 1123.0 | Foreign_relations_of_Australia |
| 1151.0 | AK-47 |
| 1153.0 | Amhrán_na_bhFiann |
| 1186.0 | Aphex_Twin |
| 1189.0 | Creed |
| 1190.0 | Alternate_history |
| 1195.0 | Allotropy |
| 1199.0 | Angles |
| 1205.0 | Atomic_orbital |
| 1220.0 | Anguilla |
| 1221.0 | Anguilla |
| 1228.0 | Ashmore_and_Cartier_Islands |
| 1229.0 | Ashmore_and_Cartier_Islands |
| 1230.0 | Ashmore_and_Cartier_Islands |
| 1231.0 | Ashmore_and_Cartier_Islands |
| 1232.0 | Ashmore_and_Cartier_Islands |
| 1233.0 | Ashmore_and_Cartier_Islands |
| 1238.0 | Nuclear_weapon |
| 1245.0 | Alpha_particle |
| 1246.0 | Alfonso_Arau |
| 1255.0 | Astronomical_unit |
| 1262.0 | Cant_(language) |
| 1268.0 | Artificial_intelligence |
| 1276.0 | Antarctica |
| 1277.0 | Antarctic_Treaty_System |
| 1280.0 | Military_activity_in_the_Antarctic |
| 1290.0 | Antarctic_Treaty_System |
| 1292.0 | Algernon_Charles_Swinburne |
| 1295.0 | American_League_Championship_Series |
| 1297.0 | Hebrew_Bible |
| 1299.0 | Abbadid_dynasty |
| 1302.0 | Abdomen |
| 1311.0 | Ada_Lovelace |
| 1312.0 | Augustine_of_Hippo |
| 1321.0 | Sagrada_Família |
| 1328.0 | Anno_Domini |
| 1339.0 | Americans_with_Disabilities_Act_of_1990 |
| 1340.0 | Americans_with_Disabilities_Act_of_1990 |
| 1341.0 | Americans_with_Disabilities_Act_of_1990 |
| 1342.0 | Anno_Domini |
| 1345.0 | Apache_HTTP_Server |
| 1355.0 | Anderitum |
| 1399.0 | Attention_deficit_hyperactivity_disorder |
| 1406.0 | Amine |
| 1407.0 | Antonie_van_Leeuwenhoek |
| 1410.0 | Antonie_van_Leeuwenhoek |
| 1415.0 | Pope_Adrian_I |
| 1426.0 | Pope_Adrian_II |
| 1429.0 | Pope_Adrian_IV |
| 1434.0 | Abgar_V |
| 1457.0 | Alzheimer's_disease |
| 1459.0 | Vitamin_C |
| 1476.0 | Prime_Minister_of_Australia |
| 1502.0 | List_of_minor_characters_in_the_Alice_series |
| 1511.0 | Albert_I_of_Germany |
| 1515.0 | Albert_III,_Duke_of_Saxony |
| 1516.0 | Albert_II,_Margrave_of_Meissen |
| 1517.0 | Albert_of_Aix |
| 1533.0 | Aachen |
| 1535.0 | Acorn |
| 1539.0 | Adirondack_Mountains |
| 1561.0 | Áedán_mac_Gabráin |
| 1572.0 | Al-Battani |
| 1609.0 | Pope_Alexander_VI |
| 1610.0 | Pope_Alexander_VII |
| 1611.0 | Pope_Alexander_VIII |
| 1626.0 | Aleksandr_Solzhenitsyn |
| 1636.0 | Antoine_de_Saint-Exupéry |
| 1641.0 | Alfred,_Duke_of_Saxe-Coburg_and_Gotha |
| 1651.0 | Alfred_of_Beverley |
| 1672.0 | Alfonso_VIII_of_Castile |
| 1673.0 | Alfonso_IX_of_León |
| 1678.0 | Alfonso_de_Cartagena |
| 1682.0 | Ahmose_I |
| 1699.0 | Alfonso_VI_of_León_and_Castile |
| 1703.0 | Alfonso_VII_of_León_and_Castile |
| 1704.0 | Alfonso_VIII_of_Castile |
| 1705.0 | Alfonso_IX_of_León |
| 1706.0 | Alfonso_X_of_Castile |
| 1707.0 | Alfonso_XI_of_Castile |
| 1708.0 | Alfonso_XII |
| 1709.0 | Alfonso_XIII |
| 1733.0 | Anacreon |
| 1744.0 | Pope_Anastasius_III |
| 1745.0 | Pope_Anastasius_IV |
| 1766.0 | Asteroid_belt |
| 1768.0 | Alice |
| 1769.0 | An_Enquiry_Concerning_Human_Understanding |
| 1771.0 | Apollo_program |
| 1772.0 | Arthritis |
| 1775.0 | Discrete_mathematics |
| 1809.0 | Thomas_Aquinas |
| 1811.0 | Hydrolysis |
| 1821.0 | Antoine_Lavoisier |
| 1824.0 | Footage |
| 1830.0 | Air_pollution |
| 1831.0 | Protocol_on_Environmental_Protection_to_the_Antarctic_Treaty |
| 1833.0 | Americentrism |
| 1838.0 | Amazon_River |
| 1852.0 | Ancient_Greece |
| 1855.0 | History_of_Africa |
| 1858.0 | Aromatic_compound |
| 1876.0 | Adémar_de_Chabannes |
| 1877.0 | Catharism |
| 1885.0 | Erotic_asphyxiation |
| 1889.0 | Assault_weapons_ban |
| 1903.0 | American_Airlines_Flight_77 |
| 1904.0 | American_Airlines_Flight_11 |
| 1906.0 | Aberration_(astronomy) |
| 1936.0 | Astronomical_unit |
| 1952.0 | Industry_Standard_Architecture |
| 1959.0 | Telephone_exchange |
| 1972.0 | Aviation |
| 1976.0 | Adomnán |
| 1978.0 | Assassin_(disambiguation) |
| 1982.0 | Alice |
| 1984.0 | Arab_world |
| 1993.0 | Alan_Ayckbourn |
| 2001.0 | Al-Qaeda |
| 2002.0 | Argumentum_ad_populum |
| 2005.0 | Addiction |
| 2008.0 | Al-Qaeda |
| 2043.0 | Anti-Americanism |
| 2050.0 | Archaeology |
| 2051.0 | Anarchism |
| 2058.0 | Atheism |
| 2071.0 | Afro_Celt_Sound_System |
| 2073.0 | Andrew_Jackson |
| 2074.0 | Andrew_Jackson |
| 2079.0 | Autumnal_equinox |
| 2090.0 | Albert_of_Hohenzollern |
| 2095.0 | Parapsychology |
| 2128.0 | Los_Angeles_Angels |
| 2132.0 | Ara_Pacis |
| 2145.0 | Catharism |
| 2146.0 | Aleksandr_Solzhenitsyn |
| 2149.0 | Armour |
| 2153.0 | Elitism |
| 2164.0 | Peremptory_plea |
| 2165.0 | Peremptory_plea |
| 2188.0 | Accident_(philosophy) |
| 2190.0 | Alternate_history |
| 2203.0 | Religion_in_Poland |
| 2206.0 | Ampere |
| 2211.0 | Folklore_of_the_United_States |
| 2213.0 | Modus_ponens |
| 2220.0 | Acts_of_the_Apostles |
| 2223.0 | Slaughterhouse |
| 2227.0 | Argumentum_a_fortiori |
| 2228.0 | Ad_hominem |
| 2249.0 | Amplification |
| 2258.0 | Anglicanism |
| 2260.0 | Analog_Science_Fiction_and_Fact |
| 2261.0 | Analog_Science_Fiction_and_Fact |
| 2262.0 | Analog_Science_Fiction_and_Fact |
| 2264.0 | Heptarchy |
| 2269.0 | Asynchronous_Transfer_Mode |
| 2271.0 | Asymmetric_digital_subscriber_line |
| 2280.0 | Giant_panda |
| 2281.0 | Arctic_fox |
| 2285.0 | Tank_destroyer |
| 2290.0 | Indigenous_peoples |
| 2295.0 | Arhat |
| 2297.0 | Springbok |
| 2298.0 | Blue_crane |
| 2302.0 | Aramaic |
| 2306.0 | AT&T |
| 2320.0 | Audio_codec |
| 2324.0 | All_Saints'_Day |
| 2351.0 | HIV/AIDS |
| 2354.0 | Outline_of_archaeology |
| 2367.0 | HIV/AIDS |
| 2379.0 | Binary_relation |
| 2404.0 | Aon_(company) |
| 2419.0 | Alloy |
| 2432.0 | Albrecht_III_Achilles,_Elector_of_Brandenburg |
| 2446.0 | Appalachian_dulcimer |
| 2462.0 | Anti-globalization_movement |
| 2464.0 | Anti-globalization_movement |
| 2468.0 | Aaron's_rod |
| 2469.0 | AB |
| 2478.0 | Barada |
| 2479.0 | Manama |
| 2486.0 | Chrysoberyl |
| 2489.0 | Abandon |
| 2492.0 | Anal_sex |
| 2495.0 | Aurochs |
| 2496.0 | Etiology |
| 2520.0 | Addition |
| 2523.0 | Alien |
| 2525.0 | Al_Jazeera |
| 2527.0 | Ruhollah_Khomeini |
| 2533.0 | Alphorn |
| 2535.0 | AW |
| 2537.0 | Analog_Science_Fiction_and_Fact |
| 2549.0 | Analog_Science_Fiction_and_Fact |
| 2561.0 | List_of_federal_political_scandals_in_the_United_States |
| 2565.0 | Albert,_Duke_of_Prussia |
| 2567.0 | Academy_Awards |
| 2568.0 | Apsis |
| 2569.0 | Apsis |
| 2571.0 | Rope_(film) |
| 2572.0 | Arianism |
| 2595.0 | Atlas_(computer) |
| 2599.0 | AA |
| 2600.0 | Aaron's_rod |
| 2601.0 | Abandon |
| 2603.0 | Abaris_the_Hyperborean |
| 2612.0 | Abbo_of_Fleury |
| 2615.0 | Charles_Farrar_Browne |
| 2631.0 | Ælfric |
| 2636.0 | Accounting |
| 2638.0 | ACID |
| 2643.0 | Ajax_the_Lesser |
| 2644.0 | Ajax_the_Great |
| 2647.0 | American_Indians |
| 2648.0 | Abandon |
| 2649.0 | Abandonment_(legal) |
| 2650.0 | Abandonment_(legal) |
| 2651.0 | Abandonment_(legal) |
| 2652.0 | Nuisance_abatement |
| 2653.0 | Abatement |
| 2655.0 | Abatement |
| 2656.0 | Abatement |
| 2657.0 | Abatement |
| 2658.0 | Abatement_(heraldry) |
| 2659.0 | American_Revolutionary_War |
| 2664.0 | Affirmation_(law) |
| 2675.0 | Abd_al-Rahman |
| 2682.0 | Abdul_Qadir |
| 2683.0 | Abdelaziz_of_Morocco |
| 2688.0 | Pneumatic_motor |
| 2697.0 | Abraham_ibn_Ezra |
| 2711.0 | Aberdeenshire_(historic) |
| 2713.0 | Aberdyfi |
| 2725.0 | Aesthetics |
| 2746.0 | Same-sex_relationship |
| 2751.0 | The_Angry_Brigade |
| 2760.0 | Arab_(disambiguation) |
| 2765.0 | Anatomical_Therapeutic_Chemical_Classification_System |
| 2768.0 | Antiarrhythmic_agent |
| 2771.0 | Air_conditioning |
| 2774.0 | Alfred_Kinsey |
| 2775.0 | Auto_racing |
| 2776.0 | Antisemitism |
| 2789.0 | James_Tiptree_Jr. |
| 2793.0 | Application_software |
| 2804.0 | Application_firewall |
| 2808.0 | Nuclear_weapon |
| 2821.0 | Set_theory |
| 2828.0 | Abipón |
| 2831.0 | Abkhazia |
| 2842.0 | Bohr_model |
| 2855.0 | Latin_American_Integration_Association |
| 2863.0 | AT&T |
| 2872.0 | Arthur,_Prince_of_Wales |
| 2880.0 | Anti-ballistic_missile |
| 2888.0 | Amorphous_solid |
| 2897.0 | Indigenous_peoples_of_Arizona |
| 2898.0 | Abdul_Rashid_Dostum |
| 2903.0 | The_Diary_of_a_Young_Girl |
| 2904.0 | Kabylia |
| 2912.0 | Archaeoastronomy |
| 2914.0 | French_hip_hop |
| 2915.0 | Gh_hip_hop |
| 2918.0 | Argument_from_ignorance |
| 2922.0 | AIM_(software) |
| 2929.0 | Armillary_sphere |
| 2937.0 | Algemeen_Nijmeegs_Studentenblad |
| 2951.0 | Louis_Althusser |
| 2969.0 | Aurora |
| 2970.0 | Aurora |
| 2971.0 | Abstraction_(computer_science) |
| 2977.0 | American_Sign_Language |
| 2993.0 | Amputation |
| 2996.0 | HMS_Ark_Royal |
| 2998.0 | Acceleration |
| 3000.0 | AD_Police_Files |
| 3005.0 | Apadravya |
| 3006.0 | Ampallang |
| 3008.0 | Albinism |
| 3009.0 | Analcime |
| 3023.0 | Archimedes'_screw |
| 3024.0 | Multiplication |
| 3033.0 | Antenna_(radio) |
| 3039.0 | Shadrach,_Meshach,_and_Abednego |
| 3041.0 | Acanthocephala |
| 3042.0 | Alcobaça |
| 3051.0 | Clan_McDuck |
| 3057.0 | List_of_Donald_Duck_universe_characters |
| 3059.0 | Athlon |
| 3062.0 | Duck_family_(Disney) |
| 3063.0 | Asperger_syndrome |
| 3066.0 | Authoritarianism |
| 3086.0 | İskenderun |
| 3099.0 | AbiWord |
| 3106.0 | AirPort |
| 3114.0 | Amiga_500 |
| 3126.0 | Ahriman |
| 3136.0 | Concept |
| 3139.0 | Apostle_(disambiguation) |
| 3154.0 | Fairchild_Republic_A-10_Thunderbolt_II |
| 3156.0 | Albrecht_Dürer |
| 3163.0 | Anthroposophy |
| 3164.0 | Evidence_of_common_descent |
| 3166.0 | A.C._Milan |
| 3180.0 | Anomaly |
| 3182.0 | Avenger |
| 3187.0 | Agglutination |
| 3190.0 | Ascending_chain_condition |
| 3197.0 | A._E._Housman |
| 3208.0 | Antidepressant |
| 3210.0 | Alexander_Rutskoy |
| 3215.0 | Multivibrator |
| 3219.0 | Actor |
| 3220.0 | Artificial_intelligence |
| 3223.0 | Ai |
| 3227.0 | Azores |
| 3230.0 | Relative_atomic_mass |
| 3232.0 | Anthropic_principle |
| 3247.0 | Roman_Catholic_Archdiocese_for_the_Military_Services,_USA |
| 3248.0 | Archaeopteryx |
| 3254.0 | Amuck! |
| 3260.0 | Line_Islands |
| 3264.0 | Aborigine |
| 3276.0 | Antiterrorism_and_Effective_Death_Penalty_Act_of_1996 |
| 3280.0 | Bomis |
| 3281.0 | Biblical_hermeneutics |
| 3282.0 | Baltic_Sea |
| 3283.0 | Ballroom_dance |
| 3284.0 | Biology |
| 3288.0 | Bill_Clinton |
| 3290.0 | Biblical_canon |
| 3298.0 | The_Buddha |
| 3299.0 | Bijection,_injection_and_surjection |
| 3300.0 | Buddhism |
| 3303.0 | Baltimore_Ravens |
| 3307.0 | Aaron |
| 3311.0 | List_of_business_schools_in_Asia |
| 3317.0 | The_Birth_of_a_Nation |
| 3318.0 | Boethius |
| 3320.0 | Mental_event |
| 3322.0 | Business_school |
| 3323.0 | Britney_Spears |
| 3326.0 | Baby_One_More_Time |
| 3327.0 | Binomial_distribution |
| 3329.0 | Binomial_distribution |
| 3330.0 | Biochemistry |
| 3342.0 | Germany |
| 3344.0 | Basic |
| 3346.0 | Robert_Byrd |
| 3349.0 | Business_school |
| 3366.0 | Commonwealth_of_Nations |
| 3369.0 | Board_game |
| 3373.0 | Outline_of_biology |
| 3407.0 | Baruch_Spinoza |
| 3409.0 | Ontology |
| 3413.0 | Batch_processing |
| 3418.0 | Basil |
| 3424.0 | BBC_Radio_1 |
| 3425.0 | BBC_Online |
| 3433.0 | Visual_impairment |
| 3445.0 | Alcohol_intoxication |
| 3448.0 | Steer_wrestling |
| 3480.0 | Royal_Bahamas_Defence_Force |
| 3481.0 | Foreign_relations_of_the_Bahamas |
| 3484.0 | Bahrain |
| 3492.0 | Baker_Island |
| 3493.0 | Baker_Island |
| 3494.0 | Baker_Island |
| 3496.0 | Baker_Island |
| 3509.0 | Foreign_relations_of_Bangladesh |
| 3510.0 | Foreign_relations_of_Bangladesh |
| 3519.0 | Foreign_relations_of_Barbados |
| 3522.0 | Bassas_da_India |
| 3524.0 | Bassas_da_India |
| 3527.0 | Bassas_da_India |
| 3529.0 | Bassas_da_India |
| 3539.0 | Telecommunications_in_Belarus |
| 3548.0 | Foreign_relations_of_Belgium |
| 3549.0 | Belgium |
| 3550.0 | Foreign_relations_of_Belgium |
| 3551.0 | Belgium |
| 3578.0 | Bermuda |
| 3587.0 | Bhutan |
| 3600.0 | Cultural_depictions_of_blindness |
| 3619.0 | Botswana_Defence_Force |
| 3622.0 | Bouvet_Island |
| 3623.0 | Bouvet_Island |
| 3624.0 | Bouvet_Island |
| 3625.0 | Bouvet_Island |
| 3626.0 | Bouvet_Island |
| 3627.0 | Bouvet_Island |
| 3628.0 | Bouvet_Island |
| 3640.0 | British_Indian_Ocean_Territory |
| 3641.0 | British_Indian_Ocean_Territory |
| 3642.0 | British_Indian_Ocean_Territory |
| 3643.0 | British_Indian_Ocean_Territory |
| 3644.0 | British_Indian_Ocean_Territory |
| 3645.0 | British_Indian_Ocean_Territory |
| 3646.0 | British_Indian_Ocean_Territory |
| 3647.0 | British_Indian_Ocean_Territory |
| 3656.0 | British_Virgin_Islands |
| 3686.0 | Geography_of_Myanmar |
| 3689.0 | Economy_of_Myanmar |
| 3690.0 | Telecommunications_in_Myanmar |
| 3723.0 | BSE |
| 3726.0 | Breakdancing |
| 3732.0 | Bhangra |
| 3737.0 | Baptists |
| 3739.0 | BSD_licenses |
| 3762.0 | Länder |
| 3763.0 | Bavaria |
| 3767.0 | Bundeskanzler |
| 3770.0 | Cabinet_of_Germany |
| 3773.0 | Der_Blaue_Reiter |
| 3781.0 | Mumbai |
| 3790.0 | Bodybuilding |
| 3791.0 | Bryan_MacLean |
| 3796.0 | Biblical_canon |
| 3803.0 | Strike_zone |
| 3804.0 | Slugging_percentage |
| 3818.0 | Babel_fish |
| 3820.0 | Mental_event |
| 3824.0 | Babel_fish |
| 3830.0 | Bryce_Canyon_National_Park |
| 3831.0 | Encyclopædia_Britannica |
| 3847.0 | Taste |
| 3855.0 | Origins_of_baseball |
| 3871.0 | Substance_theory |
| 3879.0 | Statistics |
| 3913.0 | Binary_operation |
| 3920.0 | The_Beatles |
| 3922.0 | Road_bicycle |
| 3934.0 | Baby_boom |
| 3935.0 | Buddhism |
| 3966.0 | Border_Gateway_Protocol |
| 3972.0 | Cycling |
| 3991.0 | BITS |
| 3994.0 | Benoit_Mandelbrot |
| 4003.0 | Pierre_Beaumarchais |
| 4014.0 | Bipolar_disorder |
| 4021.0 | Common_Era |
| 4022.0 | Common_Era |
| 4025.0 | BC |
| 4026.0 | Buckminster_Fuller |
| 4034.0 | Encyclopædia_Britannica_Eleventh_Edition |
| 4038.0 | Banach–Tarski_paradox |
| 4040.0 | BC |
| 4090.0 | Bitwise_operation |
| 4105.0 | Outline_of_biochemistry |
| 4122.0 | B-roll |
| 4126.0 | Ballroom_dance |
| 4129.0 | CIM-10_Bomarc |
| 4151.0 | Brainfuck |
| 4167.0 | Utility_knife |
| 4174.0 | Six_Degrees_of_Kevin_Bacon |
| 4186.0 | Bacteriostatic_agent |
| 4201.0 | Francesco_Borromini |
| 4212.0 | Bolsheviks |
| 4215.0 | Brian_De_Palma |
| 4221.0 | North_American_B-25_Mitchell |
| 4222.0 | Berry_Berenson |
| 4226.0 | Brewster's_angle |
| 4238.0 | The_Bronx |
| 4252.0 | Baháʼí_Faith |
| 4253.0 | Red_Army_Faction |
| 4265.0 | Titius–Bode_law |
| 4268.0 | The_Boston_Globe |
| 4272.0 | Elbląg |
| 4273.0 | Elbląg |
| 4275.0 | Gdańsk |
| 4276.0 | Oder |
| 4290.0 | Buddhism |
| 4291.0 | Buddhism |
| 4303.0 | University_of_Brighton |
| 4328.0 | Bohemia |
| 4336.0 | Bosnia_and_Herzegovina |
| 4412.0 | Binary_Synchronous_Communications |
| 4415.0 | ETA_(separatist_group) |
| 4426.0 | Brownian_motion |
| 4428.0 | Bacillus_thuringiensis |
| 4435.0 | Baltic_languages |
| 4439.0 | Baptists |
| 4464.0 | Book_of_Zechariah |
| 4466.0 | Black_Sox_Scandal |
| 4486.0 | Buckminsterfullerene |
| 4509.0 | GNU_Free_Documentation_License |
| 4521.0 | Bubble_sort |
| 4523.0 | Bipolar_disorder |
| 4530.0 | Blue_screen |
| 4562.0 | Pub |
| 4564.0 | Bitter_(beer) |
| 4586.0 | Greek_fire |
| 4590.0 | Brachycephaly |
| 4593.0 | Battleship_(game) |
| 4597.0 | Beryl |
| 4599.0 | Boleslaus_I |
| 4600.0 | Bolesław_III_Wrymouth |
| 4605.0 | Battle_of_the_Nile |
| 4612.0 | Bird |
| 4623.0 | Great_Britain_and_Ireland |
| 4632.0 | Monarchy_of_the_United_Kingdom |
| 4634.0 | Bombardier |
| 4655.0 | Alliance_90/The_Greens |
| 4656.0 | Shogun |
| 4657.0 | Arbitration |
| 4663.0 | Basil_of_Caesarea |
| 4666.0 | C*-algebra |
| 4678.0 | Computer_font |
| 4696.0 | Prime_Minister_of_the_United_Kingdom |
| 4697.0 | List_of_United_Kingdom_general_elections |
| 4703.0 | Bob_Dylan |
| 4716.0 | Bohemia |
| 4720.0 | Epistle_to_the_Hebrews |
| 4740.0 | International_Bureau_of_Weights_and_Measures |
| 4747.0 | Blu_Tack |
| 4750.0 | Bodhidharma |
| 4773.0 | Balfour_Declaration |
| 4784.0 | Normal_distribution |
| 4790.0 | German_Navy |
| 4798.0 | Bronze_Age |
| 4799.0 | Bicameral_mentality |
| 4808.0 | Arbitrary-precision_arithmetic |
| 4812.0 | Battle_of_Świecino |
| 4830.0 | Bohr_model |
| 4837.0 | Befehlshaber_der_U-Boote |
| 4844.0 | Symmetry_in_biology |
| 4846.0 | Symmetry_in_biology |
| 4853.0 | Wrocław |
| 4855.0 | Basso_continuo |
| 4889.0 | Semi-trailer_truck |
| 4891.0 | Ballet |
| 4901.0 | Daiquiri |
| 4903.0 | Boson |
| 4919.0 | Bipolar_II_disorder |
| 4920.0 | October_Revolution |
| 4923.0 | List_of_Bubblegum_Crisis_characters |
| 4932.0 | Basal_body_temperature |
| 4938.0 | Branch_predictor |
| 4939.0 | Gambling |
| 4954.0 | Battle_of_Świecino |
| 4962.0 | Batting_average_(baseball) |
| 4977.0 | Battle_of_Adrianople |
| 4984.0 | Battle_of_Adrianople |
| 4985.0 | Battle_of_the_Ardennes |
| 4998.0 | Operation_Aphrodite |
| 5010.0 | Mexican_tetra |
| 5012.0 | The_Adventures_of_Brisco_County,_Jr. |
| 5017.0 | The_Book_of_Counted_Sorrows |
| 5018.0 | Anal_sex |
| 5022.0 | B._F._Skinner |
| 5044.0 | Beast_of_Bodmin_Moor |
| 5054.0 | List_of_sovereign_states |
| 5055.0 | Computing |
| 5056.0 | Software |
| 5057.0 | Common_sense |
| 5058.0 | Celtic_music |
| 5060.0 | List_of_sovereign_states |
| 5061.0 | List_of_sovereign_states |
| 5062.0 | List_of_sovereign_states |
| 5063.0 | List_of_sovereign_states |
| 5064.0 | List_of_sovereign_states |
| 5065.0 | List_of_sovereign_states |
| 5066.0 | COBOL |
| 5067.0 | Christianity |
| 5068.0 | List_of_sovereign_states |
| 5069.0 | List_of_sovereign_states |
| 5070.0 | List_of_sovereign_states |
| 5071.0 | List_of_sovereign_states |
| 5072.0 | Country |
| 5073.0 | List_of_sovereign_states |
| 5074.0 | List_of_sovereign_states |
| 5075.0 | List_of_sovereign_states |
| 5076.0 | List_of_sovereign_states |
| 5077.0 | List_of_sovereign_states |
| 5078.0 | List_of_sovereign_states |
| 5079.0 | List_of_sovereign_states |
| 5080.0 | List_of_sovereign_states |
| 5081.0 | List_of_sovereign_states |
| 5082.0 | List_of_sovereign_states |
| 5085.0 | Berlin |
| 5088.0 | List_of_sovereign_states |
| 5089.0 | Cantor_set |
| 5093.0 | Cold_War |
| 5097.0 | Cryptography |
| 5098.0 | Cryptography |
| 5099.0 | Cryptanalysis |
| 5100.0 | Code |
| 5101.0 | Encryption |
| 5103.0 | Charleston |
| 5104.0 | Consequentialism |
| 5105.0 | On_the_Consolation_of_Philosophy |
| 5107.0 | Regress_argument |
| 5110.0 | Consciousness |
| 5112.0 | Charlie_Chaplin |
| 5115.0 | Khmer_language |
| 5120.0 | Chordate |
| 5121.0 | Combinatorics |
| 5122.0 | Constellation |
| 5123.0 | Cognitive_therapy |
| 5125.0 | Category_theory |
| 5126.0 | Summary_statistics |
| 5128.0 | Comedy_film |
| 5129.0 | Cult_film |
| 5130.0 | List_of_sovereign_states |
| 5133.0 | Charlize_Theron |
| 5137.0 | Cluster_sampling |
| 5138.0 | Cumulative_distribution_function |
| 5140.0 | Comedy_film |
| 5141.0 | Cult_film |
| 5143.0 | Cryptography |
| 5146.0 | Hash_function |
| 5149.0 | Computer_hardware |
| 5167.0 | Central_tendency |
| 5168.0 | Checkers |
| 5173.0 | Probability_distribution |
| 5181.0 | Continent |
| 5182.0 | Constitution |
| 5186.0 | List_of_sovereign_states |
| 5198.0 | Canadian_Armed_Forces |
| 5202.0 | List_of_cities_in_Canada |
| 5206.0 | Algorithmic_art |
| 5208.0 | List_of_sovereign_states |
| 5209.0 | The_World_Factbook |
| 5210.0 | C._S._Lewis |
| 5220.0 | Complex_number |
| 5227.0 | Chessboard |
| 5231.0 | Old_World_monkey |
| 5238.0 | List_of_sovereign_states |
| 5239.0 | Countable_set |
| 5242.0 | Ciliate |
| 5258.0 | Computer_data_storage |
| 5264.0 | Computer_monitor |
| 5283.0 | Cryptomonad |
| 5287.0 | Classical_music |
| 5289.0 | Card_game |
| 5290.0 | Casino_game |
| 5291.0 | PC_game |
| 5292.0 | Collectible_card_game |
| 5297.0 | Character_(computing) |
| 5303.0 | Conic_section |
| 5310.0 | Computer_hardware |
| 5318.0 | Time-sharing |
| 5319.0 | Computer_multitasking |
| 5341.0 | List_of_sovereign_states |
| 5343.0 | Constitution_of_Canada |
| 5345.0 | Colloid |
| 5356.0 | Cancer_cluster |
| 5359.0 | Collectible_card_game |
| 5365.0 | Ichthys |
| 5369.0 | Birth_control |
| 5392.0 | Coriander |
| 5393.0 | Coriander |
| 5396.0 | Chris_Morris |
| 5400.0 | List_of_sovereign_states |
| 5410.0 | Poales |
| 5414.0 | Wargame |
| 5418.0 | Capitalism |
| 5419.0 | Computer |
| 5423.0 | Cross-examination |
| 5425.0 | Class_conflict |
| 5426.0 | Compression |
| 5435.0 | Royal_Cambodian_Armed_Forces |
| 5441.0 | C_(programming_language) |
| 5442.0 | Constructed_language |
| 5444.0 | Regress_argument |
| 5445.0 | Class_conflict |
| 5457.0 | Civilization_(video_game) |
| 5476.0 | Cayman_Islands |
| 5501.0 | Christmas_Island |
| 5502.0 | Christmas_Island |
| 5503.0 | Christmas_Island |
| 5504.0 | Christmas_Island |
| 5505.0 | Christmas_Island |
| 5506.0 | Christmas_Island |
| 5507.0 | Christmas_Island |
| 5508.0 | Christmas_Island |
| 5511.0 | Clipperton_Island |
| 5512.0 | Clipperton_Island |
| 5513.0 | Clipperton_Island |
| 5514.0 | Clipperton_Island |
| 5515.0 | Clipperton_Island |
| 5516.0 | Clipperton_Island |
| 5517.0 | Clipperton_Island |
| 5518.0 | Clipperton_Island |
| 5521.0 | Cocos_(Keeling)_Islands |
| 5522.0 | Cocos_(Keeling)_Islands |
| 5524.0 | Cocos_(Keeling)_Islands |
| 5525.0 | Cocos_(Keeling)_Islands |
| 5526.0 | Cocos_(Keeling)_Islands |
| 5527.0 | Cocos_(Keeling)_Islands |
| 5528.0 | Cocos_(Keeling)_Islands |
| 5542.0 | Coral_Sea_Islands |
| 5543.0 | Coral_Sea_Islands |
| 5544.0 | Coral_Sea_Islands |
| 5545.0 | Coral_Sea_Islands |
| 5546.0 | Coral_Sea_Islands |
| 5547.0 | Coral_Sea_Islands |
| 5548.0 | Coral_Sea_Islands |
| 5549.0 | Coral_Sea_Islands |
| 5601.0 | Cypriot_National_Guard |
| 5604.0 | Czech_Republic |
| 5607.0 | Demographics_of_the_Czech_Republic |
| 5608.0 | Politics_of_the_Czech_Republic |
| 5612.0 | Army_of_the_Czech_Republic |
| 5613.0 | Foreign_relations_of_the_Czech_Republic |
| 5616.0 | Creutzfeldt–Jakob_disease |
| 5618.0 | A_Clockwork_Orange |
| 5620.0 | Stroke |
| 5628.0 | Compiler |
| 5631.0 | Gruyère_cheese |
| 5632.0 | Cheese_Shop_sketch |
| 5634.0 | List_of_decades,_centuries,_and_millennia |
| 5650.0 | Comet |
| 5652.0 | Computer_network |
| 5677.0 | Cerebrospinal_fluid |
| 5680.0 | Chief_executive_officer |
| 5683.0 | Trade_fair |
| 5687.0 | University_of_Cambridge |
| 5731.0 | Capitalism |
| 5737.0 | Cross-cutting |
| 5741.0 | Monetary_policy |
| 5746.0 | Hash_function |
| 5747.0 | Key_(cryptography) |
| 5753.0 | Sexual_intercourse |
| 5764.0 | Charlie_Chaplin |
| 5773.0 | Carroll_O'Connor |
| 5780.0 | Chaco_Culture_National_Historical_Park |
| 5788.0 | Cretaceous–Paleogene_extinction_event |
| 5792.0 | Probability_distribution |
| 5798.0 | Closeted |
| 5799.0 | Coming_out |
| 5801.0 | Ecumenical_council |
| 5802.0 | Council_of_Trent |
| 5803.0 | Second_Vatican_Council |
| 5842.0 | Foreign_relations_of_Colombia |
| 5852.0 | Foreign_relations_of_the_Czech_Republic |
| 5856.0 | Holy_Roman_Empire |
| 5870.0 | Comics |
| 5871.0 | Tachycardia |
| 5875.0 | Jargon |
| 5877.0 | CORAL |
| 5880.0 | Comment_(computer_programming) |
| 5900.0 | Megacorporation |
| 5908.0 | Counterpoint |
| 5911.0 | Continuum_hypothesis |
| 5913.0 | Catalysis |
| 5915.0 | Catalysis |
| 5924.0 | Christian_eschatology |
| 5925.0 | Color |
| 5953.0 | Claude_Monet |
| 5960.0 | Genetic_code |
| 5968.0 | Computer_music |
| 5975.0 | Call_of_Cthulhu_(role-playing_game) |
| 5978.0 | Kyoto_Protocol |
| 5983.0 | Computer_science |
| 6012.0 | Church–Turing_thesis |
| 6017.0 | Cruise_missile |
| 6018.0 | Call_of_Cthulhu |
| 6022.0 | Cell_biology |
| 6030.0 | Chronic_fatigue_syndrome |
| 6031.0 | Chronic_fatigue_syndrome |
| 6032.0 | Chronic_fatigue_syndrome |
| 6033.0 | Chronic_fatigue_syndrome |
| 6037.0 | Continuous_function |
| 6043.0 | Critical_point_(thermodynamics) |
| 6053.0 | CE |
| 6054.0 | CE |
| 6055.0 | CD-ROM |
| 6063.0 | Cartoonist |
| 6065.0 | Sine_and_cosine |
| 6067.0 | Common_Lisp |
| 6070.0 | Orange_(colour) |
| 6071.0 | Black |
| 6074.0 | Orange_(colour) |
| 6076.0 | Cyan |
| 6077.0 | Black |
| 6078.0 | White |
| 6086.0 | Cauchy_sequence |
| 6087.0 | Nicolaus_Copernicus |
| 6089.0 | Creationism |
| 6098.0 | Carolingian_Renaissance |
| 6142.0 | Cardinal_number |
| 6150.0 | Blanching_(cooking) |
| 6178.0 | Cardinal |
| 6179.0 | Buddhist_cuisine |
| 6190.0 | Five-spice_powder |
| 6196.0 | Self-replicating_machine |
| 6197.0 | Self-replicating_machine |
| 6202.0 | London_Convention_on_the_Prevention_of_Marine_Pollution_by_Dumping_of_Wastes_and_Other_Matter |
| 6204.0 | Ramsar_Convention |
| 6219.0 | Claudio_Monteverdi |
| 6223.0 | Comics |
| 6228.0 | List_of_ancient_Celtic_peoples_and_tribes |
| 6236.0 | Champagne_socialist |
| 6240.0 | Celtic_languages |
| 6242.0 | Glossary_of_climbing_terms |
| 6243.0 | Cascade_Range |
| 6263.0 | Charles_Darwin |
| 6266.0 | Climate_change |
| 6269.0 | Wipe_(transition) |
| 6278.0 | Banach_space |
| 6287.0 | Lists_of_cities_by_country |
| 6302.0 | Classical_element |
| 6307.0 | Aether_(classical_element) |
| 6311.0 | College_football |
| 6345.0 | Central_dogma_of_molecular_biology |
| 6348.0 | Medal_of_Honor |
| 6368.0 | Chōshū |
| 6461.0 | Wuxing_(Chinese_philosophy) |
| 6464.0 | Mobile_phone |
| 6470.0 | Computational_linguistics |
| 6500.0 | Lists_of_universities_and_colleges |
| 6502.0 | Clean_Air_Act_(United_States) |
| 6510.0 | Color_space |
| 6515.0 | Lists_of_atheists |
| 6522.0 | Chief_executive_officer |
| 6524.0 | Clam_dip |
| 6531.0 | Chinese_cuisine |
| 6553.0 | Context-free_grammar |
| 6554.0 | Computer_graphics |
| 6564.0 | Conjunction_elimination |
| 6573.0 | Widewuto |
| 6581.0 | Musique_concrète |
| 6594.0 | Casimir_IV_Jagiellon |
| 6595.0 | Computer_vision |
| 6605.0 | Citric_acid_cycle |
| 6609.0 | Stork |
| 6622.0 | Coelenterata |
| 6625.0 | Catholic_Church |
| 6646.0 | List_of_ancient_Germanic_peoples |
| 6657.0 | Catholic_Church |
| 6668.0 | Mousse |
| 6676.0 | Consociationalism |
| 6685.0 | Coca-Cola |
| 6699.0 | Plato |
| 6709.0 | Tree_(data_structure) |
| 6712.0 | Compressor |
| 6714.0 | Comic_book |
| 6726.0 | Antisemitism_in_Christianity |
| 6737.0 | Dhole |
| 6738.0 | Red_wolf |
| 6740.0 | Coyote |
Let's turn these redirect table entries into the same format as the edge links - that is, with an article ID both for the source and the destination:
val redirectsWithDstID = spark.sql("""SELECT enwiki_redirect.rd_from AS src,
enwiki_page.page_id AS dst,
enwiki_redirect.rd_title AS dst_title
FROM enwiki_redirect INNER JOIN enwiki_page
ON enwiki_redirect.rd_title = enwiki_page.page_title""")
redirectsWithDstID.createOrReplaceTempView("redirectsWithDstID")
val redirectsWithIDs = spark.sql("""SELECT redirectsWithDstID.src,
redirectsWithDstID.dst,
enwiki_page.page_title AS src_title,
redirectsWithDstID.dst_title
FROM redirectsWithDstID INNER JOIN enwiki_page
ON enwiki_page.page_id = redirectsWithDstID.src""")
display(redirectsWithIDs)
| src | dst | src_title | dst_title |
|---|---|---|---|
| 53.0 | 1171.0 | Abbreviations | Abbreviation |
| 251.0 | 2709.0 | AberdeenSouthDakota | Aberdeen,_South_Dakota |
| 255.0 | 339.0 | AynRand | Ayn_Rand |
| 296.0 | 184552.0 | ActressesS | Lists_of_actors |
| 858.0 | 2710.0 | AU | Au |
| 918.0 | 1078.0 | Anti-semitism | Antisemitism |
| 939.0 | 1.8950736e7 | Automated_Alice/VIII | Automated_Alice |
| 970.0 | 1.8934564e7 | AmbientCalculusOnline | Ambient_calculus |
| 1199.0 | 2136.0 | Angle_tribe | Angles |
| 1238.0 | 21785.0 | Atomic_bomb | Nuclear_weapon |
| 1339.0 | 1338.0 | Americans_with_Disabilities_Act_of_1990/Findings_and_Purposes | Americans_with_Disabilities_Act_of_1990 |
| 1342.0 | 1400.0 | A.D | Anno_Domini |
| 1533.0 | 1520.0 | Aix-la-Chapelle | Aachen |
| 1561.0 | 192974.0 | Aidan_of_Dalriada | Áedán_mac_Gabráin |
| 1699.0 | 69607.0 | Alfonso_VI | Alfonso_VI_of_León_and_Castile |
| 1745.0 | 23834.0 | Anastasius_IV | Pope_Anastasius_IV |
| 1766.0 | 47264.0 | Asteroid_Belt | Asteroid_belt |
| 1903.0 | 1902.0 | American_Airlines_flight_77 | American_Airlines_Flight_77 |
| 1959.0 | 2.6668156e7 | Automatic_telephone_exchange | Telephone_exchange |
| 2249.0 | 186151.0 | Amplify | Amplification |
| 2261.0 | 1.8932608e7 | Analog_Magazine | Analog_Science_Fiction_and_Fact |
| 2479.0 | 20481.0 | Al-Manamah | Manama |
| 2525.0 | 4.8370461e7 | AlJazeera | Al_Jazeera |
| 2572.0 | 1252.0 | Arians | Arianism |
| 2655.0 | 1046.0 | Abatement_in_litigation | Abatement |
| 2656.0 | 1046.0 | Abatement_of_false_lights | Abatement |
| 2659.0 | 771.0 | American_war_of_independence | American_Revolutionary_War |
| 2711.0 | 1514856.0 | Aberdeenshire/Aberdeenshire1911 | Aberdeenshire_(historic) |
| 2771.0 | 7221088.0 | Air_conditioner | Air_conditioning |
| 2776.0 | 1078.0 | Anti-Semite | Antisemitism |
| 2821.0 | 27553.0 | Axiomatic_Set_Theory | Set_theory |
| 2888.0 | 2889.0 | Amorphous | Amorphous_solid |
| 2914.0 | 53185.0 | African_rap_in_France | French_hip_hop |
| 2971.0 | 60491.0 | Abstraction_in_object-oriented_programming | Abstraction_(computer_science) |
| 2996.0 | 14292.0 | Ark_Royal | HMS_Ark_Royal |
| 3000.0 | 2284718.0 | A.D._Police:_Dead_End_City | AD_Police_Files |
| 3008.0 | 4.5105839e7 | Albino | Albinism |
| 3042.0 | 2.4737268e7 | Alcobaca | Alcobaça |
| 3062.0 | 77548.0 | Abner_Duck | Duck_family_(Disney) |
| 3220.0 | 1164.0 | A.I. | Artificial_intelligence |
| 3425.0 | 638514.0 | BBC/Online | BBC_Online |
| 3790.0 | 4360.0 | BodyBuilding | Bodybuilding |
| 3796.0 | 2.7857492e7 | Books_of_the_Bible | Biblical_canon |
| 3913.0 | 3948.0 | BinaryOperation | Binary_operation |
| 4126.0 | 3332.0 | Ballroom_dancing | Ballroom_dance |
| 4167.0 | 4168.0 | Box-cutter_knives | Utility_knife |
| 4186.0 | 153831.0 | Bacteriostat | Bacteriostatic_agent |
| 4291.0 | 3267529.0 | Buddhists | Buddhism |
| 4415.0 | 9926.0 | Basque_Fatherland_and_Liberty | ETA_(separatist_group) |
| 4612.0 | 3410.0 | Birds | Bird |
| 4697.0 | 2.5767603e7 | United_Kingdom_general_election | List_of_United_Kingdom_general_elections |
| 4773.0 | 4820.0 | Balfour_declaration | Balfour_Declaration |
| 4798.0 | 4620.0 | Bronze_age | Bronze_Age |
| 4799.0 | 2181792.0 | Bicameral_mind | Bicameral_mentality |
| 4923.0 | 1.3682978e7 | Boomeroid | List_of_Bubblegum_Crisis_characters |
| 5055.0 | 5213.0 | ComputinG | Computing |
| 5061.0 | 68253.0 | CountriesN | List_of_sovereign_states |
| 5071.0 | 68253.0 | CountriesK | List_of_sovereign_states |
| 5074.0 | 68253.0 | CountriesH | List_of_sovereign_states |
| 5100.0 | 5225.0 | CodE | Code |
| 5110.0 | 5664.0 | ConsciousNess | Consciousness |
| 5140.0 | 5644.0 | Comedy_Film | Comedy_film |
| 5173.0 | 23543.0 | Continuous_Random_Variable | Probability_distribution |
| 5287.0 | 6668778.0 | Classical_Music | Classical_music |
| 5345.0 | 5346.0 | Colloids | Colloid |
| 5457.0 | 6259.0 | Civilization/video_game | Civilization_(video_game) |
| 5513.0 | 5510.0 | Clipperton_Island/People | Clipperton_Island |
| 5518.0 | 5510.0 | Clipperton_Island/Military | Clipperton_Island |
| 5527.0 | 5520.0 | Transport_in_the_Cocos_(Keeling)_Islands | Cocos_(Keeling)_Islands |
| 5543.0 | 5541.0 | Coral_Sea_Islands/Geography | Coral_Sea_Islands |
| 5803.0 | 28134.0 | Catholicism/Second_Vatican_Council | Second_Vatican_Council |
| 6142.0 | 6173.0 | Cardinal_numbers | Cardinal_number |
| 6204.0 | 195228.0 | Convention_on_Wetlands_of_International_Importance_Especially_As_Waterfowl_Habitat | Ramsar_Convention |
| 6266.0 | 5042951.0 | Climate_Change | Climate_change |
| 6500.0 | 5252.0 | Colleges_and_universities/OldList | Lists_of_universities_and_colleges |
| 6622.0 | 1779159.0 | Coelenterates | Coelenterata |
| 6737.0 | 8626.0 | Cuon_alpinus | Dhole |
| 6741.0 | 4269567.0 | Canis_familiaris | Dog |
| 6864.0 | 1484989.0 | Chromosome_walking | Primer_walking |
| 6937.0 | 13998.0 | Containment_hierarchy | Hierarchy |
| 6953.0 | 150211.0 | Chang_San-feng | Zhang_Sanfeng |
| 7098.0 | 53133.0 | COPPA | Children's_Online_Privacy_Protection_Act |
| 7417.0 | 7411.0 | Constitution_of_Canada/1867_V_Provincial_Constitutions | Constitution_of_Canada |
| 7644.0 | 4930.0 | Colin_Fulcher | Barney_Bubbles |
| 7666.0 | 7655.0 | Clay_math_prize | Clay_Mathematics_Institute |
| 7726.0 | 15129.0 | Cow_story | You_have_two_cows |
| 7744.0 | 3359456.0 | Cell_incubator | Incubator_(culture) |
| 7880.0 | 8495.0 | DataSeT | Data_set |
| 7987.0 | 4009259.0 | Defensive_team | American_football_positions |
| 8105.0 | 13883.0 | Data_compression/Huffman_coding | Huffman_coding |
| 8423.0 | 5936580.0 | DragonMagazine | Dragon_Magazine |
| 8665.0 | 7888.0 | D.W._Griffith | D._W._Griffith |
| 8755.0 | 20018.0 | Distance_function | Metric_space |
| 8803.0 | 816386.0 | Danewerk | Danevirke |
| 8911.0 | 8900.0 | Discriminatory | Discrimination |
| 8924.0 | 8968.0 | Devanagiri | Devanagari |
| 8928.0 | 8930.0 | Denis_Arkadievich_Kaufman | Dziga_Vertov |
| 8977.0 | 39289.0 | Design_by_Contract | Design_by_contract |
| 9006.0 | 8778.0 | Daniel_Ortega_Saavedra | Daniel_Ortega |
| 9097.0 | 8968.0 | Devangari_alphabet | Devanagari |
| 9181.0 | 9223.0 | EconomicS | Economics |
| 9182.0 | 9700.0 | EdwinAustinAbbey | Edwin_Austin_Abbey |
| 9293.0 | 9454.0 | Establishing_Shot | Establishing_shot |
| 9416.0 | 9407.0 | Europa_Island/Transnational_issues | Europa_Island |
| 9435.0 | 61069.0 | Frederic_Henry | A_Farewell_to_Arms |
| 9465.0 | 1890.0 | English_language/American_English | American_English |
| 10022.0 | 51791.0 | E.163 | E.164 |
| 10143.0 | 125412.0 | East_Brunswick | East_Brunswick,_New_Jersey |
| 10532.0 | 2.3976719e7 | FootBall | Football |
| 10558.0 | 10869.0 | FrequencyProbability | Frequentist_probability |
| 10567.0 | 226981.0 | FiniteMathematics | Finite_mathematics |
| 10587.0 | 2672356.0 | Film_Techniques | Cinematic_techniques |
| 10605.0 | 10844.0 | FrenchMaterialism | French_materialism |
| 10611.0 | 2518541.0 | Film_History/Russia | Cinema_of_Russia |
| 10745.0 | 10737.0 | French_Polynesia/Military | French_Polynesia |
| 10768.0 | 10724.0 | Military_of_French_Guiana | French_Armed_Forces |
| 10840.0 | 11012.0 | FORTH | Forth_(programming_language) |
| 11014.0 | 11379.0 | Famous_Scotsmen | List_of_Scots |
| 11106.0 | 11125.0 | Francesco_Boromini | Francesco_Borromini |
| 11316.0 | 10890.0 | Fundamental_forces | Fundamental_interaction |
| 11450.0 | 2.6526175e7 | Friedrich_II | Frederick_II |
| 11482.0 | 11059.0 | Five_pillars_of_Islam | Five_Pillars_of_Islam |
| 11487.0 | 1.89336e7 | File_Formats | File_format |
| 11500.0 | 11501.0 | Federated_States_of_Micronesia/Transport | Transportation_in_the_Federated_States_of_Micronesia |
| 11578.0 | 4.9417917e7 | Facism | Fascism_(disambiguation) |
| 11808.0 | 11807.0 | Ferromagnetic | Ferromagnetism |
| 11858.0 | 1.8723138e7 | Games | Game |
| 11897.0 | 13236.0 | GNU/HURD | GNU_Hurd |
| 11911.0 | 25475.0 | Games/RolePlaying | Role-playing_game |
| 11912.0 | 2.6654282e7 | Games/TradingCard | Collectible_card_game |
| 11936.0 | 13224.0 | Germany/History | History_of_Germany |
| 12006.0 | 570227.0 | Godzilla_on_Monster_Island | Godzilla_vs._Gigan |
| 12085.0 | 7607314.0 | Military_of_Gibraltar | Gibraltar |
| 12169.0 | 12166.0 | Demographics_of_Guernsey | Guernsey |
| 12210.0 | 11985.0 | Graffiti_art | Graffiti |
| 12238.0 | 12024.0 | General_Relativity | General_relativity |
| 12530.0 | 38579.0 | Gravitational_interaction | Gravity |
| 12626.0 | 12612.0 | General_Aviation | General_aviation |
| 12757.0 | 1.8938782e7 | GNU_Free_Documentation_License/Secondary_sections | GNU_Free_Documentation_License |
| 13170.0 | 11830.0 | GT40 | Ford_GT40 |
| 13188.0 | 13207.0 | HecTor | Hector |
| 13368.0 | 51429.0 | HyperReal_numbers | Hyperreal_number |
| 13500.0 | 2.3191617e7 | Typed_link | Link_relation |
| 13504.0 | 31353.0 | Hitch_Hikers_Guide_to_the_Galaxy | The_Hitchhiker's_Guide_to_the_Galaxy |
| 13720.0 | 3.354326e7 | Higher_Criticism. | Historical_criticism |
| 13745.0 | 14045.0 | Humprey_Bogart | Humphrey_Bogart |
| 13751.0 | 2.366126e7 | Heterozygote | Zygosity |
| 13832.0 | 13834.0 | Hello_world | \"Hello,_World!\"_program |
| 13916.0 | 59701.0 | Harry_Potter/Broom | Broom |
| 13997.0 | 41821.0 | Hierarchical_tree_structure | Tree_structure |
| 14044.0 | 13509.0 | Howard_Philips_Lovecraft | H._P._Lovecraft |
| 14075.0 | 54033.0 | Horse_Breed | List_of_horse_breeds |
| 14284.0 | 549333.0 | Hemochromatosis | Iron_overload |
| 14332.0 | 88412.0 | Haenir | Hœnir |
| 14514.0 | 15049.0 | IndianapolisColts | Indianapolis_Colts |
| 14525.0 | 194373.0 | Independents | Independent |
| 14556.0 | 2.2393474e7 | Input/Output_Device | Input/output |
| 14770.0 | 14727.0 | Military_of_the_Isle_of_Man | Isle_of_Man |
| 14771.0 | 1.13051e7 | Isle_of_Man/Transnational_issues | External_relations_of_the_Isle_of_Man |
| 14846.0 | 2.3430752e7 | I.R.S. | Internal_Revenue_Service |
| 15003.0 | 1.856704e7 | Mental_deficiency | Intellectual_disability |
| 15026.0 | 19048.0 | Inertial_mass | Mass |
| 15060.0 | 15059.0 | Isaac_Bonewits_laws_of_magic | Isaac_Bonewits |
| 15157.0 | 15459.0 | ICD-CM | International_Classification_of_Diseases |
| 15162.0 | 5144840.0 | Intel_Pentium | Pentium |
| 15173.0 | 6037917.0 | Islamic | Islam |
| 15202.0 | 13998.0 | Immediate_subordinate | Hierarchy |
| 15348.0 | 519280.0 | Intelligent | Intelligence |
| 15557.0 | 62699.0 | JapanConstitution/ChapterOne | Constitution_of_Japan |
| 15558.0 | 62699.0 | JapanConstitution/ChapterTwo | Constitution_of_Japan |
| 15594.0 | 1095706.0 | JesusChrist | Jesus |
| 15727.0 | 15724.0 | Juan_de_Nova_Island/People | Juan_de_Nova_Island |
| 15840.0 | 23805.0 | John_Paul_II | Pope_John_Paul_II |
| 15957.0 | 16509.0 | Jeanne_of_Arc | Joan_of_Arc |
| 16503.0 | 31411.0 | Jake_McDuck | Clan_McDuck |
| 16532.0 | 452493.0 | Flow_through_nozzles | De_Laval_nozzle |
| 16601.0 | 16616.0 | KingCrimson | King_Crimson |
| 16706.0 | 12235.0 | Kokturks | Göktürks |
| 16798.0 | 16796.0 | Kuiper_Belt | Kuiper_belt |
| 16819.0 | 230961.0 | K-12_School | K–12 |
| 16924.0 | 2.0647197e7 | K56flex | Modem |
| 17008.0 | 27069.0 | Kierkegaard | Søren_Kierkegaard |
| 17036.0 | 327489.0 | Kimberley_Classic | Pale_lager |
| 17074.0 | 17073.0 | Kanchenjuna | Kangchenjunga |
| 17113.0 | 2201563.0 | Keyed_sequential_data_set | Key_Sequenced_Data_Set |
| 17223.0 | 379671.0 | K_and_R | The_C_Programming_Language |
| 17225.0 | 6021.0 | K_and_R_C | C_(programming_language) |
| 17347.0 | 25927.0 | Kurchatovium | Rutherfordium |
| 17437.0 | 7953994.0 | Karine_A | Karine_A_affair |
| 17438.0 | 16959.0 | Katyusha_rockets | Katyusha_rocket_launcher |
| 17508.0 | 17514.0 | LatviA | Latvia |
| 17525.0 | 17627.0 | LiberaL | Liberal |
| 17613.0 | 17615.0 | Lewis_and_Clark | Lewis_and_Clark_Expedition |
| 17678.0 | 2226.0 | Logical_fallacy/Ad_Hominem | Ad_hominem |
| 17679.0 | 39057.0 | Logical_fallacy/Straw_Man | Straw_man |
| 17708.0 | 244629.0 | Law_of_physics | Scientific_law |
| 17751.0 | 18496.0 | Loveparade | Love_Parade |
| 17969.0 | 17972.0 | Louis_the_pious | Louis_the_Pious |
| 18105.0 | 251399.0 | Large-scale_structure_of_the_Cosmos | Observable_universe |
| 18107.0 | 9767.0 | Lords_Supper | Eucharist |
| 18174.0 | 12634.0 | List_of_Greek_islands | List_of_islands_of_Greece |
| 18296.0 | 10972.0 | Loding | Fenrir |
| 18405.0 | 543568.0 | Lorentz_invariance | Lorentz_covariance |
| 18502.0 | 18499.0 | Leftists | Left-wing_politics |
| 18660.0 | 17626.0 | Labour_union | Trade_union |
| 18741.0 | 18887.0 | MetaPhilosophy | Metaphilosophy |
| 18746.0 | 18859.0 | MichigaN | Michigan |
| 18750.0 | 20087.0 | ModularArithmetic | Modular_arithmetic |
| 18782.0 | 19325.0 | MonIsm | Monism |
| 18818.0 | 1.3675377e7 | MetaWiki | History_of_wikis |
| 18827.0 | 18887.0 | Meta-Philosophy | Metaphilosophy |
| 18860.0 | 19447.0 | MathematicalGroup | Group_(mathematics) |
| 18944.0 | 4.2796964e7 | Methodological_naturalism | Naturalism_(philosophy) |
| 19317.0 | 19318.0 | Marylin_Monroe | Marilyn_Monroe |
| 19335.0 | 19338.0 | Mountain_Range | Mountain_range |
| 19480.0 | 18866.0 | Macbeth/Act_III_Scene_v | Macbeth |
| 19685.0 | 2.4698694e7 | Mythology | Myth |
| 19915.0 | 18984.0 | Mongol | Mongols |
| 19949.0 | 10585.0 | Mastigophora | Flagellate |
| 20020.0 | 20640.0 | MacOS_X | MacOS |
| 20052.0 | 18830.0 | Magic_the_Gathering | Magic:_The_Gathering |
| 20058.0 | 19999.0 | Microprogram. | Microcode |
| 20135.0 | 4.3423305e7 | Marines_(disambiguation) | Marine |
| 20163.0 | 200877.0 | Maze_generation_algorthims | Maze_generation_algorithm |
| 20382.0 | 8609564.0 | Marsh_USA | Marsh_McLennan |
| 20409.0 | 20408.0 | Marie_Sklodowska-Curie | Marie_Curie |
| 20425.0 | 20426.0 | Metonic | Metonic_cycle |
| 20473.0 | 20474.0 | Mohs_hardness_scale | Mohs_scale_of_mineral_hardness |
| 20490.0 | 88003.0 | Menstrual | Menstrual_cycle |
| 20506.0 | 233403.0 | Medieval_siege_weaponry | Siege_engine |
| 20519.0 | 5643937.0 | Mathematics_of_musical_scales | Music_and_mathematics |
| 20554.0 | 204504.0 | Millenia | Millennium |
| 20982.0 | 3189.0 | Minimum_condition | Ascending_chain_condition |
| 21016.0 | 1.858223e7 | Marsh_Gas | Methane |
| 21077.0 | 2.615557e7 | NuPedia | Nupedia |
| 21116.0 | 5.6571945e7 | NASCAR_Championship | NASCAR_Cup_Series |
| 21126.0 | 8210131.0 | New_York_(U.S._state) | New_York_(state) |
| 21130.0 | 21211.0 | NFL | National_Football_League |
| 21528.0 | 5591552.0 | Nintendo_Gameboy | Game_Boy |
| 21603.0 | 7851.0 | Nuclear_Test_Ban | Comprehensive_Nuclear-Test-Ban_Treaty |
| 21700.0 | 21699.0 | Ninevah | Nineveh |
| 21884.0 | 21523.0 | Neural_nets | Artificial_neural_network |
| 21909.0 | 3.1045316e7 | Nazis | Nazism |
| 22064.0 | 39807.0 | Nature_versus_nurture_debate | Nature_versus_nurture |
| 22215.0 | 1.8842359e7 | Oceans | Ocean |
| 22364.0 | 453372.0 | Object_orientation | Object |
| 22414.0 | 22362.0 | Ordered_pairs | Ordered_pair |
| 22432.0 | 22433.0 | Orang_utan | Orangutan |
| 22502.0 | 3009731.0 | O_Sensei | Sensei |
| 22521.0 | 72335.0 | Onanism | Onan |
| 22845.0 | 23486.0 | PhilZimmermann | Phil_Zimmermann |
| 22857.0 | 24113.0 | PresidentOfTheUnitedStates | President_of_the_United_States |
| 22884.0 | 7576966.0 | PierreDeFermat | Pierre_de_Fermat |
| 22917.0 | 23289.0 | Persistence_of_Vision | Persistence_of_vision |
| 23086.0 | 2.1431937e7 | Poker_equipment | Glossary_of_poker_terms |
| 23120.0 | 2.4527593e7 | Straight_flush | List_of_poker_hands |
| 23136.0 | 75691.0 | No_limit_(poker) | Betting_in_poker |
| 23215.0 | 6675.0 | Political_conservative | Conservatism |
| 23271.0 | 1583825.0 | Paper,_Scissor,_Stone | Paper,_Scissors,_Stone |
| 23286.0 | 23005.0 | Philip_K._Dick/The_Galactic_Pot_Healer | Philip_K._Dick |
| 23455.0 | 23450.0 | Pitcairn_Islands/Economy | Pitcairn_Islands |
| 23457.0 | 23450.0 | Transportation_on_the_Pitcairn_Islands | Pitcairn_Islands |
| 23487.0 | 468436.0 | PSTN | Public_switched_telephone_network |
| 23523.0 | 1368.0 | Programming_language/assembly | Assembly_language |
| 23581.0 | 23276.0 | Philosophers | Philosopher |
| 23609.0 | 217578.0 | Phases | Phase |
| 23993.0 | 1886819.0 | Prelude_In_G_Major | G_major |
| 24128.0 | 2.7643777e7 | Physics_instrumentation | Measuring_instrument |
| 24200.0 | 9233734.0 | Parc | PARC |
| 24299.0 | 24324.0 | PLO | Palestine_Liberation_Organization |
| 24504.0 | 23745.0 | Pokemon | Pokémon |
| 24526.0 | 22986.0 | Political | Politics |
| 24719.0 | 238253.0 | Pornografic_film | Pornographic_film |
| 25092.0 | 3.3434315e7 | PR_Watch | Center_for_Media_and_Democracy |
| 25168.0 | 25169.0 | Quentin_Tarrantino | Quentin_Tarantino |
| 25196.0 | 480513.0 | Cities_of_Qatar | List_of_cities_in_Qatar |
| 25355.0 | 25410.0 | RhodeIsland | Rhode_Island |
| 25512.0 | 210339.0 | Rap_music/Bass | Miami_bass |
| 25627.0 | 86772.0 | History_of_Reunion | Réunion |
| 25909.0 | 25389.0 | Robert_A_Heinlein | Robert_A._Heinlein |
| 26070.0 | 28506.0 | Rocket_propulsion | Spacecraft_propulsion |
| 26087.0 | 1.933731e7 | Rodentia | Rodent |
| 26120.0 | 25475.0 | Role_playing_game | Role-playing_game |
| 26165.0 | 9775.0 | Rough_ER | Endoplasmic_reticulum |
| 26178.0 | 162321.0 | Rest_mass | Invariant_mass |
| 26258.0 | 26306.0 | RnF | Radon_difluoride |
| 26528.0 | 7706.0 | Rectangular_coordinate_system | Cartesian_coordinate_system |
| 26548.0 | 26547.0 | Rugby_Union_Five_Nations_Championship/Results | Six_Nations_Championship |
| 26623.0 | 27159.0 | SherlockHolmes | Sherlock_Holmes |
| 26635.0 | 162255.0 | SwingDance | Swing_(dance) |
| 26636.0 | 26787.0 | ScienceFiction | Science_fiction |
| 26729.0 | 63780.0 | Sporangia | Sporangium |
| 26778.0 | 26915.0 | SapirWhorfHypothesis | Linguistic_relativity |
| 26796.0 | 1.7157886e7 | StarTrek | Star_Trek |
| 26801.0 | 2.8222625e7 | Sega_hardware | Sega |
| 27030.0 | 27022.0 | South_Korea/Language | Demographics_of_South_Korea |
| 27039.0 | 191302.0 | Swedish_municipality | Municipalities_of_Sweden |
| 27780.0 | 27616.0 | Sun/Sunspot | Sunspot |
| 27938.0 | 27939.0 | Stockholm/history | History_of_Stockholm |
| 27966.0 | 11041.0 | Saussure,_Ferdinand_de | Ferdinand_de_Saussure |
| 27974.0 | 43948.0 | Star_Formation | Star_formation |
| 28124.0 | 30644.0 | Stranglers/Golden_Brown | Golden_Brown |
| 28298.0 | 30320.0 | The_Strand_(Band) | Sex_Pistols |
| 28315.0 | 28314.0 | SNES | Super_Nintendo_Entertainment_System |
| 28331.0 | 894164.0 | Stubs | Stub |
| 28346.0 | 59173.0 | Superego | Id,_ego_and_super-ego |
| 28497.0 | 3.3103292e7 | Sputnik_program | List_of_spacecraft_called_Sputnik |
| 28836.0 | 28837.0 | Siege_towers | Siege_tower |
| 28883.0 | 41676.0 | Saturated | Saturation |
| 28905.0 | 1708335.0 | Sanger_method | Sanger_sequencing |
| 29061.0 | 5564386.0 | Suffix_morpheme | Suffix |
| 29117.0 | 14337.0 | Sexual_practices | Human_sexual_activity |
| 29194.0 | 13861.0 | Southamptonshire | Hampshire |
| 29220.0 | 29219.0 | Stone_age | Stone_Age |
| 29225.0 | 1.1993966e7 | Schnorkel | Submarine_snorkel |
| 29566.0 | 11757.0 | Sacramento_class_support_ship | Fast_combat_support_ship |
| 29653.0 | 29660.0 | State_Terrorism | State_terrorism |
| 29714.0 | 9302.0 | TheExistenceOfPhysicalObjects | Existence |
| 29719.0 | 30104.0 | TheProblemOfEvil | Problem_of_evil |
| 29744.0 | 29932.0 | The_Origin_of_Species/Chapter_10 | On_the_Origin_of_Species |
| 29746.0 | 29932.0 | The_Origin_of_Species/Chapter_12 | On_the_Origin_of_Species |
| 29791.0 | 24022.0 | Therapy/Physical | Physical_therapy |
| 29894.0 | 292279.0 | The_Simpsons/Elizabeth_Hoover | List_of_recurring_The_Simpsons_characters |
| 29997.0 | 1338.0 | The_Americans_with_Disabilites_Act_of_1990/Definitions | Americans_with_Disabilities_Act_of_1990 |
| 30105.0 | 1.5247542e7 | The_rationality_of_atheism | Atheism |
| 30183.0 | 30178.0 | Tromelin_Island/Economy | Tromelin_Island |
| 30218.0 | 642023.0 | Turks_and_Caicos_Islands/History | History_of_the_Turks_and_Caicos_Islands |
| 30219.0 | 30217.0 | Turks_and_Caicos_Islands/Geography | Turks_and_Caicos_Islands |
| 30238.0 | 1.1081176e7 | Mind-body_problem | Mind–body_problem |
| 30438.0 | 30439.0 | Totalitarian | Totalitarianism |
| 30626.0 | 34558.0 | Twentieth_Century | 20th_century |
| 30880.0 | 182444.0 | Thermoplasticity | Thermoplastic |
| 30970.0 | 923188.0 | The_play | Play |
| 31254.0 | 77634.0 | The_Junior_Woodchucks | Junior_Woodchucks |
| 31345.0 | 6896054.0 | Tabulating_Computing_Recording_Corporation | Computing-Tabulating-Recording_Company |
| 31380.0 | 49508.0 | The_Valkyrie | Die_Walküre |
| 31689.0 | 32022.0 | United_States/Economy | Economy_of_the_United_States |
| 31694.0 | 1.8618239e7 | United_States/States | U.S._state |
| 31763.0 | 4738483.0 | Delegates_of_American_Samoa_to_the_United_States_Congress | American_Samoa's_at-large_congressional_district |
| 31873.0 | 3434750.0 | USA | United_States |
| 31912.0 | 31641.0 | UseMod | UseModWiki |
| 31951.0 | 5741224.0 | Alternative_words_for_American | Demonyms_for_the_United_States |
| 31987.0 | 2.343106e7 | UCS-16 | Universal_Coded_Character_Set |
| 32102.0 | 31737.0 | U.S._Supreme_Court | Supreme_Court_of_the_United_States |
| 32109.0 | 54412.0 | Unicycling | Unicycle |
| 32246.0 | 1.7349325e7 | US_Marines | United_States_Marine_Corps |
| 32309.0 | 957.0 | Umbelliferae | Apiaceae |
| 32670.0 | 32669.0 | Vodun | West_African_Vodun |
| 32672.0 | 28736.0 | Velocity_of_light | Speed_of_light |
| 32860.0 | 4764461.0 | WorldWarOne | World_War_I |
| 32866.0 | 32908.0 | WarsaW | Warsaw |
| 32871.0 | 5042765.0 | WhatIsGod | God |
| 33141.0 | 33139.0 | World_wide_web | World_Wide_Web |
| 33197.0 | 33189.0 | Military_of_Wake_Island | Wake_Island |
| 33200.0 | 33199.0 | History_of_Wallis_and_Futuna | Wallis_and_Futuna |
| 33437.0 | 2.0541773e7 | Wind_generator | Wind_turbine |
| 33484.0 | 6669354.0 | Worms/Full_Wormage | Worms_(1995_video_game) |
| 34029.0 | 6669354.0 | Worms_computer_games/Roper | Worms_(1995_video_game) |
| 34509.0 | 9810476.0 | Zombie_(folklore) | Zombie |
| 35536.0 | 11378.0 | 1_Corinthians | First_Epistle_to_the_Corinthians |
| 35554.0 | 202611.0 | 3100_BC | 31st_century_BC |
| 35571.0 | 42682.0 | 1674_BC | 1670s_BC |
| 35626.0 | 30964.0 | 3_John | Third_Epistle_of_John |
| 35947.0 | 203673.0 | 1_E+10_m² | Orders_of_magnitude_(area) |
| 35950.0 | 203673.0 | 1_E+12_m² | Orders_of_magnitude_(area) |
| 35951.0 | 203673.0 | 1_E+7_m² | Orders_of_magnitude_(area) |
| 35982.0 | 203433.0 | 1_metre | Orders_of_magnitude_(length) |
| 35988.0 | 203433.0 | 1e5_m | Orders_of_magnitude_(length) |
| 36074.0 | 203433.0 | 1_micrometre | Orders_of_magnitude_(length) |
| 36100.0 | 203451.0 | 1_E-43_s | Orders_of_magnitude_(time) |
| 36106.0 | 203451.0 | 1_E38_s | Orders_of_magnitude_(time) |
| 36108.0 | 203451.0 | 1_E14_s | Orders_of_magnitude_(time) |
| 36143.0 | 26873.0 | 1_E7_s | Second |
| 36154.0 | 36156.0 | 1_E-4_s | Microsecond |
| 36155.0 | 36156.0 | 1_E-5_s | Microsecond |
| 36222.0 | 4940.0 | List_of_20th_century_brass_instrumentalists | Brass_instrument |
| 36580.0 | 104909.0 | Dauphin_Island | Dauphin_Island,_Alabama |
| 36706.0 | 8718425.0 | Circumsission | Circumcision |
| 36709.0 | 18271.0 | Lamberghini | Lamborghini |
| 36846.0 | 36845.0 | Jean_Henri_Dunant | Henry_Dunant |
| 36966.0 | 30983.0 | Testerone | Testosterone |
| 37006.0 | 2667451.0 | Sorcerers_apprentice_mode | Sorcerer's_Apprentice_Syndrome |
| 37116.0 | 31898.0 | UNFCCC | United_Nations_Framework_Convention_on_Climate_Change |
| 37195.0 | 71949.0 | Tok_Pisin_language | Tok_Pisin |
| 37210.0 | 3679017.0 | Shichi_Narabe | Domino_(card_game) |
| 37215.0 | 76029.0 | Duckburg | Donald_Duck_universe |
| 37251.0 | 37287.0 | Scooby_Doo | Scooby-Doo |
| 37343.0 | 3.7980916e7 | Monarchist | Monarchism |
| 37372.0 | 220872.0 | Diez_y_Seis_de_Septiembre | Cry_of_Dolores |
| 37415.0 | 34341.0 | Years | Year |
| 37482.0 | 64083.0 | Junkfood | Junk_food |
| 37497.0 | 37496.0 | Englishman's_knot | Fisherman's_knot |
| 37498.0 | 37496.0 | Waterman's_knot | Fisherman's_knot |
| 37679.0 | 26847.0 | Socialist | Socialism |
| 37705.0 | 2.1566765e7 | South_Asian_History | South_Asia |
| 37768.0 | 37767.0 | Badge_collecting | Patch_collecting |
| 37788.0 | 1.8932365e7 | Bono_Act | Copyright_Term_Extension_Act |
| 37790.0 | 1.8932365e7 | CTEA | Copyright_Term_Extension_Act |
| 37818.0 | 5314.0 | Charlimagne | Charlemagne |
| 37820.0 | 5314.0 | Charlamaine | Charlemagne |
| 38004.0 | 32817.0 | Vladimir_V._Putin | Vladimir_Putin |
| 38111.0 | 18934.0 | Prophet_Muhammad | Muhammad |
| 38146.0 | 38145.0 | LANL | Los_Alamos_National_Laboratory |
| 38159.0 | 4087869.0 | Radlab | Rad_Lab |
| 38220.0 | 38214.0 | The_Illuminatus_Trilogy | The_Illuminatus!_Trilogy |
| 38395.0 | 30359.0 | Tiber_river | Tiber |
| 38451.0 | 39378.0 | Distances | Distance |
| 38456.0 | 23195.0 | Crude_oil | Petroleum |
| 38544.0 | 32037.0 | Ursula_LeGuin | Ursula_K._Le_Guin |
| 38562.0 | 2.8469166e7 | H_Bar | H-bar |
| 38621.0 | 663861.0 | Private_IP_address | Private_network |
| 38704.0 | 11457.0 | Beato_Angelico | Fra_Angelico |
| 38758.0 | 82898.0 | Dolby_AC-3 | Dolby_Digital |
| 38806.0 | 38826.0 | Wenceslas_IV_the_Drunkard | Wenceslaus_IV_of_Bohemia |
| 38850.0 | 6099.0 | Carboxyl_group | Carboxylic_acid |
| 38928.0 | 248189.0 | Gaia_Hypothesis | Gaia_hypothesis |
| 38946.0 | 540154.0 | Banana,_Congo | Banana,_Democratic_Republic_of_the_Congo |
| 38991.0 | 203433.0 | 1e25_m | Orders_of_magnitude_(length) |
| 39016.0 | 4477.0 | Beach_Boys | The_Beach_Boys |
| 39037.0 | 43125.0 | Dowding | Hugh_Dowding |
| 39067.0 | 231495.0 | Coherent | Coherence |
| 39121.0 | 67762.0 | Holy_Innocents | Massacre_of_the_Innocents |
| 39153.0 | 217373.0 | Miljopartiet | Green_Party_(Sweden) |
| 39161.0 | 21289.0 | Nautical_miles | Nautical_mile |
| 39167.0 | 9843028.0 | Arms_(disambiguation) | Arms |
| 39259.0 | 5.6538779e7 | Henry_Mustin | Henry_C._Mustin |
| 39433.0 | 39432.0 | Stephen_A._Cook | Stephen_Cook |
| 39513.0 | 1.4944095e7 | 1345_(summary) | 1345 |
| 39671.0 | 39669.0 | Dengue_hemorrhagic_fever | Dengue_fever |
| 39716.0 | 39715.0 | Fertile_crescent | Fertile_Crescent |
| 39788.0 | 27931.0 | Pretty_Soldier_Sailor_Moon | Sailor_Moon |
| 39827.0 | 39825.0 | Project_Matterhorn | Princeton_Plasma_Physics_Laboratory |
| 39867.0 | 2756109.0 | Petrus_peregrinus | Petrus_Peregrinus_de_Maricourt |
| 40391.0 | 9707.0 | Pauling_scale | Electronegativity |
| 40496.0 | 60970.0 | Londons | London_(disambiguation) |
| 40585.0 | 33291.0 | WYSIAYG | WYSIWYG |
| 40736.0 | 41586.0 | Attenuation_constant | Propagation_constant |
| 40739.0 | 4011838.0 | Audible_ringing_tone | Ringing_tone |
| 40824.0 | 6968491.0 | Busy_hour | Busy-hour_call_attempts |
| 40889.0 | 7143.0 | Code-division | Code-division_multiple_access |
| 40945.0 | 5258912.0 | Conductive_coupling | Direct_coupling |
| 41006.0 | 234654.0 | Decollimation | Collimated_beam |
| 41011.0 | 41296.0 | Dejitterizer | Jitter |
| 41065.0 | 4254345.0 | Doubly_clad_fiber | Double-clad_fiber |
| 41114.0 | 3055674.0 | Equilibrium_length | Equilibrium_mode_distribution |
| 41141.0 | 271708.0 | Far-field_region | Near_and_far_field |
| 41399.0 | 25767.0 | Near_real-time | Real-time_computing |
| 41575.0 | 41107.0 | Pre-emphasis_network | Emphasis_(telecommunications) |
| 41647.0 | 41176.0 | Reframing_time | Frame_synchronization |
| 41704.0 | 202094.0 | Signal_processing_gain | Process_gain |
| 41738.0 | 1.8675102e7 | Standard_test_tone | Reference_tone |
| 41751.0 | 604831.0 | Store-and-forward_switching_center | Store_and_forward |
| 41768.0 | 28738.0 | Synchronizing | Synchronization |
| 41772.0 | 573528.0 | System_lifecycle | Systems_development_life_cycle |
| 41780.0 | 15476.0 | TCP/IP_Suite | Internet_protocol_suite |
| 41788.0 | 182745.0 | Thermal_noise | Johnson–Nyquist_noise |
| 41814.0 | 3.6254613e7 | Transmit_flow_control | Flow_control |
| 41838.0 | 2103451.0 | UPT_environment | Universal_Personal_Telecommunications |
| 41911.0 | 1177329.0 | Second_market | Secondary_market |
| 42033.0 | 26743.0 | Freudian | Sigmund_Freud |
| 42102.0 | 4764461.0 | 1st_World_War | World_War_I |
| 42119.0 | 42120.0 | Ras_Tafari | Haile_Selassie |
| 42129.0 | 7490861.0 | Tape_storage | Magnetic-tape_data_storage |
| 42381.0 | 42380.0 | Pennywhistle | Tin_whistle |
| 42430.0 | 1920222.0 | Electric_fencing | Electric_fence |
| 42533.0 | 1.8934701e7 | History_of_Bouvet_Island | Bouvet_Island |
| 42589.0 | 1.0518745e7 | Capet-Anjou | Capetian_House_of_Anjou |
| 42666.0 | 1.3108745e7 | Zerg | Races_of_StarCraft |
| 42733.0 | 53160.0 | Q_ship | Q-ship |
| 42824.0 | 2251.0 | Accusative | Accusative_case |
| 42988.0 | 2.3141006e7 | Orcs | Orc |
| 43072.0 | 4750452.0 | Umlauts | Umlaut |
| 43212.0 | 378598.0 | Urochordata | Tunicate |
| 43239.0 | 1.1604567e7 | 2001_U.S._Attack_on_the_Taliban/Timeline_January_2002 | 2002_in_Afghanistan |
| 43286.0 | 43284.0 | Java_RMI | Java_remote_method_invocation |
| 43302.0 | 287939.0 | Rogue-o-matic | Rog-O-Matic |
| 43419.0 | 44828.0 | Roman_hills | Seven_hills_of_Rome |
| 43458.0 | 491301.0 | Jin_dynasty | Jin |
| 43588.0 | 43589.0 | Fluorspar | Fluorite |
| 43891.0 | 203875.0 | 1_E-14_kg | Orders_of_magnitude_(mass) |
| 43943.0 | 43942.0 | Petri-dish | Petri_dish |
| 43953.0 | 2.7104735e7 | Speed_trap | Speed_limit_enforcement |
| 44111.0 | 1.0743994e7 | Foley_artist | Foley_(filmmaking) |
| 44123.0 | 42975.0 | Hubble_Constant | Hubble's_law |
| 44141.0 | 6.0160417e7 | Medieval_Climate_Optimum | Medieval_Warm_Period |
| 44407.0 | 44406.0 | Zarathushtra | Zoroaster |
| 44522.0 | 58439.0 | Transformational-Generative_Grammar | Transformational_grammar |
| 44595.0 | 2.1504235e7 | Actors_and_actresses | Actor |
| 44913.0 | 8504.0 | Dublin,_Ireland | Dublin |
| 44964.0 | 2815865.0 | Thwaites_Ice_Tongue | Thwaites_Glacier |
| 45011.0 | 2.1347057e7 | UNIX-like | Unix-like |
| 45054.0 | 18081.0 | Liverpudlian | Liverpool |
| 45111.0 | 42686.0 | 1606_BC | 1600s_BC_(decade) |
| 45151.0 | 144144.0 | Curly_brace_family | List_of_programming_languages_by_type |
| 45215.0 | 26791.0 | Satirical | Satire |
| 45398.0 | 3157936.0 | Australopethicines | Australopithecine |
| 45615.0 | 247725.0 | Augustus_III | Augustus_III_of_Poland |
| 45731.0 | 18947.0 | Meters | Metre |
| 45781.0 | 37803.0 | Cubist | Cubism |
| 46011.0 | 6784.0 | Citizen | Citizenship |
| 46166.0 | 38404.0 | Classless_routing | Classless_Inter-Domain_Routing |
| 46420.0 | 2.2228064e7 | Parkinson's_Disease | Parkinson's_disease |
| 46448.0 | 870329.0 | War_and_Peace_in_Russia,_1796-1825 | History_of_Russia_(1796–1855) |
| 46491.0 | 570856.0 | Hippocratic_corpus | Hippocratic_Corpus |
| 46506.0 | 849.0 | Heavier_than_air_flight | Aircraft |
| 46546.0 | 468436.0 | Public_Switched_Telephone_network | Public_switched_telephone_network |
| 46579.0 | 22468.0 | Usama_bin_laden | Osama_bin_Laden |
| 46586.0 | 22468.0 | Usama_Binladin | Osama_bin_Laden |
| 46685.0 | 870354.0 | Russian_Foreign_Affairs_after_the_Crimean_War | History_of_Russia_(1855–1892) |
| 46708.0 | 850127.0 | List_of_Senators_and_Representatives_of_Ohio | United_States_congressional_delegations_from_Ohio |
| 46739.0 | 46704.0 | CBTPA | Consumer_Broadband_and_Digital_Television_Promotion_Act |
| 46781.0 | 2776501.0 | Optical_astronomy | Visible-light_astronomy |
| 46801.0 | 46795.0 | Mono_lake | Mono_Lake |
| 46887.0 | 46884.0 | Japanese-American_relocation | Internment_of_Japanese_Americans |
| 46952.0 | 31975.0 | US_State_Department | United_States_Department_of_State |
| 46974.0 | 1.9344515e7 | Guardian_newspaper | The_Guardian |
| 47097.0 | 2376155.0 | Unknown_DJ | The_Unknown_DJ |
| 47116.0 | 16890.0 | NuqneH | Klingon_language |
| 47231.0 | 2396933.0 | Lares_(Roman_deities) | Lares |
| 47238.0 | 47235.0 | Psyche_(asteroid) | 16_Psyche |
| 47250.0 | 1.9003265e7 | Planet_Neptune | Neptune |
| 47252.0 | 44469.0 | Planet_Pluto | Pluto |
| 47268.0 | 3007285.0 | Navaho | Navajo |
| 47283.0 | 910926.0 | Topological_subspace | Subspace_topology |
| 47302.0 | 158974.0 | Stock_brokers | Stockbroker |
| 47457.0 | 31990.0 | Ultraviolet_energy | Ultraviolet |
| 47573.0 | 53782.0 | Mackinaw_trout | Lake_trout |
| 47597.0 | 3338.0 | Bronx_County,_New_York | The_Bronx |
| 47655.0 | 45207.0 | Satellite_communications | Communications_satellite |
| 47664.0 | 102671.0 | Dassault | Dassault_Group |
| 47666.0 | 200128.0 | BAe_Systems | BAE_Systems |
| 47753.0 | 47752.0 | Domesday_book | Domesday_Book |
| 47983.0 | 12448.0 | Ganges_river | Ganges |
| 47987.0 | 155534.0 | ARP | Arp |
| 47989.0 | 12293.0 | Graphical_Computer | Graphical_user_interface |
| 48174.0 | 30731.0 | Argument_from_design | Teleological_argument |
| 48206.0 | 233403.0 | Medieval_siege_weapon | Siege_engine |
| 48254.0 | 6172.0 | Cantor_dust | Cantor_set |
| 48599.0 | 554469.0 | Rock_strata | Stratum |
| 48659.0 | 1.2800642e7 | Skunk_Weed | Skunk_weed |
| 48871.0 | 48863.0 | Freshwater_sunfish | Centrarchidae |
| 48942.0 | 2.3994165e7 | Retrograde_orbit | Retrograde_and_prograde_motion |
| 49094.0 | 133295.0 | Tactical_Shooter | Tactical_shooter |
| 49101.0 | 34199.0 | Chinese_chess | Xiangqi |
| 49182.0 | 26514.0 | Roald_Hoffman | Roald_Hoffmann |
| 49267.0 | 3942.0 | Bijective | Bijection |
| 49429.0 | 330206.0 | Differentiable | Differentiable_function |
| 49442.0 | 866991.0 | Grand_conjunction | Great_conjunction |
| 49712.0 | 41551.0 | Quadrature_phase-shift_keying | Phase-shift_keying |
| 49713.0 | 29048.0 | Single-sideband_emission | Single-sideband_modulation |
| 49807.0 | 1242956.0 | Gross_national_product_(finance) | Gross_national_income |
| 49809.0 | 36218.0 | 2010_-_Odyssey_Two | 2010:_Odyssey_Two |
| 49869.0 | 4.0124159e7 | Umayad_dynasty | Umayyad_dynasty |
| 49963.0 | 50637.0 | Giant_redwood | Sequoiadendron_giganteum |
| 50155.0 | 4.1249202e7 | Italian_Red_Brigade | Red_Brigades |
| 50320.0 | 1.5092842e7 | Credit_money | Credit_theory_of_money |
| 50343.0 | 3.0865437e7 | Ranching | Ranch |
| 50359.0 | 2.5454239e7 | Masculinism | Masculism |
| 50417.0 | 15532.0 | Integral_calculus | Integral |
| 50483.0 | 70117.0 | Flood_plain | Floodplain |
| 50546.0 | 2.7310655e7 | Card_Captor_Sakura | Cardcaptor_Sakura |
| 50792.0 | 50795.0 | TIE_Advanced | TIE_fighter |
| 50954.0 | 26428.0 | Rosetta_stone | Rosetta_Stone |
| 51071.0 | 101336.0 | High-temperature_superconductor | High-temperature_superconductivity |
| 51214.0 | 188773.0 | Offroad_cycling | Mountain_biking |
| 51321.0 | 37699.0 | East_Asian_history | History_of_East_Asia |
| 51373.0 | 51563.0 | The_Luzhin_Defense | The_Luzhin_Defence |
| 51415.0 | 41997.0 | Twin_prime_conjecture | Twin_prime |
| 51501.0 | 1.3915586e7 | Rolling_barrage | Barrage_(artillery) |
| 51536.0 | 7016168.0 | IBM_Token_ring | Token_Ring |
| 51571.0 | 50347.0 | Multivariate_gaussian_distribution | Multivariate_normal_distribution |
| 51750.0 | 51758.0 | Terceet | Tercet |
| 51820.0 | 51822.0 | Allegany_River | Allegheny_River |
| 51979.0 | 34276.0 | October_war | Yom_Kippur_War |
| 51994.0 | 31627.0 | Dorpat | Tartu |
| 52195.0 | 49710.0 | 2120s_BC | 22nd_century_BC |
| 52218.0 | 3.1195579e7 | TGZ_(disambiguation) | TGZ |
| 52297.0 | 9736652.0 | Temporal_masking | Auditory_masking |
| 52559.0 | 33833.0 | W_Quine | Willard_Van_Orman_Quine |
| 52573.0 | 19738.0 | Metrisable_space | Metrizable_space |
| 52643.0 | 52642.0 | Van_de_Graff_generator | Van_de_Graaff_generator |
| 52670.0 | 47646.0 | Hippy | Hippie |
| 52697.0 | 7530.0 | Cro-Hook | Cro-hook |
| 52712.0 | 52711.0 | Leonardo_di_Caprio | Leonardo_DiCaprio |
| 52829.0 | 8769.0 | Dutch_west_india_company | Dutch_West_India_Company |
| 52984.0 | 52983.0 | Hot_sand_frying | Hot_salt_frying |
| 53158.0 | 3609782.0 | Naval_warfare_tactic | Naval_tactics |
| 53168.0 | 29475.0 | S-3_viking | Lockheed_S-3_Viking |
| 53172.0 | 188641.0 | Green_movement | Green_politics |
| 53381.0 | 3684625.0 | Periods_of_architecture | History_of_architecture |
| 53449.0 | 495383.0 | Probabalistic_algorithm | Randomized_algorithm |
| 53845.0 | 2936.0 | Alaskan_Panhandle | Southeast_Alaska |
| 53871.0 | 53869.0 | Wa-Tho-Huck | Jim_Thorpe |
| 53872.0 | 53869.0 | Bright_Path | Jim_Thorpe |
| 53957.0 | 34625.0 | Fourteenth_Century | 14th_century |
| 53963.0 | 1.8938115e7 | Twenty-first_Century | 21st_century |
| 53969.0 | 16227.0 | Jerome_David_Kern | Jerome_Kern |
| 53976.0 | 3010.0 | Alan_Lerner | Alan_Jay_Lerner |
| 54046.0 | 167109.0 | Bramble_fruit | Bramble |
| 54264.0 | 4372722.0 | Peoples_Republic_of_China/History | History_of_the_People's_Republic_of_China |
| 54272.0 | 37770.0 | Sevilla | Seville |
| 54282.0 | 34002.0 | William_ODwyer | William_O'Dwyer |
| 54321.0 | 16774.0 | Karl_Donitz | Karl_Dönitz |
| 54624.0 | 20270.0 | MC68000 | Motorola_68000 |
| 54642.0 | 8166749.0 | Gas-electric_hybrid_engine | Hybrid_electric_vehicle |
| 54823.0 | 27085.0 | Star_Trek/Chakotay | Chakotay |
| 54844.0 | 27075.0 | Star_Trek/ENT_Episode_List | List_of_Star_Trek:_Enterprise_episodes |
| 54880.0 | 4.4946818e7 | Wu_Hu_barbarians | Wu_Hu |
| 54984.0 | 54980.0 | Adirondack_mountain | Adirondack_Mountains |
| 55035.0 | 763392.0 | Battle_of_red_Cliffs | Battle_of_Red_Cliffs |
| 55131.0 | 18390.0 | Lavrentii_Beria | Lavrentiy_Beria |
| 55197.0 | 55196.0 | Adula_Alps | Lepontine_Alps |
| 55198.0 | 1185102.0 | The_Alps_of_Bavaria,_the_Vorarlberg,_and_Salzburg | Northern_Limestone_Alps |
| 55219.0 | 2.1591425e7 | Modified_Newtonian_Dynamics | Modified_Newtonian_dynamics |
| 55237.0 | 55236.0 | Compton_effect | Compton_scattering |
| 55272.0 | 9421.0 | Helsingor | Helsingør |
| 55398.0 | 1010280.0 | Disk_file_systems | File_system |
| 55547.0 | 55546.0 | Hawley-Smoot_Tariff | Smoot–Hawley_Tariff_Act |
| 55550.0 | 55556.0 | Humphrey_Hawkins_Full_Employment_Act | Humphrey–Hawkins_Full_Employment_Act |
| 55647.0 | 54481.0 | Apron_shoulder_straps | Apron |
| 55701.0 | 55546.0 | Smoot-Hawley_tariff | Smoot–Hawley_Tariff_Act |
| 55704.0 | 55706.0 | Dick_Whittington | Richard_Whittington |
| 55748.0 | 1615034.0 | Underwood_Tariff | Revenue_Act_of_1913 |
| 55863.0 | 55856.0 | Linz,_Austria | Linz |
| 55879.0 | 19058.0 | Munich,_Germany | Munich |
| 56022.0 | 5702.0 | The_Chunnel | Channel_Tunnel |
| 56204.0 | 1571082.0 | Cat_bus | Catbus |
| 56211.0 | 1.9283913e7 | Poverty_line_in_the_United_States | Poverty_in_the_United_States |
| 56281.0 | 1.8950885e7 | BBC_Microcomputer | BBC_Micro |
| 56297.0 | 21287.0 | Nuremberg,_Germany | Nuremberg |
| 56337.0 | 53949.0 | Colobus_monkey | Black-and-white_colobus |
| 56351.0 | 626718.0 | Yoga_Sutras | Yoga_Sutras_of_Patanjali |
| 56387.0 | 54943.0 | Cultural_relativsm | Cultural_relativism |
| 56468.0 | 17867.0 | London,_United_Kingdom | London |
| 56490.0 | 1021884.0 | Örnsköldsvik,_Sweden | Örnsköldsvik |
| 56493.0 | 1.8950508e7 | Aalesund | Ålesund |
| 56496.0 | 56495.0 | Bergen,_Belgium | Mons |
| 56640.0 | 27414.0 | Sri_Lanka/Government | Politics_of_Sri_Lanka |
| 56674.0 | 30112.0 | Tajikistan/Government | Politics_of_Tajikistan |
| 56687.0 | 56680.0 | Harare,_Zimbabwe | Harare |
| 56741.0 | 33225.0 | Western_Sahara/Economy | Economy_of_Western_Sahara |
| 56768.0 | 33189.0 | Wake_Island/People | Wake_Island |
| 56769.0 | 33189.0 | Wake_Island/Geography | Wake_Island |
| 56781.0 | 32135.0 | U.S._Virgin_Islands/Military | United_States_Virgin_Islands |
| 56840.0 | 56622.0 | Basseterre,_Saint_Kitts_and_Nevis | Basseterre |
| 57039.0 | 57040.0 | Malé,_Maldives | Malé |
| 57058.0 | 57061.0 | Niamey,_Niger | Niamey |
| 57100.0 | 19242.0 | Moldova/Geography | Geography_of_Moldova |
| 57126.0 | 21344.0 | New_Caledonia/Geography | Geography_of_New_Caledonia |
| 57133.0 | 19283.0 | Montserrat/Geography | Geography_of_Montserrat |
| 57178.0 | 19356.0 | Psychiatric_disorder | Mental_disorder |
| 57202.0 | 34743.0 | Third_Century | 3rd_century |
| 57271.0 | 3682.0 | Burkina_Faso/Transportation | Transport_in_Burkina_Faso |
| 57278.0 | 2589714.0 | Milky_Way_galaxy | Milky_Way |
| 57298.0 | 30143.0 | Togo/Economy | Economy_of_Togo |
| 57306.0 | 69593.0 | Gambia/Economy | Economy_of_the_Gambia |
| 57436.0 | 14676.0 | Ireland/People | Demographics_of_the_Republic_of_Ireland |
| 57464.0 | 11812.0 | F-35 | Lockheed_Martin_F-35_Lightning_II |
| 57566.0 | 37368.0 | RQ-1_Predator_UAV | General_Atomics_MQ-1_Predator |
| 57693.0 | 5750.0 | Cognitive_behaviour_therapy | Cognitive_behavioral_therapy |
| 57752.0 | 57762.0 | Psychiatric_drug | Psychiatric_medication |
| 57754.0 | 4531.0 | Bi-polar_disorder | Bipolar_disorder |
| 57786.0 | 1721361.0 | Stevedore's_knot | Stevedore_knot |
| 57919.0 | 2.8030968e7 | Q_Gospel | Q_source |
| 57984.0 | 681745.0 | Hawaiian_people | Native_Hawaiians |
| 58054.0 | 4143721.0 | Kentucky_counties | List_of_counties_in_Kentucky |
| 58082.0 | 10669.0 | Famous_football_player | Football_player |
| 58125.0 | 1.6285821e7 | United_Kingdom/Basic_Topics | Outline_of_the_United_Kingdom |
| 58196.0 | 51550.0 | Zip_code | ZIP_Code |
| 58431.0 | 1.8024177e7 | Retro-choir | Retroquire |
| 58513.0 | 2.3139208e7 | Middle-Earth | Middle-earth |
| 58718.0 | 1.3336661e7 | Presidant | President |
| 58850.0 | 22148.0 | Niccolo_Tartaglia | Niccolò_Fontana_Tartaglia |
| 58853.0 | 170104.0 | Juniperus | Juniper |
| 58876.0 | 727401.0 | Don_Manuel_Ruiz_Zorilla | Manuel_Ruiz_Zorrilla |
| 58905.0 | 203548.0 | Frailing | Clawhammer |
| 58914.0 | 26347.0 | Soviet_submarine_K-141 | Russian_submarine_Kursk_(K-141) |
| 58985.0 | 1528346.0 | Totally_bounded | Totally_bounded_space |
| 59081.0 | 59076.0 | U-553 | German_submarine_U-553 |
| 59086.0 | 1.0454705e7 | U-155 | German_submarine_U-155 |
| 59142.0 | 59352.0 | Solidus_(punctuation) | Slash_(punctuation) |
| 59178.0 | 29452.0 | Staatsicherheit | Stasi |
| 59345.0 | 2664203.0 | Period_(rhetoric) | Periodic_sentence |
| 59460.0 | 59465.0 | Lord_Jeffrey_Amherst | Jeffery_Amherst,_1st_Baron_Amherst |
| 59487.0 | 53254.0 | Nuragici_people | History_of_Sardinia |
| 59496.0 | 59483.0 | Carl_Scheele | Carl_Wilhelm_Scheele |
| 59522.0 | 2.7716891e7 | Hom-set | Morphism |
| 59547.0 | 59405.0 | Coterminal | Initial_and_terminal_objects |
| 59555.0 | 60635.0 | Oral_glucose_tolerance_test | Glucose_tolerance_test |
| 59562.0 | 320733.0 | Impedance_mismatch | Impedance_matching |
| 59647.0 | 1.7322723e7 | Pornographic_actress | Pornographic_film_actor |
| 59663.0 | 63578.0 | Terrorist_group | List_of_designated_terrorist_groups |
| 59667.0 | 10013.0 | Evidence_based_medicine | Evidence-based_medicine |
| 59724.0 | 1.8963787e7 | Cation | Ion |
| 59754.0 | 59748.0 | The_Bored_of_the_Rings | Bored_of_the_Rings |
| 59838.0 | 83516.0 | Robert_Heinlein/Universe | Orphans_of_the_Sky |
| 59839.0 | 83516.0 | Universe_(short_story_by_Robert_Heinlein) | Orphans_of_the_Sky |
| 59878.0 | 59877.0 | Molar_gas_constant | Gas_constant |
| 59910.0 | 50744.0 | Star_Wars,_Episode_VI_-_Return_of_the_Jedi | Return_of_the_Jedi |
| 60055.0 | 60056.0 | Mission_Santa_Bárbara | Mission_Santa_Barbara |
| 60081.0 | 60082.0 | Mission_San_Rafael_Arcangel | Mission_San_Rafael_Arcángel |
| 60243.0 | 411523.0 | Triton_VX | List_of_Intel_chipsets |
| 60276.0 | 348300.0 | Personal_video_recorder | Digital_video_recorder |
| 60277.0 | 251485.0 | Battle_of_the_Ironclads | Battle_of_Hampton_Roads |
| 60382.0 | 1.1519542e7 | A_Sharp | A-sharp |
| 60474.0 | 78261.0 | Asynchronous_Balanced_Mode | High-Level_Data_Link_Control |
| 60623.0 | 20155.0 | Marcus_Aurelius_Antoninus | Marcus_Aurelius |
| 60683.0 | 63392.0 | Reality_enforcement | Consensus_reality |
| 60718.0 | 203875.0 | 1_E-21_kg | Orders_of_magnitude_(mass) |
| 60769.0 | 60766.0 | Tractricoid | Pseudosphere |
| 60853.0 | 45063.0 | Abelian_categories | Abelian_category |
| 60893.0 | 3.6026428e7 | Realist | Realism |
| 61242.0 | 34740.0 | Eighth_century | 8th_century |
| 61250.0 | 34644.0 | Twelveth_century | 12th_century |
| 61330.0 | 1842.0 | Augustin_Cauchy | Augustin-Louis_Cauchy |
| 61449.0 | 21383.0 | Federal_Republic_of_Nigeria | Nigeria |
| 61453.0 | 27288.0 | Republic_of_Seychelles | Seychelles |
| 61477.0 | 61476.0 | Convergence_radius | Radius_of_convergence |
| 62015.0 | 9072.0 | Jacques_Louis_David | Jacques-Louis_David |
| 62150.0 | 2089569.0 | Mare's_tail | Marestail |
| 62434.0 | 18831.0 | Mathematical | Mathematics |
| 62478.0 | 37998.0 | Francois_Mitterand | François_Mitterrand |
| 62562.0 | 464082.0 | Theodebald | Theudebald |
| 62606.0 | 66789.0 | Alfonso_X | Alfonso_X_of_Castile |
| 62752.0 | 18538.0 | Lansing | Lansing,_Michigan |
| 62771.0 | 62743.0 | 1400_BC | 1400s_BC_(decade) |
| 62780.0 | 39248.0 | Colimit | Limit_(category_theory) |
| 62791.0 | 33265.0 | Winston_churchhill | Winston_Churchill |
| 62880.0 | 15221.0 | 80188 | Intel_80188 |
| 62989.0 | 2023036.0 | John_the_Divine | John_of_Patmos |
| 63066.0 | 13259.0 | Startsida | Home_page |
| 63111.0 | 9272073.0 | Stock_option | Option_(finance) |
| 63152.0 | 6322.0 | Conuropsis | Carolina_parakeet |
| 63220.0 | 63879.0 | IPO | Initial_public_offering |
| 63255.0 | 19496.0 | Mah_Jong | Mahjong |
| 63345.0 | 29294.0 | S/360 | IBM_System/360 |
| 63346.0 | 40642.0 | NeXTStep | NeXTSTEP |
| 63592.0 | 2110202.0 | Tsarina_Alexandra | Alexandra_Feodorovna |
| 63696.0 | 19042.0 | Metals | Metal |
| 63964.0 | 2068329.0 | Athelas | List_of_fictional_plants |
| 64021.0 | 64020.0 | Multiprocessor | Multiprocessing |
| 64121.0 | 64087.0 | Type_designer | Type_design |
| 64166.0 | 16710.0 | 10_kroner | Krone |
| 64170.0 | 16710.0 | 10_krones | Krone |
| 64230.0 | 42120.0 | Haile_Sellassie | Haile_Selassie |
| 64536.0 | 19222.0 | Mexico/History | History_of_Mexico |
| 64550.0 | 3610.0 | Bosnia_and_Herzegovina/Military | Armed_Forces_of_Bosnia_and_Herzegovina |
| 64551.0 | 3611.0 | Bosnia_and_Herzegovina/Transnational_issues | Foreign_relations_of_Bosnia_and_Herzegovina |
| 64555.0 | 1.8950915e7 | Belarus/Economy | Economy_of_Belarus |
| 64593.0 | 49401.0 | Meeting_hall | Hall |
| 64622.0 | 12736.0 | German_poets | List_of_German-language_poets |
| 64628.0 | 23517.0 | Polish_poets | List_of_Polish-language_poets |
| 64768.0 | 20003.0 | Three_tier_architecture | Multitier_architecture |
| 64798.0 | 35509.0 | 51_forth | 51-FORTH |
| 64800.0 | 35510.0 | 56_kbit/s | 56_kbit/s_line |
| 64822.0 | 292279.0 | Lunchlady_Doris | List_of_recurring_The_Simpsons_characters |
| 64838.0 | 20325.0 | 68060 | Motorola_68060 |
| 64842.0 | 20324.0 | 68LC040 | Motorola_68040 |
| 64859.0 | 292279.0 | Disco_Stu_(The_Simpsons) | List_of_recurring_The_Simpsons_characters |
| 64874.0 | 292279.0 | Doctor_Marvin_Monroe | List_of_recurring_The_Simpsons_characters |
| 64896.0 | 292279.0 | Dr._Julius_Hibbert | List_of_recurring_The_Simpsons_characters |
| 65055.0 | 8039.0 | Transnational_issues_of_Denmark | Foreign_relations_of_Denmark |
| 65110.0 | 19527.0 | Mao_Tse-Tung | Mao_Zedong |
| 65112.0 | 65113.0 | Lee_Ao | Li_Ao |
| 65168.0 | 2438208.0 | Little_Rascals | Our_Gang |
| 65251.0 | 704.0 | Angola/People | Demographics_of_Angola |
| 65296.0 | 237407.0 | Teleri | Sundering_of_the_Elves |
| 65300.0 | 237407.0 | Nandor_(Middle-earth) | Sundering_of_the_Elves |
| 65333.0 | 16697.0 | Kyrgyzstan/People | Demographics_of_Kyrgyzstan |
| 65353.0 | 19122.0 | Maldives/Economy | Economy_of_Maldives |
| 65445.0 | 381862.0 | Werewolf_novels | Werewolf_fiction |
| 65454.0 | 23190.0 | Playing_card/Cut | Cut_(cards) |
| 65587.0 | 63876.0 | History_of_the_United_States_of_America | History_of_the_United_States |
| 65618.0 | 65616.0 | British_comedian | List_of_British_comedians |
| 65657.0 | 12228.0 | Gurps | GURPS |
| 65764.0 | 37527.0 | Alfa-Romeo | Alfa_Romeo |
| 65766.0 | 30302.0 | Tardis | TARDIS |
| 65823.0 | 292279.0 | Snake_Jailbird | List_of_recurring_The_Simpsons_characters |
| 65824.0 | 2.4536543e7 | Eukaryotic | Eukaryote |
| 65895.0 | 1343597.0 | Energy_(electrical) | Electrical_energy |
| 65904.0 | 50591.0 | US_Postal_Service | United_States_Postal_Service |
| 65932.0 | 219042.0 | Electronic_power_supply | Power_supply |
| 66021.0 | 27281.0 | Senegal/People | Demographics_of_Senegal |
| 66026.0 | 27286.0 | Senegal/Military | Armed_Forces_of_Senegal |
| 66132.0 | 17835.0 | Luxembourg/Communications | Telecommunications_in_Luxembourg |
| 66138.0 | 65827.0 | Silicones | Silicone |
| 66155.0 | 379788.0 | Pummelo | Pomelo |
| 66166.0 | 241132.0 | LAMP | Lamp |
| 66226.0 | 66225.0 | Curtis_E._LeMay | Curtis_LeMay |
| 66246.0 | 43970.0 | Bomb_calorimeter | Calorimeter |
| 66280.0 | 43970.0 | Modulating_differential_scanning_calorimeter | Calorimeter |
| 66433.0 | 3.0206738e7 | Chronic_obstructive_lung_disease | Chronic_obstructive_pulmonary_disease |
| 66435.0 | 11749.0 | Famous_chess_players | List_of_chess_players |
| 66500.0 | 442294.0 | Psionic | Psionics |
| 66532.0 | 2.9983143e7 | Fern-allies | Fern_ally |
| 66590.0 | 3.6674345e7 | Computer_services | Information_technology |
| 66667.0 | 154450.0 | Samuel_Clemens | Mark_Twain |
| 66751.0 | 9379.0 | Eritrea/People | Demographics_of_Eritrea |
| 66755.0 | 9385.0 | Eritrea/Transnational_issues | Foreign_relations_of_Eritrea |
| 66760.0 | 23239.0 | Peoples_Republic_of_China/Government | Politics_of_China |
| 66766.0 | 23244.0 | Peoples_Republic_of_China/Transnational_issues | Foreign_relations_of_China |
| 66800.0 | 1324.0 | Antonio_Gaudi/Park_Guell | Park_Güell |
| 66826.0 | 21162.0 | Netherlands_Antilles/Military | Netherlands_Armed_Forces |
| 66828.0 | 21338.0 | Netherlands_Antilles/Communications | Telecommunications_in_Curaçao |
| 66830.0 | 21335.0 | Netherlands_Antilles/People | Demographics_of_the_Netherlands_Antilles |
| 67086.0 | 42005.0 | Software_collaborative_tool | Collaborative_software |
| 67089.0 | 46875.0 | Puff_paste | Puff_pastry |
| 67107.0 | 31858.0 | Uzbekistan/Economy | Economy_of_Uzbekistan |
| 67110.0 | 31862.0 | Uzbekistan/Transnational_issues | Foreign_relations_of_Uzbekistan |
| 67169.0 | 19876.0 | Motor_cycle | Motorcycle |
| 67223.0 | 268516.0 | Cost,_insurance_and_freight | Incoterms |
| 67277.0 | 67670.0 | Sweden/Government | Politics_of_Sweden |
| 67278.0 | 10703.0 | Faroe_Islands/Communications | Telecommunications_in_the_Faroe_Islands |
| 67294.0 | 67293.0 | Valery_Borzov | Valeriy_Borzov |
| 67361.0 | 367498.0 | Pseudo-fossils | Pseudofossil |
| 67439.0 | 3.6303581e7 | Family_film | Children's_film |
| 67532.0 | 1222540.0 | Federal_Government_of_Australia | Australian_Government |
| 67550.0 | 2.3661208e7 | Transnational_issues_of_Austria | Foreign_relations_of_Austria |
| 67552.0 | 67551.0 | Geography_of_Bahamas | Geography_of_the_Bahamas |
| 67569.0 | 23422.0 | Paraguay/Geography | Geography_of_Paraguay |
| 67574.0 | 23429.0 | Paraguay/Transnational_issues | Foreign_relations_of_Paraguay |
| 67595.0 | 1.895057e7 | Brazil/People | Demographics_of_Brazil |
| 67667.0 | 293288.0 | Tuskegee_Institute | Tuskegee_University |
| 67734.0 | 46663.0 | Simon_and_Garfunkel/Bookends | Bookends_(album) |
| 67833.0 | 68260.0 | Stock_Market_Crash_of_2002 | Stock_market_downturn_of_2002 |
| 67846.0 | 7397.0 | Color-blind | Color_blindness |
| 67942.0 | 67941.0 | Cassini_program | Cassini–Huygens |
| 68087.0 | 67965.0 | Ginkgoopsida | Ginkgoales |
| 68095.0 | 53058.0 | T3_space | Regular_space |
| 68098.0 | 48629.0 | T5_space | Normal_space |
| 68135.0 | 2.3535509e7 | N_SYNC | NSYNC |
| 68174.0 | 19183.0 | Mauritania/Government | Politics_of_Mauritania |
| 68218.0 | 19527.0 | Mao_Tsetung | Mao_Zedong |
| 68266.0 | 68206.0 | Central_Dogma | Central_dogma_of_molecular_biology |
| 68341.0 | 33767.0 | Corel_WordPerfect_Office | WordPerfect |
| 68708.0 | 30957.0 | Tuatha_Dé_Danaan | Tuatha_Dé_Danann |
| 68866.0 | 3.5795589e7 | Winefat | History_of_the_wine_press |
| 69165.0 | 1.5398943e7 | Tammuz_(mythology) | Dumuzid |
| 69333.0 | 24313.0 | Mythical_island | Phantom_island |
| 69395.0 | 1.7277937e7 | Quarries_(biblical) | Zedekiah's_Cave |
| 69481.0 | 69480.0 | VHF | Very_high_frequency |
| 69564.0 | 4458.0 | Prophecies_of_Habakkuk | Book_of_Habakkuk |
| 69601.0 | 2400868.0 | Tribes_of_Israel | Twelve_Tribes_of_Israel |
| 70005.0 | 28632.0 | Seventh-Day_Adventist | Seventh-day_Adventist_Church |
| 70252.0 | 3007720.0 | Maranon | Marañón |
| 70283.0 | 31353.0 | Hitch_Hiker's_Guide_to_the_Galaxy | The_Hitchhiker's_Guide_to_the_Galaxy |
| 70293.0 | 37398.0 | Disney_Corporation | The_Walt_Disney_Company |
| 70491.0 | 32388.0 | Victoria_BC | Victoria,_British_Columbia |
| 70573.0 | 591253.0 | Kirchhoffs_Current_Law | Kirchhoff's_circuit_laws |
| 70660.0 | 2300261.0 | Show_me_love | Show_Me_Love |
| 70719.0 | 8409.0 | List_of_notorious_Dictators | Dictator |
| 70895.0 | 437887.0 | Audio_editing | Audio_editing_software |
| 70901.0 | 4.0582739e7 | Department_of_Labor | Ministry_of_Labour |
| 70906.0 | 70904.0 | Department_of_the_Interior | United_States_Department_of_the_Interior |
| 70918.0 | 70919.0 | U.S._Department_of_Education | United_States_Department_of_Education |
| 71007.0 | 70959.0 | Maui_(island) | Maui |
| 71091.0 | 2670130.0 | Peter_Gandy_(author) | The_Jesus_Mysteries |
| 71151.0 | 30162.0 | Tonga/Government | Politics_of_Tonga |
| 71283.0 | 25929.0 | Regiomontan | Regiomontanus |
| 71528.0 | 71511.0 | Celtic_Metal | Celtic_metal |
| 71613.0 | 348917.0 | IBM_PC_AT | IBM_Personal_Computer/AT |
| 71852.0 | 1274.0 | Antarctica/Geography | Geography_of_Antarctica |
| 71854.0 | 27342.0 | Slovenia/Government | Politics_of_Slovenia |
| 71902.0 | 2.5739013e7 | Y2k | Year_2000_problem |
| 71905.0 | 47387.0 | William_III_of_Orange | William_III_of_England |
| 71995.0 | 63171.0 | Star_Wars/Yoda | Yoda |
| 72050.0 | 16653.0 | Kenya/History | History_of_Kenya |
| 72075.0 | 884135.0 | Creation_Spirituality | Matthew_Fox_(priest) |
| 72484.0 | 261472.0 | Alexandretta,_Syria | İskenderun |
| 72514.0 | 9335.0 | Ecuador/History | History_of_Ecuador |
| 72545.0 | 33703.0 | Sir_Walter_Raleigh | Walter_Raleigh |
| 72610.0 | 70243.0 | United_States_Commerce_Department | United_States_Department_of_Commerce |
| 72627.0 | 2.5754129e7 | Platonic_ideal | Theory_of_forms |
| 72642.0 | 23395.0 | Panama/Government | Politics_of_Panama |
| 72681.0 | 2.6378017e7 | Olympic_baseball_medalists | List_of_Olympic_medalists_in_baseball |
| 72912.0 | 203875.0 | 1e-31_kg | Orders_of_magnitude_(mass) |
| 72937.0 | 77548.0 | Goostave_Gander | Duck_family_(Disney) |
| 72938.0 | 203433.0 | 1e-6_m | Orders_of_magnitude_(length) |
| 72941.0 | 203875.0 | 1e-13_kg | Orders_of_magnitude_(mass) |
| 72981.0 | 203875.0 | 1e-1_kg | Orders_of_magnitude_(mass) |
| 72996.0 | 203875.0 | 1e0_kg | Orders_of_magnitude_(mass) |
| 73048.0 | 18030.0 | LR(0)_parser | LR_parser |
| 73052.0 | 3.7260549e7 | Moroland | Bangsamoro |
| 73053.0 | 73056.0 | LR(1)_parser | Canonical_LR_parser |
| 73091.0 | 185843.0 | End_of_the_world_(religion) | End_time |
| 73185.0 | 46539.0 | Non-government_organisation | Non-governmental_organization |
| 73305.0 | 9234237.0 | Csar | CSAR |
| 73320.0 | 9370.0 | Equatorial_Guinea/Government | Politics_of_Equatorial_Guinea |
| 73407.0 | 22216.0 | O_Brother,_Where_Art_Thou | O_Brother,_Where_Art_Thou? |
| 73470.0 | 36104.0 | 1e-9_s | Nanosecond |
| 73480.0 | 203875.0 | 1e3_kg | Orders_of_magnitude_(mass) |
| 73547.0 | 27443.0 | Svalbard/Geography | Geography_of_Svalbard |
| 73862.0 | 27463.0 | Switzerland/People | Demographics_of_Switzerland |
| 73878.0 | 31846.0 | Uruguay/People | Demographics_of_Uruguay |
| 74058.0 | 10763.0 | French_Guiana/People | Demographics_of_French_Guiana |
| 74073.0 | 27231.0 | Saint_Vincent_and_the_Grenadines/People | Demographics_of_Saint_Vincent_and_the_Grenadines |
| 74122.0 | 203875.0 | 1e9_kg | Orders_of_magnitude_(mass) |
| 74214.0 | 3144.0 | A_Dolls_House | A_Doll's_House |
| 74295.0 | 3613.0 | Botswana/Geography | Geography_of_Botswana |
| 74303.0 | 5429.0 | Cambodia/Geography | Geography_of_Cambodia |
| 74312.0 | 5480.0 | Central_African_Republic/Geography | Geography_of_the_Central_African_Republic |
| 74484.0 | 2829402.0 | I_Ching_hexagram_32 | List_of_hexagrams_of_the_I_Ching |
| 74528.0 | 2018532.0 | Ratface | List_of_Donald_Duck_universe_characters |
| 74556.0 | 58906.0 | Glands | Gland |
| 74757.0 | 12029.0 | Gabon/Geography | Geography_of_Gabon |
| 74814.0 | 65835.0 | The_Beatles/Please_Please_Me | Please_Please_Me |
| 74880.0 | 16699.0 | Kyrgyzstan/Economy | Economy_of_Kyrgyzstan |
| 74898.0 | 1.9283139e7 | Lithuania/Economy | Economy_of_Lithuania |
| 75070.0 | 70381.0 | The_Teheran_Conference | Tehran_Conference |
| 75341.0 | 10789.0 | Film_history/Poland | Cinema_of_Poland |
| 75617.0 | 578952.0 | .Net | .net_(disambiguation) |
| 75620.0 | 403357.0 | Absolute_path | Path_(computing) |
| 75699.0 | 75698.0 | Texas_hold'em | Texas_hold_'em |
| 75757.0 | 32706.0 | Vancouver,_British_Columbia,_Canada | Vancouver |
| 75842.0 | 47398.0 | Orchestrator | Orchestration |
| 75872.0 | 20414.0 | Maas_River | Meuse |
| 75962.0 | 361082.0 | Flags_of_the_world | Gallery_of_sovereign_state_flags |
| 76066.0 | 61338.0 | Addend | Addition |
| 76432.0 | 58095.0 | La_Pérouse | La_Perouse |
| 76493.0 | 30096.0 | Taiwan/Transnational_issues | Foreign_relations_of_Taiwan |
| 76530.0 | 8103499.0 | Government_of_Angola | Cabinet_of_Angola |
| 76636.0 | 2018532.0 | Chisel_McSue | List_of_Donald_Duck_universe_characters |
| 76662.0 | 3743660.0 | Botswana/Military | Botswana_Defence_Force |
| 76680.0 | 3683.0 | Burkina_Faso/Military | Burkina_Faso_Armed_Forces |
| 76689.0 | 3701.0 | Burundi/Military | National_Defence_Force_(Burundi) |
| 76739.0 | 5482.0 | Government_of_Central_African_Republic | Politics_of_the_Central_African_Republic |
| 76751.0 | 76723.0 | Toll_House_cookie | Chocolate_chip_cookie |
| 76756.0 | 6003.0 | Comoros/Government | Politics_of_the_Comoros |
| 76965.0 | 5851.0 | Czech_Republic/Transportation | Transport_in_the_Czech_Republic |
| 76974.0 | 12063.0 | Georgia/Communications | Telecommunications_in_Georgia_(country) |
| 76982.0 | 8044.0 | Djibouti/Government | Politics_of_Djibouti |
| 76983.0 | 8059.0 | Dominica/Transnational_issues | Foreign_relations_of_Dominica |
| 77011.0 | 9343.0 | Ecuador/Transnational_issues | Foreign_relations_of_Ecuador |
| 77028.0 | 9393.0 | Estonia/Transportation | Transport_in_Estonia |
| 77029.0 | 1.8917889e7 | Estonia/Military | Estonian_Defence_Forces |
| 77034.0 | 9373.0 | Equatorial_Guinea/Transportation | Transport_in_Equatorial_Guinea |
| 77064.0 | 19086.0 | Macedonia/Military | Army_of_North_Macedonia |
| 77088.0 | 11934.0 | Germany/Transnational_Issues | Foreign_relations_of_Germany |
| 77132.0 | 2.155468e7 | Movie_director | Film_director |
| 77210.0 | 12202.0 | Guyana/Transportation | Transport_in_Guyana |
| 77225.0 | 1.010083e7 | Acis | Acis_and_Galatea |
| 77422.0 | 2192581.0 | Pepin_III | Pepin_the_Short |
| 77471.0 | 77470.0 | Persa | Perse |
| 77479.0 | 7171338.0 | India/Military | Indian_Armed_Forces |
| 77503.0 | 5.0913538e7 | Government_of_Iran | Government_of_the_Islamic_Republic_of_Iran |
| 77530.0 | 15664.0 | Government_of_Jamaica | Politics_of_Jamaica |
| 77701.0 | 78332.0 | Ishtar | Inanna |
| 77706.0 | 60973.0 | List_of_places_and_things_named_Oxford | Oxford_(disambiguation) |
| 77778.0 | 3.9686851e7 | Sassanians | Sasanian_dynasty |
| 77790.0 | 36937.0 | Network_television | Television_broadcasting |
| 77912.0 | 160634.0 | Gildor_Inglorion | Finrod_Felagund |
| 77913.0 | 8203.0 | Deutchland_Uber_Alles | Deutschlandlied |
| 77939.0 | 33653.0 | Wheel_of_the_year | Wheel_of_the_Year |
| 77943.0 | 221226.0 | Midsummer_(neopagan) | Midsummer |
| 78046.0 | 16692.0 | Kuwait/Military | Kuwait_Military_Forces |
| 78060.0 | 77747.0 | Philip_K._Dick/We_Can_Remember_It_For_You_Wholesale | We_Can_Remember_It_for_You_Wholesale |
| 78064.0 | 23282.0 | Philip_K._Dick/Ubik | Ubik |
| 78105.0 | 2.7619007e7 | Ha-Mossad_le-Modiin_ule-Tafkidim_Meyuhadim | Mossad |
| 78113.0 | 17790.0 | Lesotho/Transnational_issues | Foreign_relations_of_Lesotho |
| 78120.0 | 17800.0 | Liberia/Transnational_issues | Foreign_relations_of_Liberia |
| 78272.0 | 73525.0 | Baudila | Totila |
| 78319.0 | 23624.0 | Procopius_of_Caesarea | Procopius |
| 78408.0 | 78404.0 | Aglauros | Aglaurus |
| 78485.0 | 10141.0 | The_Erinyes | Erinyes |
| 78538.0 | 78535.0 | The_Aloadae | Aloadae |
| 78603.0 | 19118.0 | Maldives/History | History_of_the_Maldives |
| 78604.0 | 19121.0 | Politics_of_Maldives | Politics_of_the_Maldives |
| 78615.0 | 19133.0 | Communications_of_Mali | Telecommunications_in_Mali |
| 78622.0 | 34374.0 | Yugoslavia/Communications | Telecommunications_in_Serbia |
| 78628.0 | 33226.0 | Western_Sahara/Communications | Telecommunications_in_Western_Sahara |
| 78645.0 | 32459.0 | Venezuela/Transportation | Transport_in_Venezuela |
| 78671.0 | 19183.0 | Government_of_Mauritania | Politics_of_Mauritania |
| 78700.0 | 31828.0 | Ukraine/Government | Politics_of_Ukraine |
| 78706.0 | 56756.0 | Government_of_Uganda | Politics_of_Uganda |
| 78735.0 | 2.0598392e7 | Priapos | Priapus |
| 78742.0 | 84597.0 | Oeno | Oenotropae |
| 78786.0 | 23037.0 | Punk_band | Punk_rock |
| 78820.0 | 76616.0 | Ma_Beagle | Beagle_Boys |
| 78931.0 | 78926.0 | Diktynna | Britomartis |
| 78993.0 | 78130.0 | Maximum_flow_minimum_cut_theorem | Max-flow_min-cut_theorem |
| 79002.0 | 79000.0 | Thisbe | Pyramus_and_Thisbe |
| 79049.0 | 21387.0 | Government_of_Nigeria | Federal_government_of_Nigeria |
| 79061.0 | 57620.0 | Transnational_issues_of_Norway | Foreign_relations_of_Norway |
| 79188.0 | 2.6289316e7 | Chronology_of_Babylonia_and_Assyria | Chronology_of_the_ancient_Near_East |
| 79212.0 | 64663.0 | Graiae | Graeae |
| 79248.0 | 44026.0 | History_of_the_United_States_National_Security_Council_1969–1974 | United_States_National_Security_Council |
| 79257.0 | 44026.0 | History_of_the_United_States_National_Security_Council_1993–present | United_States_National_Security_Council |
| 79332.0 | 79328.0 | Balios | Balius_and_Xanthus |
| 79350.0 | 79352.0 | Zetes | Boreads |
| 79358.0 | 5551335.0 | Bromios | Bromius |
| 79531.0 | 2.9033435e7 | Centimani | Hecatoncheires |
| 79550.0 | 80626.0 | Kerukes | Kerykes |
| 79730.0 | 6.5442834e7 | Toll_booth | Tollbooth |
| 79763.0 | 49728.0 | San_Francisco_County,_California | San_Francisco |
| 79776.0 | 1.9344515e7 | The_Guardian_newspaper | The_Guardian |
| 79798.0 | 8618262.0 | The_Herald-Sun | The_Herald-Sun_(Durham,_North_Carolina) |
| 79832.0 | 1853.0 | Africa/Ecology | Natural_history_of_Africa |
| 80033.0 | 398878.0 | Terry_Pratchett/The_Luggage | Rincewind |
| 80133.0 | 180370.0 | Herophile | Sibyl |
| 80198.0 | 15941.0 | Jean_Jacques_Rousseau | Jean-Jacques_Rousseau |
Now, we need to find every sequence of edges where the first is to a redirect page, and the second is a redirect link. In theory this could be done as a motif finding operation, but that is painfully slow, since it would first find all sequences of three vertices, and only then filter by the edge type being correct for the second edge. So we instead do it in a more "low-tech" way, just using an inner join on our tables - this will save us an absolute ton of time, since we don't compute any paths that aren't of the required type. Doing it with motif finding takes at least ten minutes (that is when it threw an error because my laptop went to sleep), doing it with SQL takes one minute.
redirectsWithIDs.createOrReplaceTempView("redirectsWithIDs")
val twoStepRedirects = spark.sql("""SELECT enwiki_graph_edges.src AS artA,
enwiki_graph_edges.src_title AS artA_title,
redirectsWithIDs.src AS artB,
redirectsWithIDs.src_title AS artB_title,
redirectsWithIDs.dst AS artC,
redirectsWithIDs.dst_title AS artC_title
FROM redirectsWithIDs INNER JOIN enwiki_graph_edges
ON enwiki_graph_edges.dst = redirectsWithIDs.src""")
display(twoStepRedirects)
| artA | artA_title | artB | artB_title | artC | artC_title |
|---|---|---|---|---|---|
| 297471.0 | Eisteddfod | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 601370.0 | John_Anderson,_1st_Viscount_Waverley | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1024759.0 | Rudolf_Peierls | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1320240.0 | Tom_Dowd | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7375431.0 | USS_Ernest_G._Small | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2469074e7 | Association_of_Los_Alamos_Scientists | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 183897.0 | Empire_of_Japan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6123917.0 | Up_An'_Atom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6554732e7 | Timeline_of_World_War_II_(1942) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0692824e7 | Ed_Westcott | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.8615077e7 | June_1964 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.2547084e7 | Oscar_Seborer | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6526133e7 | Bane_in_other_media | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 42297.0 | San_Luis_Valley | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 652623.0 | Otto_Robert_Frisch | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 797178.0 | First_Chief_Directorate | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4081032e7 | USS_Gasconade_(APA-85) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8482492e7 | Outline_of_United_States_history | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.966217e7 | George_A._Seitz | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 92357.0 | Military | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.7250838e7 | Critical_Mass:_America's_Race_to_Build_the_Atomic_Bomb | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.7607721e7 | John_Coster-Mullen | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2201.0 | Aage_Bohr | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 65828.0 | Smithsonian_Institution | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1680490.0 | USS_Appalachian | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.2160508e7 | Paul_W._Tibbets_IV | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0040664e7 | Allied_leaders_of_World_War_II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.6526313e7 | Harley_A._Wilhelm | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 261240.0 | Shōwa_era | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 537403.0 | Paul_Frees | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 838989.0 | Code_(cryptography) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1115774.0 | USS_Apogon | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0848236e7 | USS_Sphinx_(ARL-24) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.1379813e7 | The_Birdmen | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1363385.0 | Signal_Intelligence_Service | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3449141.0 | Symphony_No._6_(Vaughan_Williams) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4974066.0 | Xavras_Wyżryn | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5223996.0 | 1948_in_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8294561.0 | Confucius_Shrine,_Nagasaki | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.920738e7 | Avro_720 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 292758.0 | William_Higinbotham | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 477701.0 | Two-Face | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1403906.0 | Windscale_fire | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2349470.0 | James_Otsuka | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5978572e7 | University_of_Cambridge | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.5802041e7 | A._Carl_Helmholz | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.2581711e7 | Soviet_Storm:_World_War_II_in_the_East | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.5526137e7 | Enola_Gay:_The_Men,_the_Mission,_the_Atomic_Bomb | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1111596.0 | Madge_Blake | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1306229.0 | Carl_Friedrich_von_Weizsäcker | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4772073.0 | Empire_of_Vietnam | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5021154.0 | Ishfaq_Ahmad_Khan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1097549e7 | Luke_the_Spook | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.2949834e7 | Einstein_for_Beginners | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 62866.0 | United_States_Department_of_Energy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 240152.0 | Alfred_Lee_Loomis | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1903533.0 | Basque_diaspora | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2160008.0 | 76th_United_States_Congress | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2428994.0 | Cockcroft–Walton_generator | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5327576.0 | Warning_from_Space | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8855773.0 | Allied_technological_cooperation_during_World_War_II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2760938e7 | Demon_core | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4366709e7 | Ernest_B._Price | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.0113243e7 | Belgian_Congo_in_World_War_II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 30592.0 | Partial_Nuclear_Test_Ban_Treaty | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 112176.0 | Metropolis_(comics) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 194596.0 | Ore_Mountains | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 426461.0 | Sidney_H._Liebson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 841522.0 | Yoshio_Nishina | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1217440.0 | Soviet_atomic_bomb_project | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6310137e7 | List_of_shipwrecks_in_1957 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6570384e7 | Stuart_R._Schram | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.5315074e7 | Expedition_Unknown | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3378.0 | Beryllium | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 432000.0 | Arthur_Compton | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080765e7 | USS_Appling | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.9351012e7 | Pietro_Leoni | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.3296431e7 | Science_and_technology_in_Italy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.7013146e7 | Trevor_Gardner | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7.1484554e7 | Tinian_Naval_Base | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3257492.0 | Reiji_Nagakawa | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2428286e7 | Strategic_Air_Command_in_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5613512e7 | Cheng_Kaijia | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.9985187e7 | Sceptre_(fusion_reactor) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 427073.0 | USS_Stickleback_(SS-415) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 608055.0 | John_Llewellin,_1st_Baron_Llewellin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4380587.0 | Nuclear_explosion | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0318387e7 | Varian_Associates | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6266461e7 | Montreal_Laboratory | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.4298484e7 | September_1966 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7.0060916e7 | USS_Van_Valkenburgh | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6818144.0 | 1945_in_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.7088427e7 | Night_Raid_1931 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.0417329e7 | John_Lansdale_Jr. | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.3059018e7 | Calutron_Girls | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1328236.0 | 2004_Indian_Ocean_earthquake_and_tsunami | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3339809.0 | Field_coil | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3348816.0 | Frederick_Ashworth | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4439096e7 | Joan_Curran | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0384446e7 | Gokoku_Shrine | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 735622.0 | The_High_Crusade | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3916305.0 | Deaths_in_June_2006 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.2166535e7 | Donald_J._Hughes | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566564.0 | 1949_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 867074.0 | Leonid_Govorov | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3343228e7 | Western_Pipe_and_Steel_Company | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.9164498e7 | Karl_Z._Morgan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.1781954e7 | October_1976 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.447651e7 | Journals_of_Ayn_Rand | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.3576433e7 | Sniper_Elite | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2453923.0 | History_of_science_fiction_films | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.0865251e7 | George_Racey_Jordan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.729417e7 | United_States_Naval_Construction_Battalion_flame_thrower_tanks | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 126063.0 | Belen,_New_Mexico | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8401396e7 | Invention_in_Canada | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.3006249e7 | Benson_House_(Wading_River,_New_York) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.541407e7 | USS_Bowditch_(AG-30) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 365245.0 | William_O._Douglas | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 652326.0 | Rupert_Allason | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1864987.0 | USS_Greene_(DD-266) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2086313.0 | USS_Ingraham_(DD-694) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3214426.0 | Kermit_Beahan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3557640.0 | For_Want_of_a_Nail_(novel) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3673785.0 | S-50_(Manhattan_Project) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4906534.0 | History_of_mass_spectrometry | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.9272639e7 | Russell_and_Sigurd_Varian | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.4820476e7 | Undercover:_Operation_Wintersun | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.9557816e7 | Harley_D._Nygren | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6181241e7 | Zero_Hour_(2013_TV_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.5021252e7 | Connie_Frazer | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 14532.0 | Italy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 731119.0 | George_B._Pegram | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2248081.0 | USS_Gilliam_(APA-57) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2833957.0 | USS_Lowry | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.3468036e7 | History_of_weapons | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6406928e7 | Alfred_Starbird | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 106424.0 | North_Korea_and_weapons_of_mass_destruction | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 168223.0 | Theodore_Hall | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1044264.0 | Pacific_Air_Forces | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6343677.0 | Pumpkin_bomb | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0306321e7 | Harvard_John_A._Paulson_School_of_Engineering_and_Applied_Sciences | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.7659587e7 | Donald_William_Kerst | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0020033e7 | Alternate_Presidents | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.901736e7 | January_2016_North_Korean_nuclear_test | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.9055953e7 | List_of_people_from_Cedar_Rapids,_Iowa | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566574.0 | 1955_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 944651.0 | William_Shawn | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1087061.0 | Jimmy_Quillen | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1116326.0 | Uravan,_Colorado | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4546304.0 | Wendover_Air_Force_Base | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8107496.0 | Tsunami_bomb | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4793906e7 | Terrestrial_Physics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 21785.0 | Nuclear_weapon | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2978641.0 | Landing_Craft_Assault | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1148129e7 | Dayton_Project | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 221380.0 | Nagasaki_Prefecture | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7008903.0 | The_White_Negro | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 41976.0 | Franco_Rasetti | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 904771.0 | Seal_of_the_President_of_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1130207.0 | Boeing_B-29_Superfortress_variants | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1508301.0 | Futures_studies | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.1434966e7 | History_of_the_bikini | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2784489.0 | Floyd_Schmoe | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2884514.0 | Stephane_Groueff | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1496875e7 | USS_LST-661 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2785531e7 | Daniel_Klute | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.2375459e7 | List_of_American_Restoration_episodes | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.5585051e7 | Empire_State_Building_in_popular_culture | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7.1351682e7 | List_of_existing_technologies_predicted_in_science_fiction | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4868.0 | B._F._Skinner | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 236130.0 | Waukesha,_Wisconsin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 543695.0 | USS_Perkins_(DD-877) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1641423.0 | Alfred_Sturtevant | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2878196.0 | Pelindaba | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.480743e7 | 1950_in_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.9637836e7 | Mysteries_at_the_Monument | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.8289969e7 | January_1955 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.7844172e7 | Windscale_Piles | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 495005.0 | Jim_Sanborn | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8805287e7 | 1964_in_China | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.3437266e7 | Shunichi_Yamashita | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.7370993e7 | Angus_Ewan_Cameron | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 343960.0 | Heavy_bomber | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 610255.0 | 1939_in_science | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 640709.0 | Firestorm_(character) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 744371.0 | Kirtland_Air_Force_Base | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1309813.0 | The_Dark_Frontier | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8573725e7 | Cosmic_bomb_(phrase) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8750896e7 | Eric_Craven_Gregory | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0046202e7 | Harold_Hamm | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 51981.0 | List_of_planned_cities | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 419026.0 | Bayview–Hunters_Point,_San_Francisco | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 614477.0 | The_Crimson_Ghost | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2589068.0 | James_L._Cate | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5223800.0 | 1947_in_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5701828.0 | Martin_Stein | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8424612.0 | Tom_Sachs | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4741699e7 | Critical_Assembly | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 469583.0 | Pickett's_Charge | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1109287.0 | Jacob_A._Marinsky | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1162701.0 | Talia_al_Ghul | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1163830.0 | Lydia_Millet | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5058589e7 | Ben_Bruce_Blakeney | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0485345e7 | 2011_in_science | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 202643.0 | Agnes_Moorehead | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 284020.0 | Angels_in_Neon_Genesis_Evangelion | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 311260.0 | Thomas_Walker_(naval_officer) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5428681e7 | USS_Rockwall_(APA-230) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6672111e7 | Harvesting_lightning_energy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 98553.0 | Red_Skull | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 147983.0 | Preliminary_Design_of_an_Experimental_World-Circling_Spaceship | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 891446.0 | Alfred_O._C._Nier | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1988966.0 | Gregory_Breit | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2697091.0 | Timeline_of_the_Manhattan_Project | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3823666.0 | Haigerloch | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0303348e7 | USS_Orca_(AVP-49) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.816252e7 | History_of_American_comics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.8219053e7 | July_1955 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566571.0 | 1952_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0455469e7 | Honkawa_Elementary_School_Peace_Museum | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0486122e7 | History_of_the_University_of_California,_Berkeley | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.2380985e7 | Church_of_St_Editha,_Tamworth | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.380756e7 | Genius_(American_TV_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 115068.0 | Fort_Thomas,_Kentucky | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 380013.0 | The_Time_Ships | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 481491.0 | RAF_Transport_Command | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 587916.0 | Hunters_Point_Naval_Shipyard | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 890736.0 | Emory_River | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1182927.0 | Social_stratification | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0018206e7 | First_Into_Nagasaki | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5074645e7 | Lords_of_the_Psychon | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.0041779e7 | Nuclear_ethics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.4222969e7 | Katie_Ardill | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.1523278e7 | Leslie_Wolfe | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 18166.0 | List_of_agnostics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 189945.0 | USS_Nevada_(BB-36) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 383813.0 | Nuclear_and_radiation_accidents_and_incidents | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1449144.0 | List_of_Ig_Nobel_Prize_winners | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4004485e7 | Political_views_of_Albert_Einstein | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 151055.0 | Oak_Ridge,_Tennessee | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 518249.0 | USS_Tuna_(SS-203) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1248476.0 | Troy_H._Middleton | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1673736.0 | Sociology_of_the_history_of_science | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.9085527e7 | Alexander_Langsdorf_Jr. | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 19346.0 | March_1 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 151196.0 | Acute_radiation_syndrome | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3592651.0 | List_of_shipwrecks_in_1946 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0827425e7 | USS_Bayfield | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8443325e7 | Haywood_S._Hansell | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6357760.0 | Dragon_(Cussler_novel) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080827e7 | USS_Bladen | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4727747e7 | Wilhelm_Ohnesorge | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5070335e7 | Political_Science_(song) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 31282.0 | Truncated_icosahedron | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 34614.0 | 1939 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1222318.0 | Sapienza_University_of_Rome | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1793.0 | August_29 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 21210.0 | Niels_Bohr | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 186324.0 | USS_Thompson_(DD-627) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 243020.0 | Louis_A._Johnson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 422293.0 | USS_Sailfish_(SS-192) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 763708.0 | Herman_Goldstine | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5273932.0 | USS_Panamint_(AGC-13) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5559770.0 | History_of_New_Mexico | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1310622e7 | USS_Gunston_Hall_(LSD-5) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.425853e7 | Civil_Defence_Ireland | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4272303e7 | Carolinium | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.9157112e7 | 1950–51_Ashes_series | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.969082e7 | Military_history_of_Jewish_Americans | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.975373e7 | Berkeley_Piano_Club | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 188123.0 | USS_Bairoko | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1185988.0 | Alberto_Moravia | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.1637581e7 | Canada–Democratic_Republic_of_the_Congo_relations | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.3719379e7 | Timeline_of_the_20th_century | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 21277.0 | Neptunium | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 485200.0 | Lexington-class_aircraft_carrier | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1003982.0 | Dual-use_technology | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1069520.0 | List_of_people_from_New_York_City | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4444436.0 | Francis_Simon | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 47595.0 | Manchuria | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 170365.0 | History_of_Tristan_da_Cunha | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.7816236e7 | List_of_people_considered_father_or_mother_of_a_scientific_field | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.6539426e7 | Red_Joan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.5710792e7 | Casaba-Howitzer | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 75977.0 | List_of_inventors | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 242883.0 | History_of_nuclear_weapons | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 841565.0 | James_L._Tuck | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7636187.0 | Richard_Kenney_(poet) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8968304.0 | Hakushima_Station_(Hiroden) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.713328e7 | Atomic_veteran | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.9011847e7 | USS_Mispillion_(AO-105) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.0777269e7 | Jane_Hamilton_Hall | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.1053117e7 | Project_Y | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 34631.0 | 1946 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 61207.0 | Potsdam_Declaration | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1291485.0 | Situational_ethics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8084518e7 | ARA_Suboficial_Castillo | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.2933627e7 | List_of_monsters_in_Marvel_Comics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 206082.0 | USS_West_Virginia_(BB-48) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 252881.0 | Operation_Downfall | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1118396.0 | Chicago_Pile-1 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2614007.0 | Office_of_Censorship | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0168394e7 | Jennet_Conant | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.9086313e7 | Perhapsatron | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.1191171e7 | McAllister_Hull | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.898596e7 | Johnstown_flood_of_1977 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.5514071e7 | Alberto_Thompson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 157288.0 | Tamworth,_Staffordshire | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1615727.0 | Hiroshima_mon_amour | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4051468.0 | Plutonium-238 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4110093.0 | X-10_Graphite_Reactor | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5640532.0 | VFW_VAK_191B | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0230427e7 | Outliers_(book) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0885401e7 | The_Plutonium_Files | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 207714.0 | USS_Independence_(CVL-22) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080813e7 | USS_Banner_(APA-60) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5045707e7 | The_War_of_the_Worlds_(1953_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.3182077e7 | Timeline_of_the_Harry_S._Truman_presidency | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 177595.0 | Glenn_L._Martin_Company | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 269040.0 | History_of_the_United_States_(1945–1964) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 296724.0 | Lamar_Alexander | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3978499.0 | Brave_New_World_(role-playing_game) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0655215e7 | Fukuromachi_Elementary_School_Peace_Museum | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1803775e7 | Spontaneous_Combustion_(film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 31743.0 | Uranium | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6000024e7 | Let's_Go_All_the_Way_(song) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8182.0 | Dwight_D._Eisenhower | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 46825.0 | Otto_Hahn | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 76402.0 | Twelve_O'Clock_High | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 471660.0 | Nâzım_Hikmet | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566575.0 | 1956_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 947310.0 | United_States_Department_of_Energy_national_laboratories | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1074739.0 | Seth_Neddermeyer | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1210990.0 | Calutron | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3686782.0 | USS_Weeden_(DE-797) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4548723.0 | Frank_A._Armstrong | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 30395.0 | Tennessee | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 381797.0 | James_Franck | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.2184963e7 | History_of_St._Louis_(1905–1980) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.741841e7 | Edith_Warner | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 56359.0 | Leo_Szilard | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1041429.0 | Harry_Daghlian | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1067258.0 | Global_Garden | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2149724.0 | Doctor_Atomic | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.8702523e7 | List_of_fictional_United_States_presidencies_of_historical_figures_(P–R) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 698908.0 | Tube_Alloys | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1185584.0 | Melba_Phillips | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2455295.0 | Nô_(film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5595604e7 | Military_history | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 611806.0 | John_Dill | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1626768.0 | Massachusetts_Museum_of_Contemporary_Art | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2908928.0 | MAUD_Committee | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2988445.0 | Einstein–Szilard_letter | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8793967.0 | Apocrypha_(The_X-Files) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.7635462e7 | List_of_atheists_in_science_and_technology | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 32310.0 | Lockheed_U-2 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 420888.0 | USS_Tautog_(SS-199) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1543748.0 | Stephen_Toulmin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2390573.0 | Stan-hattan_Project | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4538945.0 | Presidency_of_Dwight_D._Eisenhower | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9743605.0 | From_Hell_It_Came | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.100775e7 | Shudo_Junior_and_Senior_High_School | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.6058792e7 | RAF_Lakenheath_nuclear_weapons_accidents | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7.0939397e7 | List_of_yard_and_district_craft_of_the_United_States_Navy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566201.0 | 1946_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4109863.0 | Gump_(song) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.5688415e7 | 1949_in_the_Soviet_Union | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.77489e7 | Harnekop_Nuclear_Bunker | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.6447473e7 | The_Cyclotron | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.9527094e7 | AMES_Type_85 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 241681.0 | Terence_McKenna | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1169762.0 | Freedom_Fighters_(video_game) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 100462.0 | Defense_Intelligence_Agency | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7128122.0 | List_of_The_Waltons_episodes | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1511888e7 | Thomas_Allibone | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4656403e7 | USS_Tillamook_(ATA-192) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4950378e7 | Walter_Kauzmann | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6594219e7 | USS_Oak_Hill_(LSD-7) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4137423e7 | Charles_B._Winstead | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.3980914e7 | United_States_war_plans_(1945–1950) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2957941.0 | Marman_clamp | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3666971.0 | Stainsby_Festival | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5429164e7 | USS_Bollinger_(APA-234) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8026101e7 | Arnold_Anderson_(scientist) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.5942972e7 | Polaris_(UK_nuclear_programme) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.2018264e7 | British_hydrogen_bomb_programme | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 21649.0 | New_Mexico | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4199790.0 | J._Ernest_Wilkins_Jr. | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5112421.0 | I_Melt_with_You | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.5904463e7 | The_Troubleshooters_(1959_TV_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1366782.0 | Eugene_Dooman | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4548588.0 | East_vs._West:_Berlin_1948 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5047394.0 | USAAF_unit_identification_aircraft_markings | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.7915163e7 | E._Alison_Kay | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.9862015e7 | A_Game_for_the_Living | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.2087293e7 | William_Shurcliff | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.4266507e7 | Ross_Gunn | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.740317e7 | Josephine_Herrick | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 307267.0 | Max_Frisch | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 352564.0 | John_Cockcroft | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 371973.0 | 1917_in_Canada | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2095669.0 | Nuclear_weapons_of_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2428261.0 | Tryokhgorny | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.186025e7 | Oscar_F._Perdomo | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2789033e7 | Margo_Lane | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8385871e7 | Cosmic_Ray_(film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.780791e7 | Eldorado_Radium_Silver_Express | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 274718.0 | Far_East_Air_Force_(Royal_Air_Force) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2273655.0 | Anatoli_Yatskov | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3545701e7 | List_of_National_Historic_Landmarks_in_New_York_City | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080845e7 | USS_Bracken | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 31748.0 | Ultra | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 227156.0 | Tinian | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9263047.0 | Ernie_Schroeder | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 186234.0 | David_Bohm | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3318378.0 | Pisa_University_System | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 350452.0 | Kettering | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1614761.0 | Headington_Shark | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0875609e7 | Ralph_Austin_Bard | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.5770596e7 | 10_Things_You_Don't_Know_About | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 907194.0 | Mohammad_Hatta | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1010930.0 | Kenneth_McKellar_(politician) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1840164.0 | Via_Panisperna_boys | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3383351e7 | Modulated_neutron_initiator | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8932654e7 | Koyaanisqatsi | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 283846.0 | Culture_of_Italy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1176777.0 | Science_and_technology_in_the_Soviet_Union | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5097628.0 | Above_and_Beyond_(1952_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6860985.0 | USS_Turner_(DD-834) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9009865.0 | Salinas_Peak | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.1450446e7 | List_of_shipwrecks_in_1948 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 355106.0 | Röyksopp | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0373265e7 | Timeline_of_the_North_Korean_nuclear_program | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5743474e7 | William_Duthie_Morgan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 16326.0 | John_W._Campbell | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 22054.0 | Nuclear_fission | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 185853.0 | Hans_Bethe | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 335761.0 | Seto_Inland_Sea | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 504387.0 | List_of_people_from_Nebraska | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566563.0 | 1947_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1346709.0 | Frank_Spedding | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4096976.0 | Nostradamus_in_popular_culture | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9424050.0 | The_Day_the_Fish_Came_Out | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5436286e7 | Hispanics_in_the_United_States_Marine_Corps | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.0684157e7 | Two_Bombs,_One_Satellite | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 803611.0 | Lewis_Strauss | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2505538.0 | GURPS_Alternate_Earths | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7955348.0 | Université_de_Montréal | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4928506e7 | February_1960 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5712799e7 | Chagai-II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566569.0 | 1950_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2781821.0 | Aqueous_homogeneous_reactor | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4708774.0 | Dominique_Lorentz | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2014603e7 | Operation_Passage_to_Freedom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2471253e7 | The_Second_World_War_(book_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080965e7 | USS_Crittenden_(APA-77) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8401364e7 | Natural_scientific_research_in_Canada | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.7577491e7 | Yoshio_Shigezono | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.888259e7 | Arthur_V._Peterson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 124989.0 | Mahwah,_New_Jersey | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 209935.0 | University_of_Birmingham | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3267235.0 | Gareth_Cook | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.0406914e7 | Robert_Brode | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 214043.0 | Hiroshima_Peace_Memorial | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 340801.0 | Arlington_Hall | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.343171e7 | 97th_Operations_Group | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.9213292e7 | Buck_Rogers | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.8586398e7 | SS-GB_(TV_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3392.0 | British_Columbia | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 199511.0 | Paul_Tibbets | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566572.0 | 1953_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 958549.0 | List_of_University_of_California,_Berkeley_faculty | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5260518.0 | The_Towers_of_Silence | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6754432.0 | RAF_Grafton_Underwood | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.6792968e7 | Peer_de_Silva | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 285651.0 | Jonathan_Pollard | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1627304.0 | Michihiko_Hachiya | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.6365449e7 | November_1950 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 18597.0 | Little_Boy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 37782.0 | Edward_Teller | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3904585e7 | Roger_Bourke_White | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.054518e7 | Ames_Project | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4807261e7 | 1946_in_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 127269.0 | Cutchogue,_New_York | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 410215.0 | Sam_Rayburn | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 608261.0 | First_Quebec_Conference | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3384048.0 | WASH-740 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4859933.0 | Eugene_Pallette | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6438468e7 | Alternative_versions_of_Joker | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6638928e7 | List_of_Jewish_atheists_and_agnostics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.9029604e7 | De_Avonturen_van_Pa_Pinkelman | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1141498.0 | USS_Parche_(SS-384) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4037583.0 | Laurence_Dworet | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.2802425e7 | John_T._Hayward | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.4309803e7 | Mind_at_the_End_of_Its_Tether | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.8383891e7 | Walter_M._Robertson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 295100.0 | The_Great_Artiste | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 713949.0 | Baltimore_City_College | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 894522.0 | Fu_Foundation_School_of_Engineering_and_Applied_Science | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1762066.0 | Franck_Report | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6124046.0 | Big_Stink_(aircraft) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8037469.0 | The_Pirate_(short_story) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 16518.0 | John_Adams_(composer) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2681988.0 | Steagles | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5780650.0 | Worldwar:_Striking_the_Balance | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.1819273e7 | Chemical_weapons_and_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 376433.0 | Gunbarrel_Highway | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.404778e7 | Battle_Beneath_the_Earth | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 26787.0 | Science_fiction | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566278.0 | 1945_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 949556.0 | USS_Skipjack_(SS-184) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2327916.0 | Pavel_Sudoplatov | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7246788.0 | Monster_Squad | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3670898e7 | Science_in_science_fiction | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8802741e7 | USS_Basilan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 584985.0 | List_of_Harvard_University_people | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4017673.0 | RAF_Polebrook | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0966108e7 | Noel_Gayler | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.7811234e7 | Radium_Mine | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.3851902e7 | Blue_Light_(TV_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 746591.0 | List_of_Columbia_University_people | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2887573.0 | Robert_Meeropol | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3497076.0 | Truman_(1995_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4305070.0 | History_of_Western_civilization | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4653534.0 | Military_history_of_the_United_States_during_World_War_II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.250021e7 | Joseph_George_Davidson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8648253e7 | I_Saw_It | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.7138667e7 | Paul_Olum | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1586498.0 | Y-12_National_Security_Complex | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5382389.0 | 1952_in_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9236337.0 | USS_Barrow | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 284008.0 | Peace_symbols | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1834663.0 | Bruno_Pontecorvo | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6600382e7 | 1960_in_France | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5025014e7 | Chase_Brass_and_Copper_Company | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3227851.0 | USS_Walter_X._Young_(APD-131) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5422186.0 | Arthur_Widmer | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5991505.0 | The_Shadow_(1994_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7860719.0 | Alexander_Sachs | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8603896e7 | Air_Power_(TV_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1202989.0 | El_Malpais_National_Monument | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8798023.0 | Leon_Davidson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.885462e7 | North_American_P-51_Mustang_variants | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.9690539e7 | October_1964 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7475.0 | CANDU_reactor | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 57977.0 | Mad_scientist | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 74641.0 | George_Gamow | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 76796.0 | History_of_the_People's_Republic_of_China_(1949–1976) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 255198.0 | Notorious_(1946_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566570.0 | 1951_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 590219.0 | Robert_R._Wilson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 635378.0 | Boeing_B-50_Superfortress | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 725910.0 | George_Paget_Thomson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4563051.0 | 509th_Bomb_Wing | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0291182e7 | Swiatecki_bomb_slip | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.0521574e7 | Raemer_Schreiber | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 645510.0 | Len_Beadell | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.3010948e7 | Most_Dangerous_Man_Alive | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 59503.0 | Bioaccumulation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 156512.0 | University_of_Liverpool | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5448406e7 | Deadline_(science_fiction_story) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.7192205e7 | Teck_Cominco_smelter | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 563950.0 | Coesite | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4331044.0 | List_of_World_War_II_films | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.0931083e7 | 147th_Regiment_(United_States) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.2715001e7 | Michael_D._Gordin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.9316616e7 | Military_Intelligence_Bureau | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 34604.0 | 1949 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 894309.0 | The_Beast_from_20,000_Fathoms | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 915493.0 | Robert | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1051266.0 | Ash_Wednesday_bushfires | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4891267e7 | Ye_Qisun | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.6734588e7 | Harold_G._Bowen_Sr. | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.7577492e7 | Minoru_Yamamoto | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 151876.0 | Bulgarians | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2587870.0 | History_of_Washington_(state) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3128713.0 | People's_Liberation_Army_Rocket_Force | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.034096e7 | Timeline_of_the_nuclear_program_of_Iran | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5106061e7 | Barry_Goldwater_1964_presidential_campaign | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4814676e7 | 1995_in_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.8115177e7 | Stewart_Menaul | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 459254.0 | Dogfight | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1630495.0 | Norman_Cousins | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2095158.0 | Downtown_Las_Vegas | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4364523.0 | Fission_products_(by_element) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5053802.0 | Reprieve_(album) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1458432e7 | Take_It_So_Hard | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4877182e7 | Samuel_Curran | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.8260456e7 | Golden_Days_(novel) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.2130966e7 | List_of_modern_obelisks | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.3607564e7 | Type_B_ship | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4394952.0 | Thomas_Ferebee | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1477953e7 | USS_Sioux_(AT-75) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8649025e7 | Jacob_Bigeleisen | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.0399438e7 | Fulmer_Research_Institute | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 366224.0 | Anchor_telephone_exchange | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 864141.0 | Prentice_Cooper | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 920062.0 | Special_Bulletin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4989316e7 | Aircraft_in_fiction | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 173857.0 | Harvard_Mark_I | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 304427.0 | Abdus_Salam | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1225199.0 | Henry_DeWolf_Smyth | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1986557.0 | Invasion,_U.S.A._(1952_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4885883.0 | William_G._Windrich | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9031869.0 | High_and_low_politics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.2233947e7 | Riazuddin_(physicist) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8886184e7 | Karl_K._Darrow | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.2044512e7 | Shuntaro_Hida | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 396703.0 | John_Hasbrouck_Van_Vleck | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 839581.0 | Charles_Sweeney | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 916752.0 | Shelby_Foote | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.558561e7 | Southbridge,_Massachusetts | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 299543.0 | The_Fourth_Protocol | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1242120.0 | GURPS_Technomancer | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1906796.0 | Yves_Rocard | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4490386.0 | Sniper_Elite_(video_game) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.1795239e7 | John_R._Huizenga | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 29365.0 | Synthetic_element | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2548934.0 | Frisch–Peierls_memorandum | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3744153.0 | Charles_D._Neff | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4645143e7 | Goodbye_California | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.3358285e7 | Middlesex_Sampling_Plant | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0343428e7 | Aaron_Novick | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.1968585e7 | RDS-3 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.7873336e7 | Gerhard_Dickel | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 199804.0 | Science_and_technology_in_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9710670.0 | List_of_Sliders_characters | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8815826e7 | USS_Avery_Island | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.5351286e7 | Buoyant_Billions | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 157173.0 | Last_Year_at_Marienbad | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.9376664e7 | Rhydymwyn | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 197870.0 | Chūgoku_region | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1430753.0 | Glendale,_Queens | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1711682e7 | James_C._Marshall | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2343369e7 | Yukawa_Institute_for_Theoretical_Physics | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.0355866e7 | The_Catcher_Was_a_Spy_(film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 655444.0 | Satish_Kumar | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1616268e7 | Kenneth_Hubbard | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.8900893e7 | Westinghouse_Lamp_Plant | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.4282331e7 | Wolfenstein_II:_The_New_Colossus | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 10979.0 | Franklin_D._Roosevelt | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 157241.0 | Edwin_McMillan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4395936.0 | Harrie_Massey | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3800803e7 | Uranium_hydride_bomb | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.7550552e7 | Władysław_Świątecki_(inventor) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 71469.0 | Barn_(unit) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.201155e7 | USS_Wharton_(AP-7) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6450276e7 | Lynde_D._McCormick | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5031314e7 | Frank_W._Bubb_Sr. | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6693409e7 | Albert_G._Mumma | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.4936329e7 | 2017_Barcelona_attacks | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.7009377e7 | Ron_Robin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.8101625e7 | Paul_F._Kerr | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1121111.0 | HMS_Tracker | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2073714.0 | Daniel_Pedoe | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.770163e7 | Oppenheimer_security_hearing | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.8927159e7 | Federal_Reserve_Bank_Building_(Seattle) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1136451.0 | Philip_Klutznick | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1762770.0 | The_Japan_That_Can_Say_No | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1850366.0 | John_E._Rankin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.7484707e7 | Five_(1951_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.3595815e7 | Union_of_Australian_Women | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.4074888e7 | USS_LST-911 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 207545.0 | Ronald_Knox | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 566217.0 | 1948_in_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1478613.0 | Vemork | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8426318.0 | Four_Pillars_of_Destiny | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080945e7 | USS_Cortland_(APA-75) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4214337e7 | Project-706 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.2274342e7 | 159th_Liaison_Squadron | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 331795.0 | Kon-Tiki_expedition | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.3624019e7 | Hunter_(1977_TV_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 49814.0 | USS_Salt_Lake_City | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 458829.0 | North_American_AJ_Savage | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2246401.0 | Kenneth_Bainbridge | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3431902.0 | USS_Stack | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5699083.0 | Survival_Under_Atomic_Attack | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6932508.0 | Pacific_Vortex! | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1496437e7 | USS_LST-545 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5881941e7 | Suippes | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8459715e7 | Oscar_D'Agostino | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.5640974e7 | April_1958 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.6170003e7 | African-American_scientists_and_technicians_on_the_Manhattan_Project | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8524.0 | Deuterium | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 797121.0 | Powel_Crosley_Jr. | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0673649e7 | Robert_Cornog | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7834.0 | Chain_reaction | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 14875.0 | Iowa_State_University | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5231726.0 | 1940_in_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.9382758e7 | Dowding_system | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.5541387e7 | Japanese_submarine_Ha-204 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 614502.0 | 1941_in_science | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1584322.0 | Scuttling | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3632184.0 | George_Economou_(Manhattan_Project) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7650738.0 | John_Rowlands_(RAF_officer) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1388754e7 | List_of_Eastern_Bloc_agents_in_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.1135461e7 | Vance_Bourjaily | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.7756905e7 | Red_Barbarian | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.4780239e7 | Exercise_Ardent | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 225214.0 | Timeline_of_United_States_history_(1930–1949) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 752468.0 | 10th_Division_(Australia) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8740320.0 | 1950_in_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2858697e7 | List_of_The_Hardy_Boys_characters | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.1611268e7 | List_of_shipwrecks_in_1951 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.2701269e7 | Muhammad_Hafeez_Qureshi | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.7004687e7 | John_D._Craig | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 151018.0 | North_Augusta,_South_Carolina | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 428792.0 | Michael_Frayn | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1096465.0 | Radio_Yerevan_jokes | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0784195e7 | London_Letters | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.7441105e7 | Alan_Herries_Wilson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.406565e7 | Ralph_Landau | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.2573493e7 | High_Explosive_Research | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 65001.0 | Gamera | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3192836.0 | Naval_history_of_Japan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3874749.0 | Devil's_Planet | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6992045.0 | 1945_(Gingrich_and_Forstchen_novel) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1384035e7 | Yutaka_Yaguchi | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.3140913e7 | One_Ring | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.0350501e7 | List_of_Silicon_Valley_characters | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 34550.0 | 1964 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1588017.0 | Minor_characters_in_Bloom_County | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4831178.0 | Civil_defense_in_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9443576.0 | Claus_Helberg | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.758355e7 | Hugh_Bradner | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.2443672e7 | USS_Varuna_(AGP-5) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.1589599e7 | Attack_on_Yokosuka | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.4794861e7 | Project_Nobska | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.3469248e7 | Robert_Lyster_Thornton | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 365519.0 | Victory_over_Japan_Day | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3618428.0 | David_Lawrence_(publisher) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3491749e7 | USS_Rockingham_(APA-229) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8218304e7 | July_1946 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 32767.0 | Vannevar_Bush | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 560402.0 | Fang_Lizhi | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3676851.0 | Eugene_Rabinowitch | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4126534.0 | Charles_Wilson,_1st_Baron_Moran | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4385677.0 | How_Not_to_Be_Seen_sketch | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9365369.0 | Leona_Woods | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9986683.0 | Army_Service_Forces | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 63794.0 | Impact_event | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2346523.0 | Morris_R._Jeppson | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6156759.0 | Mark_(designation) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 125944.0 | Alamogordo,_New_Mexico | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2933431.0 | USS_Mayrant_(DD-402) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3005313.0 | Sergey_Kurnakov | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4308762.0 | List_of_Sin_City_characters | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 25523.0 | Richard_Feynman | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 343445.0 | Qian_Xuesen | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2395137.0 | Surrender_of_Japan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3761256.0 | Roy_Pinney | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5320361.0 | List_of_Batman:_The_Animated_Series_episodes | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9504718.0 | Happy_Nation_(song) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6310812e7 | List_of_shipwrecks_in_1952 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.1211197e7 | Grasshoppers_(Cavallette) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.1816179e7 | Union_Prayer_Book | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 146971.0 | Green_Goddess | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 207089.0 | USS_Arkansas_(BB-33) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 515922.0 | Judith_Miller | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 972784.0 | Military_aviation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.5489058e7 | List_of_battlecruisers_of_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 34624.0 | 1945 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6478244.0 | Little_Green_Men_(Star_Trek:_Deep_Space_Nine) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.2939865e7 | Charlotte_Serber | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.0441714e7 | Lindsay_Helmholz | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1155121.0 | USS_Niagara_(APA-87) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2719824e7 | USS_Aucilla | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0516946e7 | Lawrence_E._Glendenin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1356997.0 | Vermont_C._Royster | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1374482.0 | Urakami | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2149708.0 | Did_Six_Million_Really_Die? | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9676404.0 | Nguyễn_Chí_Thiện | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3391948e7 | Gustave_Reininger | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4038372e7 | Bismuth_phosphate_process | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1951235.0 | Metallurgical_Laboratory | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3093327.0 | Caesium-137 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.7626644e7 | Frances_V._Harbour | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.4322758e7 | Edward_P._Ney | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.3764346e7 | AI_Mark_VIII_radar | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.3331078e7 | List_of_Operational_Requirements_for_nuclear_weapons | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.1487212e7 | Allen_F._Donovan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1197121.0 | USS_Pilotfish_(SS-386) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1643572.0 | José_Leite_Lopes | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3818583.0 | HMH-361 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5987636.0 | USS_Trefoil_(IX-149) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2768472e7 | USS_Enoree_(AO-69) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 206657.0 | USS_Shangri-La | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1791662.0 | William_Penney,_Baron_Penney | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0707567e7 | Clinton_Engineer_Works | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.3355228e7 | Latin_music | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 155558.0 | Sandia_National_Laboratories | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 310004.0 | Ronald_W._Clark | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 490588.0 | Bernard_T._Feld | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2742737e7 | The_\"Fish\"_Cheer/I-Feel-Like-I'm-Fixin'-to-Die_Rag | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0390062e7 | M._S._Factory,_Valley | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.3779882e7 | Robert_von_Ezdorf | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 448716.0 | William_D._Leahy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1056809.0 | Yoshito_Matsushige | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1125711.0 | Operation_Hurricane | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6976884.0 | Battlefield_Earth_(novel) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8663924.0 | 1955_in_the_United_Kingdom | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2536016e7 | Hispanic_Americans_in_World_War_II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 17845.0 | Letter_(message) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 33496.0 | Weapon | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 164329.0 | Avro_Lancaster | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2933554.0 | USS_Trippe_(DD-403) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6123978.0 | Laggin'_Dragon | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1637189.0 | William_L._Clayton | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3774164.0 | USS_Geneva_(APA-86) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5521547.0 | Danish_Americans | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4065418e7 | Non-stick_surface | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.7978606e7 | A_Thousand_Suns | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.764875e7 | David_B._Nicodemus | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.8890803e7 | Norman_Hilberry | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 22133.0 | Nuclear_chain_reaction | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3226839.0 | George_Silk | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3528759.0 | History_of_Halifax,_Nova_Scotia | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7174639.0 | Insertion_time | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.8012103e7 | The_Untold_History_of_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.310898e7 | Kaj_Aage_Gunnar_Strand | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 10264.0 | Enrico_Fermi | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 66057.0 | DuMont_Television_Network | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 495585.0 | Taiyō_o_Nusunda_Otoko | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4827066.0 | Paul_Norris | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1252250.0 | Godzilla,_Mothra_and_King_Ghidorah:_Giant_Monsters_All-Out_Attack | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1607173.0 | George_L._Harrison | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.4806977e7 | 1939_in_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 11493.0 | Fallout_shelter | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 206221.0 | Henry_H._Arnold | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2063689.0 | Arthur_Jeffrey_Dempster | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3648687.0 | Shinkolobwe | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0727293e7 | Iccho_Itoh | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2819734e7 | List_of_people_considered_father_or_mother_of_a_field | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.3093845e7 | Noriaki_Tsuchimoto | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.3990103e7 | Box_Car_Racer | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.1957579e7 | British_contribution_to_the_Manhattan_Project | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.977474e7 | Deaths_in_March_2000 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 63335.0 | Childhood's_End | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 898162.0 | List_of_fictional_monarchs_of_real_countries | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8224295.0 | A4200_road | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9087090.0 | Astral_Doors | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3674069e7 | Hippocratic_Oath_for_scientists | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.5256987e7 | Kirill_Tolpygo | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.2952515e7 | Charles_L._Carpenter | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 179512.0 | Jumping_the_shark | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 203279.0 | USS_Saratoga_(CV-3) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 396733.0 | Val_Logsdon_Fitch | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 507859.0 | Louis_Slotin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 594037.0 | Special_Relationship | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 597900.0 | Saunders-Roe_SR.53 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080927e7 | USS_Cleburne_(APA-73) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.4565427e7 | James_C._Keck | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 193367.0 | USS_Salt_Lake_City_(CA-25) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 999864.0 | Trinity_(video_game) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4920194.0 | Frederick_Hurten_Rhead | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5807387.0 | List_of_Jesuits | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.255528e7 | June_1959 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.0488736e7 | The_Second_World_War_(book) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.5111069e7 | The_Man_in_the_High_Castle_(TV_series) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4162206.0 | Fort_Halstead | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3065014e7 | Top_Cottage | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.2802349e7 | Naval_history_of_World_War_II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2509392.0 | GURPS_Infinite_Worlds | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5372973.0 | History_of_St._Louis | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2124633e7 | USS_Tills | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.5795138e7 | List_of_World_War_II_science_fiction,_fantasy,_and_horror_films | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.7564195e7 | Lester_Skaggs | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.6933294e7 | Bombing_of_Tokyo_(10_March_1945) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1107368.0 | Langdon_Warner | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1217460.0 | Scuola_Normale_Superiore_di_Pisa | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2268250.0 | Mark_Muir_Mills | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0984126e7 | Seabees_in_World_War_II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.1913329e7 | Munir_Ahmad_Khan | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.8619803e7 | The_Cyborg_and_the_Sorcerers | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8695418e7 | Curious_Notions | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.9051028e7 | List_of_fictional_presidents_of_the_United_States_(G–H) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2639754.0 | William_Deakin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1759466e7 | Jacob_Beser | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.2815847e7 | Day_One_(1989_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 128452.0 | Cavendish_Laboratory | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 587693.0 | William_Sterling_Parsons | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2276037.0 | Padre_Island | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0944233e7 | Charles_McNider | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 879501.0 | Norwegian_heavy_water_sabotage | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.271696e7 | USS_Chilton_(APA-38) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.6555042e7 | Timeline_of_World_War_II_(1945–1991) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.7661638e7 | Ray_Lawrence_(record_producer) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 97830.0 | Nuclear_technology | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7305060.0 | USS_Mender | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1388411.0 | Copenhagen_(play) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1964078.0 | Crash_Dive | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3372435.0 | Charles_Christian_Lauritsen | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3698050.0 | Hans_Rosbaud | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6728339.0 | Raymond_R._Schumacher | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8340706.0 | Killers_from_Space | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.0853791e7 | War_against_the_potato_beetle | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.4496752e7 | Arnold_Wolfers | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.6724944e7 | August_1945 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2522233.0 | USCGC_Bramble_(WLB-392) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2696902.0 | P._Y._Saeki | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5674470.0 | Fireworks_by_Grucci | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5710347e7 | 515th_Parachute_Infantry_Regiment_(United_States) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6801864e7 | Kon-Tiki_(2012_film) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.0525886e7 | Hydrogen_isotope_biogeochemistry | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 516918.0 | Jose_P._Laurel | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.7354728e7 | Chemical_Warfare_Service:_Flame_Tank_Group_Seabees | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7729.0 | Captain_America | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 307257.0 | Roscoe_H._Hillenkoetter | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1016442.0 | Manuela_Santiago_Collazo | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2317416.0 | Coffee_Talk_(Saturday_Night_Live) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2875647.0 | Energy–momentum_relation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4714516.0 | Laser_weapon | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8589876e7 | First_Yank_into_Tokyo | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8783048e7 | Nuclear_Secrets | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.9464762e7 | Herbert_Durkin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 19603.0 | Manhattan_Project | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 39038.0 | Hanford_Site | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 39040.0 | Ernest_Lawrence | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 423366.0 | Jacob_Viner | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 711853.0 | Joe_Kieyoomia | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.1443761e7 | Mikhail_Pervukhin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 406017.0 | Balance_of_terror | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 777174.0 | Big_science | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1312930.0 | Wrong_Is_Right | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2777254.0 | Helge_Jung | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6346748.0 | Manhattan_Project_(song) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5.3343304e7 | Arthur_David_Torlesse | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 177729.0 | Zeppo_Marx | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 355000.0 | James_B._Conant | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 951124.0 | Auric_Goldfinger | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3048699.0 | Moist_desquamation | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8892305.0 | 2002_Eastern_Mediterranean_event | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7.0694704e7 | Theta_pinch | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 39034.0 | J._Robert_Oppenheimer | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4061738.0 | Thomas_Farrell_(United_States_Army_officer) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.9283873e7 | 1950 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0526307e7 | Charles_D._Coryell | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7.1664437e7 | Liu_Yunbin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 315710.0 | Mark_Oliphant | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1015331.0 | Bat_bomb | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1261101.0 | Oliver_Wendell_Jones | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.6618104e7 | The_Manhattan_Projects | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 342826.0 | 1940_in_science | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1083070.0 | Arnold_Potts | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.349352e7 | USS_Bland | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8306543e7 | Jessie_Stevenson_Kovalenko_Medal | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 129652.0 | Oakwood,_Montgomery_County,_Ohio | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 401225.0 | The_City_on_the_Edge_of_Forever | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 507796.0 | HMS_Uganda_(66) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 518003.0 | USS_Searaven_(SS-196) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.602142e7 | Mari_Gorman | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.2115877e7 | Tsutomu_Yamaguchi | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.8061808e7 | William_L._Uanna | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.5554068e7 | Leonard_Peter_Schultz | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.926546e7 | Edwin_Flavell_(RAF_officer) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 116691.0 | North_Adams,_Massachusetts | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8826410.0 | USS_Braxton | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080915e7 | USS_Carteret_(APA-70) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.0981692e7 | Ted_Doyle | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 60540.0 | Leslie_Groves | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 326834.0 | Patrick_Blackett | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 441642.0 | Operation_Grapple | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2391828.0 | Claude_Eatherly | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4884462.0 | Hammer_&_Sickle | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8339779.0 | Breeds_There_a_Man...? | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.3446366e7 | Ukrainians_in_Russia | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4049029e7 | George_Koval | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080852e7 | USS_Briscoe_(APA-65) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.5899369e7 | Robie_Macauley | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.3916699e7 | Strategic_Computing_Initiative | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 6.4011351e7 | Discovery_of_nuclear_fission | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 31922.0 | University_of_California,_Berkeley | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 102915.0 | Jack_Parsons | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 712716.0 | NERVA | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 730450.0 | Deutsche_Physik | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2252464.0 | Alexander_Scourby | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2594225.0 | Samuel_King_Allison | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2599915.0 | Leonid_Kvasnikov | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 8080591.0 | List_of_World_War_II_military_operations | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.0246099e7 | United_Nations_Security_Council_Resolution_1747 | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.1561403e7 | USS_Pelican_(AMS-32) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4080997e7 | USS_Fallon_(APA-81) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 363992.0 | 1950s_in_film | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 416813.0 | University_of_Minnesota | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.4410346e7 | Max_Bodenstein | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.0785562e7 | Western_Military_Academy | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 5987775.0 | USS_Quartz_(IX-150) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 7852575.0 | Ernest_Titterton | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9210780.0 | List_of_The_Six_Million_Dollar_Man_episodes | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 9938573.0 | Isaak_Kikoin | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1.8985287e7 | Culture_of_the_United_States | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.5670368e7 | List_of_Sigma_Nu_brothers | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 924954.0 | The_Heroes_of_Telemark | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 326225.0 | Technology_during_World_War_II | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1109514.0 | USS_Trepang_(SS-412) | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 1822400.0 | List_of_American_University_people | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 2.6255671e7 | Moshi_Monsters | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 3.5802855e7 | Properties_of_metals,_metalloids_and_nonmetals | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 4.5461344e7 | Eric_Burhop | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
| 244749.0 | Chelyabinsk | 1238.0 | Atomic_bomb | 21785.0 | Nuclear_weapon |
Now we create our new table of edges, where we drop all edges to redirects and add in the direct edges instead.
twoStepRedirects.createOrReplaceTempView("twoStepRedirects")
SELECT enwiki_graph_edges.src,
enwiki_graph_edges.src_title,
enwiki_graph_edges.dst,
enwiki_graph_edges.dst_title,
0 AS shortenedRedirect
FROM enwiki_graph_edges INNER JOIN enwiki_page ON enwiki_page.page_id = enwiki_graph_edges.dst
WHERE enwiki_page.page_is_redirect = 0
UNION ALL
SELECT twoStepRedirects.artA AS src,
twoStepRedirects.artA_title AS src_title,
twoStepRedirects.artC AS dst,
twoStepRedirects.artC_title AS dst_title,
1 AS shortenedRedirect
FROM twoStepRedirects
| src | src_title | dst | dst_title |
|---|---|---|---|
| 3.0322746e7 | General_Staff_of_Azerbaijani_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8849049e7 | Aşağı_Ağcakənd | 1088.0 | Azerbaijani_Armed_Forces |
| 412390.0 | Administrative_divisions_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.2576829e7 | Agriculture_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6041396e7 | Namig_Islamzadeh | 1088.0 | Azerbaijani_Armed_Forces |
| 5.7836785e7 | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan | 1088.0 | Azerbaijani_Armed_Forces |
| 31730.0 | British_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 31861.0 | Armed_Forces_of_the_Republic_of_Uzbekistan | 1088.0 | Azerbaijani_Armed_Forces |
| 67538.0 | Australian_Defence_Force | 1088.0 | Azerbaijani_Armed_Forces |
| 1492872.0 | Qakh_District | 1088.0 | Azerbaijani_Armed_Forces |
| 3.2945088e7 | Red_Army_invasion_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 4526380.0 | Ministry_of_National_Security_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.0314065e7 | Najmeddin_Sadikov | 1088.0 | Azerbaijani_Armed_Forces |
| 1.1447628e7 | Abkhazian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0187921e7 | Samir_Kachayev | 1088.0 | Azerbaijani_Armed_Forces |
| 12065.0 | Defence_Forces_of_Georgia | 1088.0 | Azerbaijani_Armed_Forces |
| 5731277.0 | Fauna_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 17769.0 | Latvian_National_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9230903e7 | Yevgeny_Karlov | 1088.0 | Azerbaijani_Armed_Forces |
| 12126.0 | Military_of_Greenland | 1088.0 | Azerbaijani_Armed_Forces |
| 2.1490379e7 | Rail_Rzayev | 1088.0 | Azerbaijani_Armed_Forces |
| 288188.0 | Bundeswehr | 1088.0 | Azerbaijani_Armed_Forces |
| 2.4777268e7 | Armed_Forces_of_Transnistria | 1088.0 | Azerbaijani_Armed_Forces |
| 1.2339349e7 | Architecture_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 30116.0 | Armed_Forces_of_the_Republic_of_Tajikistan | 1088.0 | Azerbaijani_Armed_Forces |
| 69007.0 | Military_of_Bhutan | 1088.0 | Azerbaijani_Armed_Forces |
| 2867590.0 | Royal_Cambodian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6040932.0 | Security_Forces_Command | 1088.0 | Azerbaijani_Armed_Forces |
| 5.5095974e7 | Healthcare_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.2197952e7 | List_of_Azerbaijani_flags | 1088.0 | Azerbaijani_Armed_Forces |
| 3.051115e7 | Azerbaijani_Flag_Order | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5910706e7 | Zafar_Order | 1088.0 | Azerbaijani_Armed_Forces |
| 5949758.0 | Land_mine_situation_in_Nagorno-Karabakh | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5625767e7 | 2020_Ghazanchetsots_Cathedral_shelling | 1088.0 | Azerbaijani_Armed_Forces |
| 1492928.0 | Tovuz_District | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8849026e7 | Gülüstan,_Goranboy | 1088.0 | Azerbaijani_Armed_Forces |
| 3.5079877e7 | Azerbaijani_traditional_clothing | 1088.0 | Azerbaijani_Armed_Forces |
| 5.7994574e7 | Military_Band_Service_of_the_Armed_Forces_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.013264e7 | 1920_Ganja_revolt | 1088.0 | Azerbaijani_Armed_Forces |
| 2.2612236e7 | 2008_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 7.1744547e7 | September_2022_Armenia–Azerbaijan_clashes | 1088.0 | Azerbaijani_Armed_Forces |
| 2.4315452e7 | List_of_people_from_Baku | 1088.0 | Azerbaijani_Armed_Forces |
| 5.6000989e7 | Tsarist_officers_in_the_Red_Army | 1088.0 | Azerbaijani_Armed_Forces |
| 161087.0 | Timor_Leste_Defence_Force | 1088.0 | Azerbaijani_Armed_Forces |
| 27346.0 | Slovenian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1.1169023e7 | Ministry_of_Defence_Industry_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.9331755e7 | Task_Force_ALBA | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9607179e7 | Fakhraddin_Najafov | 1088.0 | Azerbaijani_Armed_Forces |
| 34252.0 | Republic_of_Yemen_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1478175.0 | Public_holidays_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 32425.0 | Military_in_Vatican_City | 1088.0 | Azerbaijani_Armed_Forces |
| 67639.0 | Politics_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1274140.0 | Islam_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6499801e7 | Tarlan_Aliyarbayov | 1088.0 | Azerbaijani_Armed_Forces |
| 3.3284721e7 | List_of_massacres_of_Armenians | 1088.0 | Azerbaijani_Armed_Forces |
| 5.7265128e7 | Baykar | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5605916e7 | 2020_bombardment_of_Stepanakert | 1088.0 | Azerbaijani_Armed_Forces |
| 914180.0 | Stepanakert | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6168931e7 | Marine_Infantry_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.572327e7 | EXTRA_artillery_rocket_system | 1088.0 | Azerbaijani_Armed_Forces |
| 39237.0 | Israel_Defense_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7127135e7 | Commander_of_the_Navy_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 10724.0 | French_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 19269.0 | Public_Services_(Monaco) | 1088.0 | Azerbaijani_Armed_Forces |
| 27468.0 | Swiss_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 343356.0 | List_of_cities_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.7721373e7 | Medicine_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.5829912e7 | Natural_resources_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 32384.0 | People's_Army_of_Vietnam | 1088.0 | Azerbaijani_Armed_Forces |
| 21263.0 | Korean_People's_Army | 1088.0 | Azerbaijani_Armed_Forces |
| 3206857.0 | Religion_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 4.2626991e7 | Commission_on_Combating_Corruption | 1088.0 | Azerbaijani_Armed_Forces |
| 6.614052e7 | Karim_Valiyev | 1088.0 | Azerbaijani_Armed_Forces |
| 6.3734783e7 | Azerbaijan_in_antiquity | 1088.0 | Azerbaijani_Armed_Forces |
| 562798.0 | Defence_Forces_(Ireland) | 1088.0 | Azerbaijani_Armed_Forces |
| 1.7888556e7 | Corps_of_drums | 1088.0 | Azerbaijani_Armed_Forces |
| 6.022641e7 | Beyler_Agayev | 1088.0 | Azerbaijani_Armed_Forces |
| 10715.0 | Finnish_Defence_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 30136.0 | Royal_Thai_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 2.9435352e7 | Yavar_Jamalov | 1088.0 | Azerbaijani_Armed_Forces |
| 36398.0 | 2020s | 1088.0 | Azerbaijani_Armed_Forces |
| 6.57206e7 | STM_Kargu | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5805089e7 | 2020–2021_Armenian_protests | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8918691e7 | Farukh | 1088.0 | Azerbaijani_Armed_Forces |
| 3.8023365e7 | List_of_Azerbaijani_generals | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6359386e7 | Subhan_Jabrayilov | 1088.0 | Azerbaijani_Armed_Forces |
| 7311197.0 | Azerbaijan–Turkey_relations | 1088.0 | Azerbaijani_Armed_Forces |
| 2.2469823e7 | Azerbaijani_peacekeeping_forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.1609086e7 | Bronze_and_Iron_Age_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.7911049e7 | Ministry_of_Defence_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8119649e7 | Special_Purpose_Police_Unit | 1088.0 | Azerbaijani_Armed_Forces |
| 4.3807767e7 | Ilyas_Ismayilli | 1088.0 | Azerbaijani_Armed_Forces |
| 1081.0 | Economy_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3537.0 | Armed_Forces_of_Belarus | 1088.0 | Azerbaijani_Armed_Forces |
| 698454.0 | Azerbaijanis | 1088.0 | Azerbaijani_Armed_Forces |
| 4788086.0 | Azerbaijan_Medical_University | 1088.0 | Azerbaijani_Armed_Forces |
| 6.235848e7 | Air_and_Coastal_Defense_Command | 1088.0 | Azerbaijani_Armed_Forces |
| 6.3900274e7 | Shushi_Liberation_Day | 1088.0 | Azerbaijani_Armed_Forces |
| 401606.0 | Index_of_Azerbaijan-related_articles | 1088.0 | Azerbaijani_Armed_Forces |
| 4941803.0 | Azerbaijani_Navy | 1088.0 | Azerbaijani_Armed_Forces |
| 6.3098671e7 | Poverty_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 7.1288138e7 | History_of_the_Azerbaijani_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 19115.0 | Malaysian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 309778.0 | Music_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5699078e7 | Armenia–Iraq_relations | 1088.0 | Azerbaijani_Armed_Forces |
| 58145.0 | Cossacks | 1088.0 | Azerbaijani_Armed_Forces |
| 68951.0 | Belgian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.2357499e7 | Royal_Thai_Naval_Air_Division | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6185091e7 | 4th_Army_Corps_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 4116970.0 | Central_Bank_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 4380486.0 | Armenian–Tatar_massacres_of_1905–1907 | 1088.0 | Azerbaijani_Armed_Forces |
| 6415919.0 | Maraga_massacre | 1088.0 | Azerbaijani_Armed_Forces |
| 7171338.0 | Indian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 2.339895e7 | Romanian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0017617e7 | Military_parades_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2693748.0 | December_1993 | 1088.0 | Azerbaijani_Armed_Forces |
| 1.0927351e7 | Azerbaijani_National_Guard | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5514952e7 | Hikmat_Mirzayev | 1088.0 | Azerbaijani_Armed_Forces |
| 40196.0 | Transport_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1074631.0 | Common_Security_and_Defence_Policy | 1088.0 | Azerbaijani_Armed_Forces |
| 8626193.0 | Gurgen_Dalibaltayan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8933221e7 | Royal_Brunei_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 2.3408142e7 | Sri_Lanka_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5921759e7 | Babak_Samidli | 1088.0 | Azerbaijani_Armed_Forces |
| 3197492.0 | Rovshan_Javadov | 1088.0 | Azerbaijani_Armed_Forces |
| 6922486.0 | Extreme_points_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.614834e7 | Freedom_of_religion_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.9079143e7 | Armed_Forces_of_South_Ossetia | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8096514e7 | Jamshid_Nakhchivanski_Military_Lyceum | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8846257e7 | Cocuq_Mərcanlı | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8037194e7 | Dadash_Rzayev | 1088.0 | Azerbaijani_Armed_Forces |
| 4.41739e7 | Military_activity_of_the_Islamic_State | 1088.0 | Azerbaijani_Armed_Forces |
| 5.8641561e7 | Isgender_Aznaurov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6297159e7 | Ramiz_Gasimov | 1088.0 | Azerbaijani_Armed_Forces |
| 20394.0 | Tatmadaw | 1088.0 | Azerbaijani_Armed_Forces |
| 36397.0 | 2010s | 1088.0 | Azerbaijani_Armed_Forces |
| 27276.0 | Armed_Forces_of_Saudi_Arabia | 1088.0 | Azerbaijani_Armed_Forces |
| 1222633.0 | Royal_Thai_Navy | 1088.0 | Azerbaijani_Armed_Forces |
| 4695860.0 | Nagorno-Karabakh_conflict | 1088.0 | Azerbaijani_Armed_Forces |
| 2.5137672e7 | Energy_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6176862e7 | 2nd_Army_Corps_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 6.8603785e7 | Ramiz_Tahirov | 1088.0 | Azerbaijani_Armed_Forces |
| 40195.0 | Telecommunications_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.4890635e7 | 1993–2016_military_reforms_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5581.0 | Armed_Forces_of_Croatia | 1088.0 | Azerbaijani_Armed_Forces |
| 19248.0 | Armed_Forces_of_the_Republic_of_Moldova | 1088.0 | Azerbaijani_Armed_Forces |
| 2.1864529e7 | Aeronautics_Defense_Orbiter | 1088.0 | Azerbaijani_Armed_Forces |
| 6.641496e7 | Babak_Alakbarov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.8233981e7 | 2020_bombardment_of_Martuni | 1088.0 | Azerbaijani_Armed_Forces |
| 21162.0 | Netherlands_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 5.8621677e7 | Naig_Yusifov | 1088.0 | Azerbaijani_Armed_Forces |
| 5366487.0 | Human_rights_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 9519693.0 | Participants_in_Operation_Enduring_Freedom | 1088.0 | Azerbaijani_Armed_Forces |
| 2.3916399e7 | Sport_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.4496251e7 | Barak_8 | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7243383e7 | Intigam_Asgarli | 1088.0 | Azerbaijani_Armed_Forces |
| 19279.0 | Mongolian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 669244.0 | War_college | 1088.0 | Azerbaijani_Armed_Forces |
| 1.328078e7 | Arayik_Harutyunyan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.1170719e7 | Azerbaijani_Army_100th_anniversary_medal | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5848493e7 | Samir_Safarov | 1088.0 | Azerbaijani_Armed_Forces |
| 27479.0 | Syrian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1.3427826e7 | Cabinet_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8846258e7 | Şəybəy | 1088.0 | Azerbaijani_Armed_Forces |
| 3.7014386e7 | Talish-Mughan_culture | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6328752e7 | 2020_Azerbaijani_protests | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6677371e7 | Operation_Kalbajar | 1088.0 | Azerbaijani_Armed_Forces |
| 7.1844164e7 | Death_of_Anush_Apetyan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.1447694e7 | List_of_companies_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.1659771e7 | Military_history_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 4.0153898e7 | Polish_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 4.2424746e7 | Rasul_Chunayev | 1088.0 | Azerbaijani_Armed_Forces |
| 6.767076e7 | Tofig_Aghahuseynov | 1088.0 | Azerbaijani_Armed_Forces |
| 1230371.0 | Royal_Thai_Army | 1088.0 | Azerbaijani_Armed_Forces |
| 1.5721449e7 | Azerbaijan–European_Union_relations | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5743443e7 | Yashar_Hasanov | 1088.0 | Azerbaijani_Armed_Forces |
| 291026.0 | List_of_battles_in_the_21st_century | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8918966e7 | Hadrut | 1088.0 | Azerbaijani_Armed_Forces |
| 1.3634062e7 | Constitution_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.2503385e7 | Armed_Forces_of_the_Republic_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.035997e7 | Hidayat_Rustamov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5922276e7 | Madagiz_offensive | 1088.0 | Azerbaijani_Armed_Forces |
| 4318954.0 | Azerbaijani_Air_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1.716738e7 | Azerbaijan_Military | 1088.0 | Azerbaijani_Armed_Forces |
| 5.3412468e7 | Military_ranks_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.6759971e7 | Conscription_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6279185e7 | Anar_Aliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 2.3269917e7 | Military_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.836273e7 | Garachay | 1088.0 | Azerbaijani_Armed_Forces |
| 5.415509e7 | State_Service_for_Mobilization_and_Conscription_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9387862e7 | Yavar_Aliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 6.579868e7 | 2020_Russian_Mil_Mi-24_shootdown | 1088.0 | Azerbaijani_Armed_Forces |
| 339643.0 | Flag_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5424688.0 | Jordanian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 5650950.0 | Zoroastrianism_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.1353482e7 | Albert_Agarunov | 1088.0 | Azerbaijani_Armed_Forces |
| 4.3454993e7 | Armed_forces_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1151523.0 | Azerbaijani_manat | 1088.0 | Azerbaijani_Armed_Forces |
| 5444617.0 | Armed_Forces_of_Montenegro | 1088.0 | Azerbaijani_Armed_Forces |
| 8038.0 | Danish_Defence | 1088.0 | Azerbaijani_Armed_Forces |
| 27335.0 | Slovak_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 544668.0 | Ismail_I | 1088.0 | Azerbaijani_Armed_Forces |
| 3.0135122e7 | Immigration_to_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.6193899e7 | 2018_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1097.0 | Armed_Forces_of_Armenia | 1088.0 | Azerbaijani_Armed_Forces |
| 7077806.0 | Orography_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0003869e7 | Elman_Huseynov | 1088.0 | Azerbaijani_Armed_Forces |
| 27256.0 | Sammarinese_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 67638.0 | Demographics_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5561994e7 | Zaur_Rzayev | 1088.0 | Azerbaijani_Armed_Forces |
| 1351138.0 | Elections_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.0825064e7 | Baháʼí_Faith_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 13431.0 | Hungarian_Defence_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 5122310.0 | March_Days | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8270723e7 | Sotk | 1088.0 | Azerbaijani_Armed_Forces |
| 6.883363e7 | Azerbaijan_in_the_Council_of_Europe | 1088.0 | Azerbaijani_Armed_Forces |
| 1322733.0 | Black_January | 1088.0 | Azerbaijani_Armed_Forces |
| 3.7897147e7 | National_symbols_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 25709.0 | Russian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5613218e7 | Casualties_of_the_2020_Nagorno-Karabakh_war | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6258543e7 | Zaur_Guliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 2.3575502e7 | Tourism_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.807413e7 | 2010_in_Europe | 1088.0 | Azerbaijani_Armed_Forces |
| 5.8483543e7 | Yusif_Akhundzade | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6101111e7 | Kanan_Seyidov | 1088.0 | Azerbaijani_Armed_Forces |
| 2.5131731e7 | Azerbaijani_Army | 1088.0 | Azerbaijani_Armed_Forces |
| 6.377299e7 | Pornography_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6040794e7 | Aghamir_Sultanov | 1088.0 | Azerbaijani_Armed_Forces |
| 2073962.0 | ASQ | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8882652e7 | Seysulan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.1634642e7 | Novruz_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.7958605e7 | Eldar_Mammadov | 1088.0 | Azerbaijani_Armed_Forces |
| 5.3929862e7 | Syrian_Special_Mission_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6221176e7 | Victory_Banner_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 19086.0 | Army_of_North_Macedonia | 1088.0 | Azerbaijani_Armed_Forces |
| 4016533.0 | National_Assembly_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 6.672858e7 | Shahin_Allahyarov | 1088.0 | Azerbaijani_Armed_Forces |
| 1.0927665e7 | Internal_Troops_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 7.059512e7 | Matin_Karimli | 1088.0 | Azerbaijani_Armed_Forces |
| 3674.0 | Bulgarian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 17779.0 | Lebanese_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 7370911.0 | History_of_the_Jews_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.6084354e7 | Women_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 4.000345e7 | 1995_Azerbaijani_coup_d'état_attempt | 1088.0 | Azerbaijani_Armed_Forces |
| 6.2009022e7 | Elshad_Akhadov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.4398676e7 | Azerbaijani_National_Army | 1088.0 | Azerbaijani_Armed_Forces |
| 1.2975707e7 | Safar_Abiyev | 1088.0 | Azerbaijani_Armed_Forces |
| 3.6945373e7 | Theatre_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.9601754e7 | Saudi_Arabian_Military_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 17837.0 | Luxembourg_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8271586e7 | Yeraskh | 1088.0 | Azerbaijani_Armed_Forces |
| 4.1427083e7 | 2014_in_aviation | 1088.0 | Azerbaijani_Armed_Forces |
| 16959.0 | Katyusha_rocket_launcher | 1088.0 | Azerbaijani_Armed_Forces |
| 1.7967625e7 | Mineral_industry_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.741215e7 | Military_Trophy_Park_(Baku) | 1088.0 | Azerbaijani_Armed_Forces |
| 5463765.0 | List_of_military_clothing_camouflage_patterns | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9101736e7 | Hafiz_Bakhshaliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9921988e7 | Metallurgy_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 14650.0 | Indonesian_National_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6389332.0 | Military_history_of_Scotland | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5986628e7 | Kazakh_sultanate | 1088.0 | Azerbaijani_Armed_Forces |
| 23369.0 | Pakistan_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 877164.0 | Arran_(Caucasus) | 1088.0 | Azerbaijani_Armed_Forces |
| 3.0321796e7 | Nuraddin_Sadigov | 1088.0 | Azerbaijani_Armed_Forces |
| 404448.0 | Azerbaijan_Soviet_Socialist_Republic | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6096694e7 | Ilham_Mehdiyev | 1088.0 | Azerbaijani_Armed_Forces |
| 5.0235358e7 | Kyaram_Sloyan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.863037e7 | Chingiz_Gurbanov | 1088.0 | Azerbaijani_Armed_Forces |
| 746.0 | Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 40207.0 | Albanian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 7150649.0 | Environmental_issues_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.0078221e7 | Elchin_Guliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 6.9847606e7 | Battle_of_Yalama | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9608408e7 | Igor_Vladimirovich_Makeyev | 1088.0 | Azerbaijani_Armed_Forces |
| 21330.0 | Nepalese_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.416948e7 | Elnur_M._Allahverdiyev | 1088.0 | Azerbaijani_Armed_Forces |
| 5844475.0 | Palestinian_National_Security_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6150419e7 | 3rd_Army_Corps_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 12116.0 | Hellenic_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1492937.0 | Khojavend_District | 1088.0 | Azerbaijani_Armed_Forces |
| 4941797.0 | Azerbaijani_Land_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6344907e7 | 811th_Lachin_Alpine_Rifle_Regiment | 1088.0 | Azerbaijani_Armed_Forces |
| 2.7172367e7 | Azerbaijani_folklore | 1088.0 | Azerbaijani_Armed_Forces |
| 6.4563867e7 | Polad_Hashimov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6101738e7 | Zaur_Nudiraliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 774820.0 | List_of_Azerbaijanis | 1088.0 | Azerbaijani_Armed_Forces |
| 3764215.0 | Prime_Minister_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8109579e7 | Jamshid_Nakhchivanski | 1088.0 | Azerbaijani_Armed_Forces |
| 4.2799055e7 | Armed_Forces_of_Ukraine | 1088.0 | Azerbaijani_Armed_Forces |
| 3.9315722e7 | Swisscoy | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5898802e7 | Gorkhmaz_Eyvazov | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8209104e7 | Mubariz_Ibrahimov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5787844e7 | Battle_of_Shusha_(2020) | 1088.0 | Azerbaijani_Armed_Forces |
| 2.1189576e7 | Azerbaijani_rug | 1088.0 | Azerbaijani_Armed_Forces |
| 7772957.0 | Christianity_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8846225e7 | Böyük_Mərcanlı | 1088.0 | Azerbaijani_Armed_Forces |
| 6.1912815e7 | \"90th_Anniversary_of_the_Armed_Forces_of_Azerbaijan_(1918–2008)\"_Medal | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6138118e7 | Haydar_Piriyev | 1088.0 | Azerbaijani_Armed_Forces |
| 2.0876674e7 | Rafael_Aghayev | 1088.0 | Azerbaijani_Armed_Forces |
| 2.3896354e7 | Christianity_in_the_21st_century | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8024426e7 | Mammadrafi_Mammadov | 1088.0 | Azerbaijani_Armed_Forces |
| 5.5843237e7 | Azerbaijan–NATO_relations | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5904472e7 | Ilgar_Mirzayev | 1088.0 | Azerbaijani_Armed_Forces |
| 14705.0 | Italian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 877182.0 | Shirvan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8017509e7 | Valeh_Barshadly | 1088.0 | Azerbaijani_Armed_Forces |
| 6.4718117e7 | List_of_modern_equipment_of_the_Azerbaijani_Air_Force | 1088.0 | Azerbaijani_Armed_Forces |
| 51387.0 | 2016 | 1088.0 | Azerbaijani_Armed_Forces |
| 2785204.0 | Japan_Self-Defense_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 8620021.0 | Australian_Defence_Organisation | 1088.0 | Azerbaijani_Armed_Forces |
| 1.4338552e7 | List_of_military_special_forces_units | 1088.0 | Azerbaijani_Armed_Forces |
| 5.8424551e7 | Valeh_Muslumov | 1088.0 | Azerbaijani_Armed_Forces |
| 806090.0 | Qara_Qoyunlu | 1088.0 | Azerbaijani_Armed_Forces |
| 5.224123e7 | Borders_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0459344e7 | Anatoly_Nikolayevich_Davidovich | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5948171e7 | 641st_Naval_Special_Operations_Brigade | 1088.0 | Azerbaijani_Armed_Forces |
| 1437631.0 | Armed_Forces_of_Malta | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8048362e7 | Shahin_Musayev | 1088.0 | Azerbaijani_Armed_Forces |
| 68932.0 | Bangladesh_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 3.035175e7 | Azerbaijani_military | 1088.0 | Azerbaijani_Armed_Forces |
| 5.0529206e7 | Robert_Abajyan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6305663e7 | Ramiz_Jafarov | 1088.0 | Azerbaijani_Armed_Forces |
| 4.8268769e7 | Day_of_Restoration_of_Independence_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6149221e7 | 1st_Army_Corps_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 16692.0 | Kuwait_Military_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 757750.0 | Norwegian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 5.357506e7 | Corruption_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3308803.0 | Goris | 1088.0 | Azerbaijani_Armed_Forces |
| 5639884.0 | Armenian–Azerbaijani_war_(1918–1920) | 1088.0 | Azerbaijani_Armed_Forces |
| 7095335.0 | Climate_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 8386163.0 | Armen_Sarkissian | 1088.0 | Azerbaijani_Armed_Forces |
| 1.0934404e7 | Wildlife_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0198993e7 | Nadir_Aliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 1.0500625e7 | Mikael_Harutyunyan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.6348707e7 | Military_of_England | 1088.0 | Azerbaijani_Armed_Forces |
| 3.2850702e7 | List_of_World_Heritage_Sites_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.9847781e7 | Battle_of_Kurdamir | 1088.0 | Azerbaijani_Armed_Forces |
| 2.3290269e7 | Istiglal_anti-materiel_rifle | 1088.0 | Azerbaijani_Armed_Forces |
| 5.0021902e7 | 2016_Nagorno-Karabakh_conflict | 1088.0 | Azerbaijani_Armed_Forces |
| 7.0696138e7 | Noyemberyan_District | 1088.0 | Azerbaijani_Armed_Forces |
| 25194.0 | Qatar_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 3.9626995e7 | Armasuisse | 1088.0 | Azerbaijani_Armed_Forces |
| 4.190231e7 | Special_Forces_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2152685.0 | Cypriot_National_Guard | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8846287e7 | Jabrayil | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7782661e7 | Zigana_(pistol) | 1088.0 | Azerbaijani_Armed_Forces |
| 6131588.0 | Petroleum_industry_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.6664048e7 | Dzhokhar_Dudayev_Battalion | 1088.0 | Azerbaijani_Armed_Forces |
| 6.1689141e7 | Agil_Mammadov_(soldier) | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7094659e7 | 701st_Motorized_Rifle_Brigade | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9633726e7 | Matlab_Guliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 4059749.0 | Artsakh_Defence_Army | 1088.0 | Azerbaijani_Armed_Forces |
| 4.9718489e7 | Samra_Rahimli | 1088.0 | Azerbaijani_Armed_Forces |
| 6.640573e7 | Faig_Gasimov | 1088.0 | Azerbaijani_Armed_Forces |
| 3.1126572e7 | Ibad_Huseynov | 1088.0 | Azerbaijani_Armed_Forces |
| 5.8506289e7 | Raguf_Orujov | 1088.0 | Azerbaijani_Armed_Forces |
| 16650.0 | Armed_Forces_of_the_Republic_of_Kazakhstan | 1088.0 | Azerbaijani_Armed_Forces |
| 7105996.0 | State_reserves_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2.6157272e7 | Azerbaijani_art | 1088.0 | Azerbaijani_Armed_Forces |
| 4.0389354e7 | ASAN_service | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5369178e7 | List_of_caves_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6229889e7 | Rustam_Gasparyan | 1088.0 | Azerbaijani_Armed_Forces |
| 956689.0 | Kura–Araxes_culture | 1088.0 | Azerbaijani_Armed_Forces |
| 1.0927815e7 | Caspian_Guard_Initiative | 1088.0 | Azerbaijani_Armed_Forces |
| 19076.0 | Macao_Garrison | 1088.0 | Azerbaijani_Armed_Forces |
| 30095.0 | Republic_of_China_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 7107998.0 | Bodies_of_water_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 7429325.0 | Hinduism_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5488239e7 | Hikmat_Hasanov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6065854e7 | Baku_Victory_Parade_of_2020 | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7101223e7 | Training_and_Education_Center_of_the_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6939602.0 | Battle_of_Shusha_(1992) | 1088.0 | Azerbaijani_Armed_Forces |
| 4.6847468e7 | Vali_bey_Yadigarov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5676927e7 | Aras_Valley_campaign | 1088.0 | Azerbaijani_Armed_Forces |
| 3.4024533e7 | Leyla-Tepe_culture | 1088.0 | Azerbaijani_Armed_Forces |
| 3.9653948e7 | List_of_years_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0555433e7 | War_College_of_the_Azerbaijani_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 6.4529517e7 | July_2020_Armenian–Azerbaijani_clashes | 1088.0 | Azerbaijani_Armed_Forces |
| 2.805727e7 | Chingiz_Ildyrym | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9606287e7 | Anvar_Arazov | 1088.0 | Azerbaijani_Armed_Forces |
| 3.9354729e7 | Red_Cross_service | 1088.0 | Azerbaijani_Armed_Forces |
| 1115368.0 | Maldives_National_Defence_Force | 1088.0 | Azerbaijani_Armed_Forces |
| 2251178.0 | Singapore_Armed_Forces_Best_Unit_Competition | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8013699e7 | Ministry_of_Internal_Affairs_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8027854e7 | Vahid_Musayev | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5431221e7 | 2020_Nagorno-Karabakh_war | 1088.0 | Azerbaijani_Armed_Forces |
| 877787.0 | Azerbaijani_literature | 1088.0 | Azerbaijani_Armed_Forces |
| 3.0335881e7 | Rufat_Amirov | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9232802e7 | Mazahir_Rustamov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6096991e7 | Zaur_Javanshir | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6286297e7 | Karam_Mustafayev | 1088.0 | Azerbaijani_Armed_Forces |
| 17760.0 | Lao_People's_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 408284.0 | List_of_political_parties_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.9338836e7 | Spiez_Laboratory | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0486441e7 | Kanan_Yusif-zada | 1088.0 | Azerbaijani_Armed_Forces |
| 7.0595126e7 | Shoragel_sultanate | 1088.0 | Azerbaijani_Armed_Forces |
| 2.1653069e7 | Geology_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5925026e7 | 2nd_Commando_Brigade_(Turkey) | 1088.0 | Azerbaijani_Armed_Forces |
| 5853.0 | Army_of_the_Czech_Republic | 1088.0 | Azerbaijani_Armed_Forces |
| 4363966.0 | History_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 56966.0 | Portuguese_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 30215.0 | Armed_Forces_of_Turkmenistan | 1088.0 | Azerbaijani_Armed_Forces |
| 104985.0 | List_of_events_named_massacres | 1088.0 | Azerbaijani_Armed_Forces |
| 6.3564471e7 | Deaths_in_June_1993 | 1088.0 | Azerbaijani_Armed_Forces |
| 6.4398883e7 | Nakhchivan_Garrison | 1088.0 | Azerbaijani_Armed_Forces |
| 3610.0 | Armed_Forces_of_Bosnia_and_Herzegovina | 1088.0 | Azerbaijani_Armed_Forces |
| 7388232.0 | MOIK_Baku | 1088.0 | Azerbaijani_Armed_Forces |
| 1.1273902e7 | Multi-National_Force_West | 1088.0 | Azerbaijani_Armed_Forces |
| 1.1670391e7 | Gabala_Radar_Station | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0131111e7 | Ruslan_Muradov | 1088.0 | Azerbaijani_Armed_Forces |
| 1.5860804e7 | Mass_media_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 4.4412408e7 | 2014_Armenian_Mil_Mi-24_shootdown | 1088.0 | Azerbaijani_Armed_Forces |
| 5.7728678e7 | 2018_Armenian–Azerbaijani_clashes | 1088.0 | Azerbaijani_Armed_Forces |
| 1.1197435e7 | Maciej_Sulkiewicz | 1088.0 | Azerbaijani_Armed_Forces |
| 3.0455197e7 | Khojaly–Gadabay_culture | 1088.0 | Azerbaijani_Armed_Forces |
| 192825.0 | Azerbaijani_language | 1088.0 | Azerbaijani_Armed_Forces |
| 1.4305018e7 | Islamic_Republic_of_Iran_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 2.4533988e7 | Allahverdi_Bagirov | 1088.0 | Azerbaijani_Armed_Forces |
| 2.5278391e7 | List_of_protected_areas_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 20182.0 | Military_history_of_Afghanistan | 1088.0 | Azerbaijani_Armed_Forces |
| 4.4075958e7 | Nabat_(film) | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7135415e7 | Commander_of_the_Air_Force_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7666835e7 | 2021–2022_Armenia–Azerbaijan_border_crisis | 1088.0 | Azerbaijani_Armed_Forces |
| 67658.0 | Bahrain_Defence_Force | 1088.0 | Azerbaijani_Armed_Forces |
| 6.4209873e7 | Shamshadil | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8846305e7 | Quycaq | 1088.0 | Azerbaijani_Armed_Forces |
| 6.2509106e7 | 2010s_in_political_history | 1088.0 | Azerbaijani_Armed_Forces |
| 5569221.0 | Shulaveri–Shomu_culture | 1088.0 | Azerbaijani_Armed_Forces |
| 6367906.0 | Azerbaijani_dances | 1088.0 | Azerbaijani_Armed_Forces |
| 1.6278429e7 | Outline_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.1362503e7 | Stone_Age_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.3805278e7 | 2013_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9357147e7 | Hikmet_Nazarli | 1088.0 | Azerbaijani_Armed_Forces |
| 1.2835793e7 | Azerbaijani_cuisine | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8084481e7 | Tahir_Aliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 2.9069593e7 | List_of_heads_of_state_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.9311132e7 | Swissint | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6545755e7 | Gunduz_Safarli | 1088.0 | Azerbaijani_Armed_Forces |
| 7015198.0 | LGBT_rights_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.6607024e7 | AF_Holding | 1088.0 | Azerbaijani_Armed_Forces |
| 5.938745e7 | Sergei_Senyuskin | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6404258e7 | Azerbaijani_Red_Army | 1088.0 | Azerbaijani_Armed_Forces |
| 938372.0 | President_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.7167197e7 | Azeri_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 5.7937775e7 | Aliyar_Aliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0392656e7 | Ilgar_Ismailov | 1088.0 | Azerbaijani_Armed_Forces |
| 1519005.0 | Sultan_of_Oman's_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 2.3797951e7 | List_of_countries_by_number_of_military_and_paramilitary_personnel | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6496992e7 | Fuzuli_International_Airport | 1088.0 | Azerbaijani_Armed_Forces |
| 4.3480308e7 | Media_freedom_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 23448.0 | Armed_Forces_of_the_Philippines | 1088.0 | Azerbaijani_Armed_Forces |
| 2697610.0 | Jermuk | 1088.0 | Azerbaijani_Armed_Forces |
| 17827.0 | Lithuanian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 462640.0 | Chief_of_staff | 1088.0 | Azerbaijani_Armed_Forces |
| 4.0503488e7 | List_of_equipment_of_the_Azerbaijani_Land_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 27027.0 | Republic_of_Korea_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 3.5450533e7 | Day_of_the_Armed_Forces_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0092014e7 | Deaths_in_March_1995 | 1088.0 | Azerbaijani_Armed_Forces |
| 16702.0 | Armed_Forces_of_the_Kyrgyz_Republic | 1088.0 | Azerbaijani_Armed_Forces |
| 30205.0 | Turkish_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 897352.0 | Singapore_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9597765e7 | Tahir_Hasanov | 1088.0 | Azerbaijani_Armed_Forces |
| 1.6569312e7 | Education_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 21133.0 | NATO | 1088.0 | Azerbaijani_Armed_Forces |
| 1.1356544e7 | Law_enforcement_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5815949e7 | Arif_Pasha | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6475944e7 | Khudayar_Yusifzade | 1088.0 | Azerbaijani_Armed_Forces |
| 1.2085342e7 | Khanates_of_the_Caucasus | 1088.0 | Azerbaijani_Armed_Forces |
| 1087.0 | Foreign_relations_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1622783.0 | Silk_Way_Airlines | 1088.0 | Azerbaijani_Armed_Forces |
| 2.031521e7 | Xudaverdili | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6356955e7 | Wind_Unit | 1088.0 | Azerbaijani_Armed_Forces |
| 6.9527474e7 | Armenian_prisoners_of_the_2020_Nagorno-Karabakh_war | 1088.0 | Azerbaijani_Armed_Forces |
| 51396.0 | 2020 | 1088.0 | Azerbaijani_Armed_Forces |
| 1986639.0 | Languages_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.9653787e7 | Caspian_Sea | 1088.0 | Azerbaijani_Armed_Forces |
| 3.3162574e7 | Abdulhamid_bey_Gaytabashi | 1088.0 | Azerbaijani_Armed_Forces |
| 5.8757841e7 | Rovshan_Rzayev | 1088.0 | Azerbaijani_Armed_Forces |
| 2071240.0 | Culture_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.6369933e7 | Orders,_decorations,_and_medals_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 4.8708538e7 | Azerbaijan_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 64586.0 | Defence_of_Iceland | 1088.0 | Azerbaijani_Armed_Forces |
| 7077602.0 | Environment_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 2409969.0 | Azerbaijan_Democratic_Republic | 1088.0 | Azerbaijani_Armed_Forces |
| 5.8423883e7 | Nofal_Guliyev | 1088.0 | Azerbaijani_Armed_Forces |
| 5.9661079e7 | Israfil_Shahverdiyev | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5686536e7 | Lachin_offensive | 1088.0 | Azerbaijani_Armed_Forces |
| 31841.0 | United_Arab_Emirates_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 7145213.0 | List_of_militaries_by_country | 1088.0 | Azerbaijani_Armed_Forces |
| 1.8917889e7 | Estonian_Defence_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 34669.0 | 1992 | 1088.0 | Azerbaijani_Armed_Forces |
| 4.1471871e7 | List_of_lakes_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 5427517.0 | Department_of_Defence_(Australia) | 1088.0 | Azerbaijani_Armed_Forces |
| 1.1776466e7 | Ethnic_minorities_in_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 3.8212231e7 | Drone_warfare | 1088.0 | Azerbaijani_Armed_Forces |
| 3932850.0 | Spanish_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 3.8072822e7 | June_1918 | 1088.0 | Azerbaijani_Armed_Forces |
| 1082.0 | Geography_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6016112e7 | Memorial_Day_(Azerbaijan) | 1088.0 | Azerbaijani_Armed_Forces |
| 67549.0 | Austrian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1.4836618e7 | TecSAR-1 | 1088.0 | Azerbaijani_Armed_Forces |
| 5.6408207e7 | Nshan_Topouzian | 1088.0 | Azerbaijani_Armed_Forces |
| 20311.0 | Military_academy | 1088.0 | Azerbaijani_Armed_Forces |
| 26895.0 | Swedish_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1.7167243e7 | Armed_Forces_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 8355037.0 | Vardenis | 1088.0 | Azerbaijani_Armed_Forces |
| 3.949525e7 | Timeline_of_modern_Armenian_history | 1088.0 | Azerbaijani_Armed_Forces |
| 6.3475155e7 | National_Army_of_the_Azerbaijan_Democratic_Republic | 1088.0 | Azerbaijani_Armed_Forces |
| 1.0254803e7 | Cinema_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 1.0927369e7 | State_Border_Service | 1088.0 | Azerbaijani_Armed_Forces |
| 2.9957491e7 | Altay_Mehdiyev | 1088.0 | Azerbaijani_Armed_Forces |
| 3.0524746e7 | Shah_Ismail_Order | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5459244e7 | Operation_Horadiz | 1088.0 | Azerbaijani_Armed_Forces |
| 381170.0 | Serbian_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 1100953.0 | Khachkar | 1088.0 | Azerbaijani_Armed_Forces |
| 7150805.0 | National_parks_of_Azerbaijan | 1088.0 | Azerbaijani_Armed_Forces |
| 6.2428178e7 | Vagif_Gurbanov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.4702581e7 | Medal_\"For_services_in_the_field_of_military_cooperation\" | 1088.0 | Azerbaijani_Armed_Forces |
| 6.5834115e7 | Fariz_Najafov | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7228635e7 | Rovshan_Akbarov | 1088.0 | Azerbaijani_Armed_Forces |
| 4627429.0 | Iraqi_Armed_Forces | 1088.0 | Azerbaijani_Armed_Forces |
| 3.309651e7 | Habib_Bey_Salimov | 1088.0 | Azerbaijani_Armed_Forces |
| 2.8046216e7 | Tajaddin_Mehdiyev | 1088.0 | Azerbaijani_Armed_Forces |
| 3.2636241e7 | Jebrail_uezd | 1088.0 | Azerbaijani_Armed_Forces |
| 3.8938602e7 | \"For_Faultless_Service\"_medal | 1088.0 | Azerbaijani_Armed_Forces |
| 6.6083612e7 | Tehran_Mansimov | 1088.0 | Azerbaijani_Armed_Forces |
| 5612659.0 | List_of_coups_and_coup_attempts | 1088.0 | Azerbaijani_Armed_Forces |
| 6.0544953e7 | Azerbaijan_Higher_Military_Academy | 1088.0 | Azerbaijani_Armed_Forces |
| 4.1723946e7 | List_of_aircraft_of_the_Royal_Thai_Air_Force | 1088.0 | Azerbaijani_Armed_Forces |
| 6.7122586e7 | 777th_Special_Forces_Regiment | 1088.0 | Azerbaijani_Armed_Forces |
| 743896.0 | Timeline_of_Western_philosophers | 1580.0 | Alcidamas |
| 1036228.0 | Alcidamas_of_Elaea | 1580.0 | Alcidamas |
| 1232997.0 | Oikonomos | 1580.0 | Alcidamas |
| 5.7442757e7 | Critheïs | 1580.0 | Alcidamas |
| 291170.0 | Pythagoreanism | 1580.0 | Alcidamas |
| 13633.0 | Homer | 1580.0 | Alcidamas |
| 2.2877693e7 | Orpheus | 1580.0 | Alcidamas |
| 4.747118e7 | Slavery_in_ancient_Greece | 1580.0 | Alcidamas |
| 3.5139015e7 | Alkidamas | 1580.0 | Alcidamas |
| 98394.0 | Gorgias | 1580.0 | Alcidamas |
| 472876.0 | List_of_ancient_Greeks | 1580.0 | Alcidamas |
| 1.3405274e7 | Aleus | 1580.0 | Alcidamas |
| 78976.0 | Telephus | 1580.0 | Alcidamas |
| 6.6441371e7 | Index_of_ancient_Greece-related_articles | 1580.0 | Alcidamas |
| 1965077.0 | Slavery_in_antiquity | 1580.0 | Alcidamas |
| 3476868.0 | Nauplius_(mythology) | 1580.0 | Alcidamas |
| 3.5139025e7 | Alkidamas_of_Elaea | 1580.0 | Alcidamas |
| 6.8859938e7 | List_of_editiones_principes_in_Greek | 1580.0 | Alcidamas |
| 1065085.0 | Chilon_of_Sparta | 1580.0 | Alcidamas |
| 1.5103874e7 | Contest_of_Homer_and_Hesiod | 1580.0 | Alcidamas |
| 6508591.0 | Auge | 1580.0 | Alcidamas |
| 13700.0 | Hesiod | 1580.0 | Alcidamas |
| 80585.0 | Aerope | 1580.0 | Alcidamas |
| 4854673.0 | List_of_Trojan_War_characters | 1580.0 | Alcidamas |
| 5.7293774e7 | List_of_pre-modern_Arab_scientists_and_scholars | 1645.0 | Ibn_al-Haytham |
| 1408.0 | Alcuin | 1645.0 | Ibn_al-Haytham |
| 95429.0 | Bonaventure | 1645.0 | Ibn_al-Haytham |
| 358484.0 | Girard_Desargues | 1645.0 | Ibn_al-Haytham |
| 1778440.0 | 'Ubayd_Allah_ibn_Bakhtishu | 1645.0 | Ibn_al-Haytham |
| 2364264.0 | Ibn_al-Rawandi | 1645.0 | Ibn_al-Haytham |
| 2848164.0 | Nur_ad-Din_al-Bitruji | 1645.0 | Ibn_al-Haytham |
| 1766908.0 | 'Ali_ibn_al-'Abbas_al-Majusi | 1645.0 | Ibn_al-Haytham |
| 3013733.0 | Ibn_Abi_Usaybi'a | 1645.0 | Ibn_al-Haytham |
| 3335321.0 | Shams_al-Din_al-Samarqandi | 1645.0 | Ibn_al-Haytham |
| 4072182.0 | What_the_Ancients_Did_for_Us | 1645.0 | Ibn_al-Haytham |
| 1.0712582e7 | Sinan_ibn_Thabit | 1645.0 | Ibn_al-Haytham |
| 3.1562331e7 | Al-Kashkari | 1645.0 | Ibn_al-Haytham |
| 735136.0 | Ibn_Zuhr | 1645.0 | Ibn_al-Haytham |
| 1.5309628e7 | Muhammad_Ali_Astarabadi | 1645.0 | Ibn_al-Haytham |
| 536739.0 | Avempace | 1645.0 | Ibn_al-Haytham |
| 6.6960791e7 | Ibn_Habib | 1645.0 | Ibn_al-Haytham |
| 34875.0 | 1000s_(decade) | 1645.0 | Ibn_al-Haytham |
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| 4617851.0 | Bab_(Shia_Islam) | 1645.0 | Ibn_al-Haytham |
| 2.8412183e7 | Projector | 1645.0 | Ibn_al-Haytham |
| 4.069588e7 | Ibn_al-Adami | 1645.0 | Ibn_al-Haytham |
| 9117159.0 | Sahl_ibn_Bishr | 1645.0 | Ibn_al-Haytham |
| 2.6185766e7 | Masawaih_al-Mardini | 1645.0 | Ibn_al-Haytham |
| 10606.0 | Factorial | 1645.0 | Ibn_al-Haytham |
| 2.1492554e7 | Anselm_of_Canterbury | 1645.0 | Ibn_al-Haytham |
| 4.7787936e7 | Schema_for_horizontal_dials | 1645.0 | Ibn_al-Haytham |
| 6.024167e7 | Abraham_of_Toledo | 1645.0 | Ibn_al-Haytham |
| 5962454.0 | Zij-i_Sultani | 1645.0 | Ibn_al-Haytham |
| 4.3756445e7 | Al-Isfizari | 1645.0 | Ibn_al-Haytham |
| 1.2775341e7 | Ibn_Al-Haytham | 1645.0 | Ibn_al-Haytham |
| 3.6143542e7 | Ibn_al-Majdi | 1645.0 | Ibn_al-Haytham |
| 2012352.0 | Allamah_Al-Hilli | 1645.0 | Ibn_al-Haytham |
| 2984836.0 | Ophthalmology_in_the_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 23670.0 | Perfect_number | 1645.0 | Ibn_al-Haytham |
| 73664.0 | Astrolabe | 1645.0 | Ibn_al-Haytham |
| 299138.0 | Giles_of_Rome | 1645.0 | Ibn_al-Haytham |
| 2042491.0 | Yuhanna_ibn_Bukhtishu | 1645.0 | Ibn_al-Haytham |
| 7325366.0 | Timeline_of_telescope_technology | 1645.0 | Ibn_al-Haytham |
| 2163566.0 | Nafi_ibn_al-Harith | 1645.0 | Ibn_al-Haytham |
| 2937325.0 | List_of_Muslim_theologians | 1645.0 | Ibn_al-Haytham |
| 2.5009227e7 | Abu_Ali_al-Hasan_ibn_al-Hasan_ibn_al-Haytham | 1645.0 | Ibn_al-Haytham |
| 2674.0 | Abd_al-Latif_al-Baghdadi | 1645.0 | Ibn_al-Haytham |
| 2.1208262e7 | Western_culture | 1645.0 | Ibn_al-Haytham |
| 6.2773262e7 | Sadr_al-Shari'a_al-Asghar | 1645.0 | Ibn_al-Haytham |
| 2042194.0 | Al-Nagawri | 1645.0 | Ibn_al-Haytham |
| 3304216.0 | Mathematics_in_the_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 2.2795725e7 | Al-Dakhwar | 1645.0 | Ibn_al-Haytham |
| 323592.0 | Nicolaus_Copernicus | 1645.0 | Ibn_al-Haytham |
| 1766735.0 | Al-Isfahani | 1645.0 | Ibn_al-Haytham |
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| 44328.0 | Ulugh_Beg | 1645.0 | Ibn_al-Haytham |
| 171177.0 | Early_Islamic_philosophy | 1645.0 | Ibn_al-Haytham |
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| 227323.0 | Wilson's_theorem | 1645.0 | Ibn_al-Haytham |
| 1822322.0 | Muhammad_ibn_Mahmud_Amuli | 1645.0 | Ibn_al-Haytham |
| 5290740.0 | Sa'ad_al-Dawla | 1645.0 | Ibn_al-Haytham |
| 1.9804384e7 | Maulana_Azad_Library | 1645.0 | Ibn_al-Haytham |
| 4.8407889e7 | The_Physicist | 1645.0 | Ibn_al-Haytham |
| 768566.0 | List_of_important_publications_in_physics | 1645.0 | Ibn_al-Haytham |
| 2.757938e7 | Ibn_al-Saffar | 1645.0 | Ibn_al-Haytham |
| 2.7579858e7 | Abu_al-Salt | 1645.0 | Ibn_al-Haytham |
| 3.9292005e7 | Ibn_Hindu | 1645.0 | Ibn_al-Haytham |
| 1782310.0 | Abu_Said_Gorgani | 1645.0 | Ibn_al-Haytham |
| 1.1091123e7 | Ibn_Jazla | 1645.0 | Ibn_al-Haytham |
| 3.4629603e7 | Shams_al-Din_al-Khafri | 1645.0 | Ibn_al-Haytham |
| 26833.0 | Scientific_method | 1645.0 | Ibn_al-Haytham |
| 27680.0 | Supernova | 1645.0 | Ibn_al-Haytham |
| 506138.0 | Ali_ibn_Sahl_Rabban_al-Tabari | 1645.0 | Ibn_al-Haytham |
| 958988.0 | History_of_astrology | 1645.0 | Ibn_al-Haytham |
| 5453536.0 | Zij-i_Ilkhani | 1645.0 | Ibn_al-Haytham |
| 34566.0 | 1040 | 1645.0 | Ibn_al-Haytham |
| 1613042.0 | Ibn_al_Haythen | 1645.0 | Ibn_al-Haytham |
| 1782585.0 | Jabril_ibn_Bukhtishu | 1645.0 | Ibn_al-Haytham |
| 2.1490957e7 | Thomas_Aquinas | 1645.0 | Ibn_al-Haytham |
| 22939.0 | Physics | 1645.0 | Ibn_al-Haytham |
| 146607.0 | Al-Ghazali | 1645.0 | Ibn_al-Haytham |
| 416776.0 | Timeline_of_algorithms | 1645.0 | Ibn_al-Haytham |
| 746117.0 | History_of_calculus | 1645.0 | Ibn_al-Haytham |
| 2.4924385e7 | Al_Hazen | 1645.0 | Ibn_al-Haytham |
| 1.4951467e7 | Ali_Qushji | 1645.0 | Ibn_al-Haytham |
| 4.8407877e7 | Al-Misri | 1645.0 | Ibn_al-Haytham |
| 5.3083061e7 | Abu_Ishaq_al-Kubunani | 1645.0 | Ibn_al-Haytham |
| 23289.0 | Persistence_of_vision | 1645.0 | Ibn_al-Haytham |
| 195684.0 | Boethius | 1645.0 | Ibn_al-Haytham |
| 1767004.0 | Abu_Mansur_Muwaffaq | 1645.0 | Ibn_al-Haytham |
| 5186903.0 | Tusi_couple | 1645.0 | Ibn_al-Haytham |
| 1492381.0 | Ibn_Al-Thahabi | 1645.0 | Ibn_al-Haytham |
| 1840730.0 | Muhammad_ibn_Yusuf_al-Harawi | 1645.0 | Ibn_al-Haytham |
| 1104605.0 | Isaac_Israeli_ben_Solomon | 1645.0 | Ibn_al-Haytham |
| 1822442.0 | Aqsara'i | 1645.0 | Ibn_al-Haytham |
| 1.9217647e7 | Abul_Qasim_ibn_Mohammed_al-Ghassani | 1645.0 | Ibn_al-Haytham |
| 7898478.0 | Jamal_ad-Din_Bukhari | 1645.0 | Ibn_al-Haytham |
| 8128856.0 | Eye_movement_in_reading | 1645.0 | Ibn_al-Haytham |
| 8878908.0 | De_Gradibus | 1645.0 | Ibn_al-Haytham |
| 23604.0 | Photography | 1645.0 | Ibn_al-Haytham |
| 34645.0 | 11th_century | 1645.0 | Ibn_al-Haytham |
| 593659.0 | Nicole_Oresme | 1645.0 | Ibn_al-Haytham |
| 2042316.0 | Nurbakhshi | 1645.0 | Ibn_al-Haytham |
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| 1841939.0 | Muhammad_ibn_Yusuf_al-Ilaqi | 1645.0 | Ibn_al-Haytham |
| 1855317.0 | Theodoric_of_Freiberg | 1645.0 | Ibn_al-Haytham |
| 3.2142292e7 | Ibrahim_ibn_Baks | 1645.0 | Ibn_al-Haytham |
| 1485.0 | Alain_de_Lille | 1645.0 | Ibn_al-Haytham |
| 191295.0 | Aswan_Dam | 1645.0 | Ibn_al-Haytham |
| 4849167.0 | Brethren_of_Purity | 1645.0 | Ibn_al-Haytham |
| 5.210954e7 | Al-Tamimi,_the_physician | 1645.0 | Ibn_al-Haytham |
| 715596.0 | Ptolemy_(name) | 1645.0 | Ibn_al-Haytham |
| 2041938.0 | Mansur_ibn_Ilyas | 1645.0 | Ibn_al-Haytham |
| 3.3642424e7 | Nasir_al-Din_al-Tusi | 1645.0 | Ibn_al-Haytham |
| 5.3090488e7 | Haseb-i_Tabari | 1645.0 | Ibn_al-Haytham |
| 145242.0 | The_Canon_of_Medicine | 1645.0 | Ibn_al-Haytham |
| 4644443.0 | Al-Hazen | 1645.0 | Ibn_al-Haytham |
| 1.0553199e7 | Jamshīd_al-Kāshī | 1645.0 | Ibn_al-Haytham |
| 1.0714039e7 | Ibn_al-Haitham | 1645.0 | Ibn_al-Haytham |
| 33426.0 | Wave–particle_duality | 1645.0 | Ibn_al-Haytham |
| 1731800.0 | Triquetrum_(astronomy) | 1645.0 | Ibn_al-Haytham |
| 1.327905e7 | Ibn_Butlan | 1645.0 | Ibn_al-Haytham |
| 4.6587853e7 | Khafi_Alayee | 1645.0 | Ibn_al-Haytham |
| 5.1317367e7 | Nastulus | 1645.0 | Ibn_al-Haytham |
| 1830204.0 | List_of_Iraqis | 1645.0 | Ibn_al-Haytham |
| 2479369.0 | Abu_Kamil | 1645.0 | Ibn_al-Haytham |
| 6.5419249e7 | Ali_ibn_Khalaf | 1645.0 | Ibn_al-Haytham |
| 1793021.0 | Ahmad_ibn_Farrokh | 1645.0 | Ibn_al-Haytham |
| 1917134.0 | Sultan_Ali_Khorasani | 1645.0 | Ibn_al-Haytham |
| 3.4504565e7 | Crowding | 1645.0 | Ibn_al-Haytham |
| 3.5767797e7 | Nizam_al-Din_al-Nisapuri | 1645.0 | Ibn_al-Haytham |
| 4.2421061e7 | Hiding_in_the_Light | 1645.0 | Ibn_al-Haytham |
| 3.8261744e7 | 'Abd_al-'Aziz_al-Wafa'i | 1645.0 | Ibn_al-Haytham |
| 5.7398059e7 | Najm_al‐Din_al‐Misri | 1645.0 | Ibn_al-Haytham |
| 81609.0 | Pinhole_camera | 1645.0 | Ibn_al-Haytham |
| 4.7149691e7 | Yusuf_al-Khuri | 1645.0 | Ibn_al-Haytham |
| 5.4285532e7 | Ibn_al-Samh | 1645.0 | Ibn_al-Haytham |
| 2.755431e7 | Abu_Jafar_ibn_Harun_al-Turjali | 1645.0 | Ibn_al-Haytham |
| 1285108.0 | Ibn_al-Shatir | 1645.0 | Ibn_al-Haytham |
| 2.3247759e7 | List_of_philosophers_of_science | 1645.0 | Ibn_al-Haytham |
| 29544.0 | Scientific_Revolution | 1645.0 | Ibn_al-Haytham |
| 2042205.0 | Najib_ad-Din_Samarqandi | 1645.0 | Ibn_al-Haytham |
| 1.8973446e7 | Geometry | 1645.0 | Ibn_al-Haytham |
| 83754.0 | Geocentric_model | 1645.0 | Ibn_al-Haytham |
| 1.2742387e7 | Al_Hazan | 1645.0 | Ibn_al-Haytham |
| 380406.0 | Comparative_psychology | 1645.0 | Ibn_al-Haytham |
| 4.0695634e7 | Al-Adami | 1645.0 | Ibn_al-Haytham |
| 272065.0 | Al-Kindi | 1645.0 | Ibn_al-Haytham |
| 1768580.0 | Sharaf_al-Din_al-Tusi | 1645.0 | Ibn_al-Haytham |
| 2600270.0 | Timeline_of_Polish_science_and_technology | 1645.0 | Ibn_al-Haytham |
| 8406253.0 | Astrology_in_the_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 1.0279126e7 | Aristotelian_physics | 1645.0 | Ibn_al-Haytham |
| 1.3861753e7 | Said_al-Andalusi | 1645.0 | Ibn_al-Haytham |
| 173378.0 | Anselm_of_Laon | 1645.0 | Ibn_al-Haytham |
| 8064048.0 | Abu'l-Hasan_ibn_Ali_al-Qalasadi | 1645.0 | Ibn_al-Haytham |
| 56153.0 | Ophthalmology | 1645.0 | Ibn_al-Haytham |
| 246612.0 | Iraqi_dinar | 1645.0 | Ibn_al-Haytham |
| 1741105.0 | Muḥammad_ibn_Ibrāhīm_al-Fazārī | 1645.0 | Ibn_al-Haytham |
| 6.5164199e7 | Abd_El_Razzaq_Al-Jazaïri | 1645.0 | Ibn_al-Haytham |
| 18031.0 | Leon_Battista_Alberti | 1645.0 | Ibn_al-Haytham |
| 38737.0 | Cosmos | 1645.0 | Ibn_al-Haytham |
| 1.0029198e7 | List_of_structural_engineers | 1645.0 | Ibn_al-Haytham |
| 7256533.0 | Islamic_attitudes_towards_science | 1645.0 | Ibn_al-Haytham |
| 2.1800807e7 | Zakhireye_Khwarazmshahi | 1645.0 | Ibn_al-Haytham |
| 3.2144014e7 | Ibn_Hamza_al-Maghribi | 1645.0 | Ibn_al-Haytham |
| 5.1310315e7 | Al-ʻIjliyyah | 1645.0 | Ibn_al-Haytham |
| 982540.0 | Taqi_ad-Din_Muhammad_ibn_Ma'ruf | 1645.0 | Ibn_al-Haytham |
| 19445.0 | Maimonides | 1645.0 | Ibn_al-Haytham |
| 267542.0 | Science_in_the_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 271979.0 | Abu_Hanifa_Dinawari | 1645.0 | Ibn_al-Haytham |
| 1782879.0 | Shapur_ibn_Sahl | 1645.0 | Ibn_al-Haytham |
| 3663691.0 | Hering's_law_of_equal_innervation | 1645.0 | Ibn_al-Haytham |
| 2.3649689e7 | Shadow_square | 1645.0 | Ibn_al-Haytham |
| 50416.0 | Differential_calculus | 1645.0 | Ibn_al-Haytham |
| 74844.0 | Glasses | 1645.0 | Ibn_al-Haytham |
| 1766793.0 | Al-Sijzi | 1645.0 | Ibn_al-Haytham |
| 1.3632955e7 | Yusuf_ibn_Ismail_al-Kutubi | 1645.0 | Ibn_al-Haytham |
| 36281.0 | 1088 | 1645.0 | Ibn_al-Haytham |
| 477316.0 | List_of_theoretical_physicists | 1645.0 | Ibn_al-Haytham |
| 1.0444102e7 | Science_and_technology_of_the_Song_dynasty | 1645.0 | Ibn_al-Haytham |
| 3.3461884e7 | Ishaq_ibn_Hunayn | 1645.0 | Ibn_al-Haytham |
| 3.6102244e7 | Sharaf_al-Zaman_al-Marwazi | 1645.0 | Ibn_al-Haytham |
| 4.6781843e7 | Abu_Bakr_Rabee_Ibn_Ahmad_Al-Akhawyni_Bokhari | 1645.0 | Ibn_al-Haytham |
| 3035257.0 | Masarjawaih | 1645.0 | Ibn_al-Haytham |
| 3.5777337e7 | Cosmos:_A_Spacetime_Odyssey | 1645.0 | Ibn_al-Haytham |
| 26700.0 | Science | 1645.0 | Ibn_al-Haytham |
| 2042154.0 | Shaykh_Muhammad_ibn_Thaleb | 1645.0 | Ibn_al-Haytham |
| 2596746.0 | Newton_disc | 1645.0 | Ibn_al-Haytham |
| 2.4712631e7 | Abu_Ali_al-Haitam | 1645.0 | Ibn_al-Haytham |
| 2.4893445e7 | Book_of_the_Ten_Treatises_of_the_Eye | 1645.0 | Ibn_al-Haytham |
| 673618.0 | Michael_Scot | 1645.0 | Ibn_al-Haytham |
| 6328738.0 | Ibn_Mu'adh_al-Jayyani | 1645.0 | Ibn_al-Haytham |
| 1.0137505e7 | List_of_people_on_banknotes | 1645.0 | Ibn_al-Haytham |
| 4.9712277e7 | Al-Ruhawi | 1645.0 | Ibn_al-Haytham |
| 2185.0 | Arabs | 1645.0 | Ibn_al-Haytham |
| 6037917.0 | Islam | 1645.0 | Ibn_al-Haytham |
| 94721.0 | Robert_Grosseteste | 1645.0 | Ibn_al-Haytham |
| 1367068.0 | Judeo-Islamic_philosophies_(800–1400) | 1645.0 | Ibn_al-Haytham |
| 11953.0 | History_of_geometry | 1645.0 | Ibn_al-Haytham |
| 32502.0 | Vacuum | 1645.0 | Ibn_al-Haytham |
| 323646.0 | Wilson_prime | 1645.0 | Ibn_al-Haytham |
| 1997.0 | Algebraic_geometry | 1645.0 | Ibn_al-Haytham |
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| 5.3090036e7 | Al-Wabkanawi | 1645.0 | Ibn_al-Haytham |
| 1086231.0 | Al-Abbās_ibn_Said_al-Jawharī | 1645.0 | Ibn_al-Haytham |
| 3.0274023e7 | Aswan_Low_Dam | 1645.0 | Ibn_al-Haytham |
| 4.6527701e7 | Treatise_on_Light | 1645.0 | Ibn_al-Haytham |
| 353215.0 | Al-Zahrawi | 1645.0 | Ibn_al-Haytham |
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| 3.8674159e7 | Dawud_al-Antaki | 1645.0 | Ibn_al-Haytham |
| 519400.0 | Ibn_Al-Haitham | 1645.0 | Ibn_al-Haytham |
| 1782729.0 | Al-Mahani | 1645.0 | Ibn_al-Haytham |
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| 9320723.0 | Islam_in_England | 1645.0 | Ibn_al-Haytham |
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| 86820.0 | Khalid_ibn_Abd_al‐Malik_al‐Marwarrudhi | 1645.0 | Ibn_al-Haytham |
| 2341370.0 | List_of_motifs_on_banknotes | 1645.0 | Ibn_al-Haytham |
| 2.5009201e7 | Abū_ʿAlī_al-Ḥasan_ibn_al-Ḥasan_ibn_al-Haytham | 1645.0 | Ibn_al-Haytham |
| 982595.0 | Constantinople_observatory_of_Taqi_ad-Din | 1645.0 | Ibn_al-Haytham |
| 4385475.0 | Ancient_Greek_astronomy | 1645.0 | Ibn_al-Haytham |
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| 1.9594028e7 | Theoretical_physics | 1645.0 | Ibn_al-Haytham |
| 971922.0 | Contrast_effect | 1645.0 | Ibn_al-Haytham |
| 2.0936837e7 | Anatomy_Charts_of_the_Arabs | 1645.0 | Ibn_al-Haytham |
| 52202.0 | Magic_square | 1645.0 | Ibn_al-Haytham |
| 3729.0 | Burning_glass | 1645.0 | Ibn_al-Haytham |
| 251713.0 | Qibla | 1645.0 | Ibn_al-Haytham |
| 1406909.0 | The_Compendious_Book_on_Calculation_by_Completion_and_Balancing | 1645.0 | Ibn_al-Haytham |
| 1589482.0 | Abu-Mahmud_Khojandi | 1645.0 | Ibn_al-Haytham |
| 2042430.0 | Qumri | 1645.0 | Ibn_al-Haytham |
| 2882418.0 | Abraham_Maimonides | 1645.0 | Ibn_al-Haytham |
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| 2041982.0 | Al-Shahrazuri | 1645.0 | Ibn_al-Haytham |
| 439770.0 | Abu_Nasr_Mansur | 1645.0 | Ibn_al-Haytham |
| 482939.0 | Al-Hakim_bi-Amr_Allah | 1645.0 | Ibn_al-Haytham |
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| 1.3692155e7 | Philosophy | 1645.0 | Ibn_al-Haytham |
| 35174.0 | 960s | 1645.0 | Ibn_al-Haytham |
| 1784072.0 | Atmospheric_refraction | 1645.0 | Ibn_al-Haytham |
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| 2042576.0 | Amin_al-Din_Rashid_al-Din_Vatvat | 1645.0 | Ibn_al-Haytham |
| 2231772.0 | Ibn_Sahl_(mathematician) | 1645.0 | Ibn_al-Haytham |
| 519403.0 | Ibn_al-haitham | 1645.0 | Ibn_al-Haytham |
| 2.2351023e7 | Shia_Islam_in_the_Indian_subcontinent | 1645.0 | Ibn_al-Haytham |
| 3.9389024e7 | Abu_ali_al-Hasan_ibn_al-Hasan_ibn_al-Haytham | 1645.0 | Ibn_al-Haytham |
| 6.281435e7 | Muwaqqit | 1645.0 | Ibn_al-Haytham |
| 2122.0 | Astrology | 1645.0 | Ibn_al-Haytham |
| 1.0228966e7 | Jabir_ibn_Aflah | 1645.0 | Ibn_al-Haytham |
| 1.1089309e7 | Al-Ḥajjāj_ibn_Yūsuf_ibn_Maṭar | 1645.0 | Ibn_al-Haytham |
| 58775.0 | Timeline_of_electromagnetism_and_classical_optics | 1645.0 | Ibn_al-Haytham |
| 9550030.0 | History_of_algebra | 1645.0 | Ibn_al-Haytham |
| 1.0082768e7 | Hockney–Falco_thesis | 1645.0 | Ibn_al-Haytham |
| 5.3090162e7 | Yahya_ibn_Abi_Mansur | 1645.0 | Ibn_al-Haytham |
| 360726.0 | Planisphere | 1645.0 | Ibn_al-Haytham |
| 1766939.0 | Al-Natili | 1645.0 | Ibn_al-Haytham |
| 1766840.0 | Al-Saghani | 1645.0 | Ibn_al-Haytham |
| 2042257.0 | Nakhshabi | 1645.0 | Ibn_al-Haytham |
| 7718539.0 | Al-'Adudi_Hospital | 1645.0 | Ibn_al-Haytham |
| 2.7375401e7 | Sanad_ibn_Ali | 1645.0 | Ibn_al-Haytham |
| 1291656.0 | Early_modern_period | 1645.0 | Ibn_al-Haytham |
| 1.1133782e7 | Halazen | 1645.0 | Ibn_al-Haytham |
| 3.2100257e7 | Ibn_Abi_al-Ashʿath | 1645.0 | Ibn_al-Haytham |
| 1130.0 | Avicenna | 1645.0 | Ibn_al-Haytham |
| 29266.0 | Relationship_between_religion_and_science | 1645.0 | Ibn_al-Haytham |
| 2.1508913e7 | Abu_ul-Ala_Shirazi | 1645.0 | Ibn_al-Haytham |
| 4.8407883e7 | Al-Miṣrī | 1645.0 | Ibn_al-Haytham |
| 145227.0 | The_Book_of_Healing | 1645.0 | Ibn_al-Haytham |
| 5.6430943e7 | Ammar_al-Mawsili | 1645.0 | Ibn_al-Haytham |
| 2.6571896e7 | Medieval_philosophy | 1645.0 | Ibn_al-Haytham |
| 37232.0 | Fermat's_principle | 1645.0 | Ibn_al-Haytham |
| 5719662.0 | A._I._Sabra | 1645.0 | Ibn_al-Haytham |
| 92550.0 | Omar_Khayyam | 1645.0 | Ibn_al-Haytham |
| 1.8878165e7 | Al-Hassan_ibn_al-Haitham | 1645.0 | Ibn_al-Haytham |
| 58610.0 | Non-Euclidean_geometry | 1645.0 | Ibn_al-Haytham |
| 3032314.0 | History_of_the_camera | 1645.0 | Ibn_al-Haytham |
| 2.8700369e7 | Ibn_Ghazi_al-Miknasi | 1645.0 | Ibn_al-Haytham |
| 3.6920393e7 | History_of_experiments | 1645.0 | Ibn_al-Haytham |
| 207547.0 | Thābit_ibn_Qurra | 1645.0 | Ibn_al-Haytham |
| 3447151.0 | Scientific_demonstration | 1645.0 | Ibn_al-Haytham |
| 2426527.0 | Ibn_al-Nafis | 1645.0 | Ibn_al-Haytham |
| 1.4642431e7 | Alhaitham | 1645.0 | Ibn_al-Haytham |
| 2.5343863e7 | Nur_al-Din_Bimaristan | 1645.0 | Ibn_al-Haytham |
| 5.1518592e7 | Alhazen | 1645.0 | Ibn_al-Haytham |
| 1782819.0 | Al-Nayrizi | 1645.0 | Ibn_al-Haytham |
| 5438833.0 | 'Abd_al-Hamīd_ibn_Turk | 1645.0 | Ibn_al-Haytham |
| 6143364.0 | Alhacen | 1645.0 | Ibn_al-Haytham |
| 3.7464286e7 | Peter_Abelard | 1645.0 | Ibn_al-Haytham |
| 649861.0 | Muhammad_ibn_Musa_al-Khwarizmi | 1645.0 | Ibn_al-Haytham |
| 2.7361247e7 | Al-Kharaqī | 1645.0 | Ibn_al-Haytham |
| 25879.0 | Roger_Bacon | 1645.0 | Ibn_al-Haytham |
| 61626.0 | Gersonides | 1645.0 | Ibn_al-Haytham |
| 8868835.0 | Ibn_Haitham | 1645.0 | Ibn_al-Haytham |
| 1.9374361e7 | Timeline_of_calculus_and_mathematical_analysis | 1645.0 | Ibn_al-Haytham |
| 47836.0 | Averroes | 1645.0 | Ibn_al-Haytham |
| 998087.0 | Ibn_Yunus | 1645.0 | Ibn_al-Haytham |
| 2.4703916e7 | Sullam_al-sama' | 1645.0 | Ibn_al-Haytham |
| 5.4421139e7 | Muhammad_al-Baghdadi | 1645.0 | Ibn_al-Haytham |
| 3099132.0 | Kerala_school_of_astronomy_and_mathematics | 1645.0 | Ibn_al-Haytham |
| 2.7405151e7 | Muhammad_al-Rudani | 1645.0 | Ibn_al-Haytham |
| 23666.0 | Prime_number | 1645.0 | Ibn_al-Haytham |
| 58953.0 | Timeline_of_telescopes,_observatories,_and_observing_technology | 1645.0 | Ibn_al-Haytham |
| 1.4642325e7 | Ibn_alhaitham | 1645.0 | Ibn_al-Haytham |
| 1.9337543e7 | Ibn_Alhazen | 1645.0 | Ibn_al-Haytham |
| 160930.0 | Lorenzo_Ghiberti | 1645.0 | Ibn_al-Haytham |
| 1253603.0 | Abu_Ma'shar_al-Balkhi | 1645.0 | Ibn_al-Haytham |
| 1.171752e7 | Al-Khazini | 1645.0 | Ibn_al-Haytham |
| 47220.0 | 965 | 1645.0 | Ibn_al-Haytham |
| 86822.0 | Ali_ibn_Isa_al-Asturlabi | 1645.0 | Ibn_al-Haytham |
| 1741027.0 | Ibrāhīm_al-Fazārī | 1645.0 | Ibn_al-Haytham |
| 3304608.0 | Astronomy_in_the_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 2.8779877e7 | Atmospheric_optics | 1645.0 | Ibn_al-Haytham |
| 1778258.0 | Alī_ibn_Ahmad_al-Nasawī | 1645.0 | Ibn_al-Haytham |
| 2592962.0 | Octant_(instrument) | 1645.0 | Ibn_al-Haytham |
| 1.0879533e7 | Aja'ib_al-Makhluqat | 1645.0 | Ibn_al-Haytham |
| 1.2654431e7 | Al-Birjandi | 1645.0 | Ibn_al-Haytham |
| 1.3083487e7 | Baha'_al-din_al-'Amili | 1645.0 | Ibn_al-Haytham |
| 1.6352e7 | Al-Haytham | 1645.0 | Ibn_al-Haytham |
| 20545.0 | Mirror | 1645.0 | Ibn_al-Haytham |
| 21527.0 | Number_theory | 1645.0 | Ibn_al-Haytham |
| 22915.0 | Planet | 1645.0 | Ibn_al-Haytham |
| 503345.0 | High_Middle_Ages | 1645.0 | Ibn_al-Haytham |
| 5553121.0 | Latin_translations_of_the_12th_century | 1645.0 | Ibn_al-Haytham |
| 3.2078146e7 | Muhammad_ibn_Aslam_Al-Ghafiqi | 1645.0 | Ibn_al-Haytham |
| 36283.0 | 1011 | 1645.0 | Ibn_al-Haytham |
| 3.7091792e7 | Ibn_Uthal | 1645.0 | Ibn_al-Haytham |
| 69677.0 | Ramon_Llull | 1645.0 | Ibn_al-Haytham |
| 241291.0 | Hyperbolic_geometry | 1645.0 | Ibn_al-Haytham |
| 1766622.0 | Abolfadl_Harawi | 1645.0 | Ibn_al-Haytham |
| 1422050.0 | Timeline_of_Middle_Eastern_history | 1645.0 | Ibn_al-Haytham |
| 5367777.0 | Abu_Sa'id_al-Afif | 1645.0 | Ibn_al-Haytham |
| 8477832.0 | Sibt_al-Maridini | 1645.0 | Ibn_al-Haytham |
| 3.1076646e7 | Al_Achsasi_al_Mouakket | 1645.0 | Ibn_al-Haytham |
| 6330034.0 | Eutychius_of_Alexandria | 1645.0 | Ibn_al-Haytham |
| 223124.0 | List_of_geographers | 1645.0 | Ibn_al-Haytham |
| 1766764.0 | Abu_Sahl_al-Quhi | 1645.0 | Ibn_al-Haytham |
| 6785051.0 | History_of_trigonometry | 1645.0 | Ibn_al-Haytham |
| 655002.0 | Philosophy_of_space_and_time | 1645.0 | Ibn_al-Haytham |
| 1082384.0 | John_Scotus_Eriugena | 1645.0 | Ibn_al-Haytham |
| 8284152.0 | Bimaristan | 1645.0 | Ibn_al-Haytham |
| 33617.0 | William_of_Ockham | 1645.0 | Ibn_al-Haytham |
| 519404.0 | Ibn_al-haytham | 1645.0 | Ibn_al-Haytham |
| 3871014.0 | Rainbow | 1645.0 | Ibn_al-Haytham |
| 6.1768389e7 | Encyclopædia_Meysari | 1645.0 | Ibn_al-Haytham |
| 7660879.0 | List_of_inventions_in_the_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 1.3672511e7 | Ibn_al-Hayham | 1645.0 | Ibn_al-Haytham |
| 1.5515167e7 | Ibn_al-Kattani | 1645.0 | Ibn_al-Haytham |
| 23979.0 | Ptolemy | 1645.0 | Ibn_al-Haytham |
| 78209.0 | Abu_Bakr_al-Razi | 1645.0 | Ibn_al-Haytham |
| 1778150.0 | Ahmad_Nahavandi | 1645.0 | Ibn_al-Haytham |
| 1835859.0 | Husayni_Isfahani | 1645.0 | Ibn_al-Haytham |
| 6.4652504e7 | Zaynab_al-Awadiya | 1645.0 | Ibn_al-Haytham |
| 383129.0 | Celestial_spheres | 1645.0 | Ibn_al-Haytham |
| 771589.0 | Roscellinus | 1645.0 | Ibn_al-Haytham |
| 1741220.0 | Bukhtishu | 1645.0 | Ibn_al-Haytham |
| 2.4923294e7 | Ulugh_Beg_Observatory | 1645.0 | Ibn_al-Haytham |
| 4.1652083e7 | List_of_scientific_demonstrations | 1645.0 | Ibn_al-Haytham |
| 1766960.0 | Abu_al-Hasan_al-Tabari | 1645.0 | Ibn_al-Haytham |
| 2232040.0 | Abu_'Ubayd_al-Juzjani | 1645.0 | Ibn_al-Haytham |
| 2.3812041e7 | Critique_of_Ptolemy | 1645.0 | Ibn_al-Haytham |
| 2.8005345e7 | Sadid_al-Din_al-Kazaruni | 1645.0 | Ibn_al-Haytham |
| 2.9310437e7 | Ibn_Juljul | 1645.0 | Ibn_al-Haytham |
| 3.3327375e7 | Ibn_al‐Haytham | 1645.0 | Ibn_al-Haytham |
| 14021.0 | History_of_astronomy | 1645.0 | Ibn_al-Haytham |
| 1782358.0 | Ibn_Abi_Sadiq | 1645.0 | Ibn_al-Haytham |
| 4391548.0 | Sinān_ibn_al-Fatḥ | 1645.0 | Ibn_al-Haytham |
| 1.1468771e7 | Qāḍī_Zāda_al-Rūmī | 1645.0 | Ibn_al-Haytham |
| 5.7151342e7 | Ibn_Ishaq_al-Tunisi | 1645.0 | Ibn_al-Haytham |
| 6.7610731e7 | Hussam_al-Din_al-Jarrahi | 1645.0 | Ibn_al-Haytham |
| 2.3568467e7 | Rashidun_al-Suri | 1645.0 | Ibn_al-Haytham |
| 13758.0 | History_of_physics | 1645.0 | Ibn_al-Haytham |
| 257242.0 | Apollonius_of_Perga | 1645.0 | Ibn_al-Haytham |
| 1.4642321e7 | Ibn_al_haitham | 1645.0 | Ibn_al-Haytham |
| 4621330.0 | Banū_Mūsā | 1645.0 | Ibn_al-Haytham |
| 1.0780372e7 | Muhammad_Baqir_Yazdi | 1645.0 | Ibn_al-Haytham |
| 2.1691772e7 | Yahya_ibn_Sarafyun | 1645.0 | Ibn_al-Haytham |
| 1.1436522e7 | Zij | 1645.0 | Ibn_al-Haytham |
| 1.5927465e7 | The_Remaining_Signs_of_Past_Centuries | 1645.0 | Ibn_al-Haytham |
| 5.2758716e7 | Ibn_Tumlus | 1645.0 | Ibn_al-Haytham |
| 2933164.0 | List_of_scientists_in_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 4.8407865e7 | Al-Basri | 1645.0 | Ibn_al-Haytham |
| 7.0318183e7 | Polynomials_calculating_sums_of_powers_of_arithmetic_progressions | 1645.0 | Ibn_al-Haytham |
| 2287216.0 | Meanings_of_minor_planet_names:_59001–60000 | 1645.0 | Ibn_al-Haytham |
| 1.1133779e7 | Alazen | 1645.0 | Ibn_al-Haytham |
| 23633.0 | List_of_physicists | 1645.0 | Ibn_al-Haytham |
| 49856.0 | Abbasid_Caliphate | 1645.0 | Ibn_al-Haytham |
| 317238.0 | Book_of_Fixed_Stars | 1645.0 | Ibn_al-Haytham |
| 8868818.0 | Ibn_Haytham | 1645.0 | Ibn_al-Haytham |
| 1786.0 | Arabic_numerals | 1645.0 | Ibn_al-Haytham |
| 181918.0 | Bernard_of_Chartres | 1645.0 | Ibn_al-Haytham |
| 271975.0 | Al-Biruni | 1645.0 | Ibn_al-Haytham |
| 1560514.0 | Ahmad_ibn_Yusuf | 1645.0 | Ibn_al-Haytham |
| 6012554.0 | Cosmology_in_medieval_Islam | 1645.0 | Ibn_al-Haytham |
| 2.0956167e7 | Al-Hassan_Ibn_al-Haytham | 1645.0 | Ibn_al-Haytham |
| 2.6499076e7 | Rufaida_Al-Aslamia | 1645.0 | Ibn_al-Haytham |
| 5.3082933e7 | Abu_al-Hasan_al-Ahwazi | 1645.0 | Ibn_al-Haytham |
| 1.2868203e7 | Geography_and_cartography_in_the_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 5.3839589e7 | Abu_Ali_Hasan_Ibn_al-Haitham | 1645.0 | Ibn_al-Haytham |
| 36161.0 | 1080s | 1645.0 | Ibn_al-Haytham |
| 8510733.0 | Muhyi_al-Din_al-Maghribi | 1645.0 | Ibn_al-Haytham |
| 4.7027397e7 | The_Complete_Book_of_the_Medical_Art | 1645.0 | Ibn_al-Haytham |
| 2042347.0 | Zakariya_al-Qazwini | 1645.0 | Ibn_al-Haytham |
| 4890385.0 | Abu_Ali_al-Hasan_ibn_al-Haytham | 1645.0 | Ibn_al-Haytham |
| 2881.0 | Alexander_of_Hales | 1645.0 | Ibn_al-Haytham |
| 3335703.0 | Al-Samawal_al-Maghribi | 1645.0 | Ibn_al-Haytham |
| 1.1356099e7 | Ibn_al-Heitham | 1645.0 | Ibn_al-Haytham |
| 1.7944118e7 | Physics_in_the_medieval_Islamic_world | 1645.0 | Ibn_al-Haytham |
| 2.2883647e7 | Abu_Ali_al-Khayyat | 1645.0 | Ibn_al-Haytham |
| 3.1562722e7 | Ibn_al-Tilmidh | 1645.0 | Ibn_al-Haytham |
| 1.0713305e7 | Abu_Mansur_al-Baghdadi | 1645.0 | Ibn_al-Haytham |
| 1.3224789e7 | Sextant_(astronomy) | 1645.0 | Ibn_al-Haytham |
| 4.8407869e7 | Al-Baṣrī | 1645.0 | Ibn_al-Haytham |
| 9264.0 | Ecliptic | 1645.0 | Ibn_al-Haytham |
| 2042240.0 | Najm_al-Din_Mahmud_ibn_Ilyas_al-Shirazi | 1645.0 | Ibn_al-Haytham |
| 14220.0 | History_of_mathematics | 1645.0 | Ibn_al-Haytham |
| 1101492.0 | Hunayn_ibn_Ishaq | 1645.0 | Ibn_al-Haytham |
| 59861.0 | Experiment | 1645.0 | Ibn_al-Haytham |
| 1914053.0 | Mu'ayyad_al-Din_al-Urdi | 1645.0 | Ibn_al-Haytham |
| 6328758.0 | Ibrahim_ibn_Sinan | 1645.0 | Ibn_al-Haytham |
| 1842746.0 | Horopter | 1645.0 | Ibn_al-Haytham |
| 3143150.0 | History_of_scientific_method | 1645.0 | Ibn_al-Haytham |
| 6844954.0 | William_of_Auvergne | 1645.0 | Ibn_al-Haytham |
| 1.7140872e7 | Ibn_Shuayb | 1645.0 | Ibn_al-Haytham |
| 3.2077839e7 | Ali_ibn_Isa_al-Kahhal | 1645.0 | Ibn_al-Haytham |
| 5.2173672e7 | Al-Mubashshir_ibn_Fatik | 1645.0 | Ibn_al-Haytham |
| 1741520.0 | Kamāl_al-Dīn_al-Fārisī | 1645.0 | Ibn_al-Haytham |
| 3515519.0 | Lambert_quadrilateral | 1645.0 | Ibn_al-Haytham |
| 414271.0 | Abū_Isḥāq_Ibrāhīm_al-Zarqālī | 1645.0 | Ibn_al-Haytham |
| 48193.0 | Camera_obscura | 1645.0 | Ibn_al-Haytham |
| 564579.0 | Rashid_al-Din_Hamadani | 1645.0 | Ibn_al-Haytham |
| 5088630.0 | Ibn_al-Haithem | 1645.0 | Ibn_al-Haytham |
| 2.2509814e7 | Al-Qifti | 1645.0 | Ibn_al-Haytham |
| 14400.0 | History_of_science | 1645.0 | Ibn_al-Haytham |
| 5553546.0 | Toledo_School_of_Translators | 1645.0 | Ibn_al-Haytham |
| 8465426.0 | Maragheh_observatory | 1645.0 | Ibn_al-Haytham |
| 1.2680597e7 | List_of_people_with_craters_of_the_Moon_named_after_them | 1645.0 | Ibn_al-Haytham |
| 3.2111438e7 | Abu_al-Majd_ibn_Abi_al-Hakam | 1645.0 | Ibn_al-Haytham |
| 3.7091344e7 | Al-Harith_ibn_Kalada | 1645.0 | Ibn_al-Haytham |
| 5.6795161e7 | Ibn_al-A'lam | 1645.0 | Ibn_al-Haytham |
| 1174529.0 | Al-Tasrif | 1645.0 | Ibn_al-Haytham |
| 1759881.0 | Abu_Ja'far_al-Khazin | 1645.0 | Ibn_al-Haytham |
| 6134187.0 | History_of_mathematical_notation | 1645.0 | Ibn_al-Haytham |
| 56978.0 | Song_dynasty | 1645.0 | Ibn_al-Haytham |
| 3.083974e7 | Cavalieri's_quadrature_formula | 1645.0 | Ibn_al-Haytham |
| 1782511.0 | Habash_al-Hasib_al-Marwazi | 1645.0 | Ibn_al-Haytham |
| 2.0407945e7 | Ibn_al-Wafid | 1645.0 | Ibn_al-Haytham |
| 6.0325936e7 | Historical_models_of_the_Solar_System | 1645.0 | Ibn_al-Haytham |
| 4849234.0 | Encyclopedia_of_the_Brethren_of_Purity | 1645.0 | Ibn_al-Haytham |
val allEdgesShortenedRedirects = spark.sql("""
SELECT enwiki_graph_edges.src,
enwiki_graph_edges.src_title,
enwiki_graph_edges.dst,
enwiki_graph_edges.dst_title,
0 AS shortenedRedirect
FROM enwiki_graph_edges INNER JOIN enwiki_page ON enwiki_page.page_id = enwiki_graph_edges.dst
WHERE enwiki_page.page_is_redirect = 0
UNION ALL
SELECT twoStepRedirects.artA AS src,
twoStepRedirects.artA_title AS src_title,
twoStepRedirects.artC AS dst,
twoStepRedirects.artC_title AS dst_title,
1 AS shortenedRedirect
FROM twoStepRedirects
""")
allEdgesShortenedRedirects.createOrReplaceTempView("allEdges")
allEdgesShortenedRedirects: org.apache.spark.sql.DataFrame = [src: int, src_title: string ... 3 more fields]
SELECT count(src) FROM allEdges WHERE shortenedRedirect = 0
| count(src) |
|---|
| 5.47063067e8 |
allEdgesShortenedRedirects.write.saveAsTable("enwiki_graph_edges_shortenedredirects")
SELECT count(src) FROM enwiki_graph_edges_shortenedredirects
| count(src) |
|---|
| 6.07780945e8 |
Creating the graph of Wikipedia articles
The problem here is that the pagelinks table does not contain pairs of IDs, but rather IDs of the source of the link and titles of the destination of the link. So we need to do a join between it and the pages table to get the data in the format we want.
SELECT * FROM enwiki_page
| page_id | page_title | page_is_redirect | has_been_edited | page_len | page_content_model | page_lang |
|---|---|---|---|---|---|---|
| 10.0 | AccessibleComputing | 1.0 | 0.0 | 111.0 | wikitext | NULL |
| 12.0 | Anarchism | 0.0 | 0.0 | 108971.0 | wikitext | NULL |
| 13.0 | AfghanistanHistory | 1.0 | 0.0 | 90.0 | wikitext | NULL |
| 14.0 | AfghanistanGeography | 1.0 | 0.0 | 92.0 | wikitext | NULL |
| 15.0 | AfghanistanPeople | 1.0 | 0.0 | 95.0 | wikitext | NULL |
| 18.0 | AfghanistanCommunications | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 19.0 | AfghanistanTransportations | 1.0 | 0.0 | 113.0 | wikitext | NULL |
| 20.0 | AfghanistanMilitary | 1.0 | 0.0 | 154.0 | wikitext | NULL |
| 21.0 | AfghanistanTransnationalIssues | 1.0 | 0.0 | 101.0 | wikitext | NULL |
| 23.0 | AssistiveTechnology | 1.0 | 0.0 | 88.0 | wikitext | NULL |
| 24.0 | AmoeboidTaxa | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 25.0 | Autism | 1.0 | 0.0 | 150.0 | wikitext | NULL |
| 27.0 | AlbaniaHistory | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 29.0 | AlbaniaPeople | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 30.0 | AsWeMayThink | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 35.0 | AlbaniaGovernment | 1.0 | 0.0 | 87.0 | wikitext | NULL |
| 36.0 | AlbaniaEconomy | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 39.0 | Albedo | 0.0 | 0.0 | 61598.0 | wikitext | NULL |
| 40.0 | AfroAsiaticLanguages | 1.0 | 0.0 | 89.0 | wikitext | NULL |
| 42.0 | ArtificalLanguages | 1.0 | 0.0 | 160.0 | wikitext | NULL |
| 46.0 | AbacuS | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 47.0 | AbalonE | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 48.0 | AbbadideS | 1.0 | 0.0 | 83.0 | wikitext | NULL |
| 49.0 | AbbesS | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 50.0 | AbbevilleFrance | 1.0 | 0.0 | 77.0 | wikitext | NULL |
| 51.0 | AbbeY | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 52.0 | AbboT | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 53.0 | Abbreviations | 1.0 | 0.0 | 77.0 | wikitext | NULL |
| 54.0 | AtlasShrugged | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 56.0 | ArtificialLanguages | 1.0 | 0.0 | 88.0 | wikitext | NULL |
| 58.0 | AtlasShruggedCharacters | 1.0 | 0.0 | 101.0 | wikitext | NULL |
| 59.0 | AtlasShruggedCompanies | 1.0 | 0.0 | 82.0 | wikitext | NULL |
| 60.0 | AyersMusicPublishingCompany | 1.0 | 0.0 | 100.0 | wikitext | NULL |
| 241.0 | AfricanAmericanPeople | 1.0 | 0.0 | 85.0 | wikitext | NULL |
| 242.0 | AdolfHitler | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 247.0 | AbeceDarians | 1.0 | 0.0 | 79.0 | wikitext | NULL |
| 248.0 | AbeL | 1.0 | 0.0 | 81.0 | wikitext | NULL |
| 249.0 | AbensbergGermany | 1.0 | 0.0 | 77.0 | wikitext | NULL |
| 251.0 | AberdeenSouthDakota | 1.0 | 0.0 | 90.0 | wikitext | NULL |
| 254.0 | ArthurKoestler | 1.0 | 0.0 | 83.0 | wikitext | NULL |
| 255.0 | AynRand | 1.0 | 0.0 | 76.0 | wikitext | NULL |
| 256.0 | AlexanderTheGreat | 1.0 | 0.0 | 87.0 | wikitext | NULL |
| 258.0 | AnchorageAlaska | 1.0 | 0.0 | 85.0 | wikitext | NULL |
| 259.0 | ArgumentForms | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 260.0 | ArgumentsForTheExistenceOfGod | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 263.0 | AnarchY | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 264.0 | AsciiArt | 1.0 | 0.0 | 77.0 | wikitext | NULL |
| 269.0 | AcademyAwards | 1.0 | 0.0 | 82.0 | wikitext | NULL |
| 270.0 | AcademyAwards/BestPicture | 1.0 | 0.0 | 115.0 | wikitext | NULL |
| 271.0 | AustriaLanguage | 1.0 | 0.0 | 83.0 | wikitext | NULL |
| 272.0 | AcademicElitism | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 274.0 | AxiomOfChoice | 1.0 | 0.0 | 83.0 | wikitext | NULL |
| 276.0 | AmericanFootball | 1.0 | 0.0 | 85.0 | wikitext | NULL |
| 278.0 | AmericA | 1.0 | 0.0 | 173.0 | wikitext | NULL |
| 279.0 | AnnaKournikova | 1.0 | 0.0 | 83.0 | wikitext | NULL |
| 280.0 | AndorrA | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 287.0 | AustroAsiaticLanguages | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 289.0 | ActresseS | 1.0 | 0.0 | 109.0 | wikitext | NULL |
| 290.0 | A | 0.0 | 0.0 | 30290.0 | wikitext | NULL |
| 291.0 | AnarchoCapitalism | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 293.0 | AnarchoCapitalists | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 296.0 | ActressesS | 1.0 | 0.0 | 83.0 | wikitext | NULL |
| 299.0 | AnAmericanInParis | 1.0 | 0.0 | 88.0 | wikitext | NULL |
| 301.0 | AutoMorphism | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 302.0 | ActionFilm | 1.0 | 0.0 | 79.0 | wikitext | NULL |
| 303.0 | Alabama | 0.0 | 0.0 | 226818.0 | wikitext | NULL |
| 304.0 | AfricA | 1.0 | 0.0 | 115.0 | wikitext | NULL |
| 305.0 | Achilles | 0.0 | 0.0 | 77348.0 | wikitext | NULL |
| 306.0 | AppliedStatistics | 1.0 | 0.0 | 78.0 | wikitext | NULL |
| 307.0 | Abraham_Lincoln | 0.0 | 0.0 | 197343.0 | wikitext | NULL |
| 308.0 | Aristotle | 0.0 | 0.0 | 158506.0 | wikitext | NULL |
| 309.0 | An_American_in_Paris | 0.0 | 0.0 | 24243.0 | wikitext | NULL |
| 316.0 | Academy_Award_for_Best_Production_Design | 0.0 | 0.0 | 99157.0 | wikitext | NULL |
| 324.0 | Academy_Awards | 0.0 | 0.0 | 149454.0 | wikitext | NULL |
| 325.0 | Action_Film | 1.0 | 0.0 | 56.0 | wikitext | NULL |
| 330.0 | Actrius | 0.0 | 0.0 | 6542.0 | wikitext | NULL |
| 332.0 | Animalia_(book) | 0.0 | 0.0 | 6920.0 | wikitext | NULL |
| 334.0 | International_Atomic_Time | 0.0 | 0.0 | 15202.0 | wikitext | NULL |
| 336.0 | Altruism | 0.0 | 0.0 | 77534.0 | wikitext | NULL |
| 338.0 | AutoRacing | 1.0 | 0.0 | 79.0 | wikitext | NULL |
| 339.0 | Ayn_Rand | 0.0 | 0.0 | 88464.0 | wikitext | NULL |
| 340.0 | Alain_Connes | 0.0 | 0.0 | 9175.0 | wikitext | NULL |
| 344.0 | Allan_Dwan | 0.0 | 0.0 | 13381.0 | wikitext | NULL |
| 347.0 | Algeria/People | 1.0 | 0.0 | 89.0 | wikitext | NULL |
| 353.0 | Algeria/Transnational_Issues | 1.0 | 0.0 | 94.0 | wikitext | NULL |
| 358.0 | Algeria | 0.0 | 0.0 | 173692.0 | wikitext | NULL |
| 359.0 | List_of_Atlas_Shrugged_characters | 0.0 | 0.0 | 33550.0 | wikitext | NULL |
| 369.0 | Topics_of_note_in_Atlas_Shrugged | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 569.0 | Anthropology | 0.0 | 0.0 | 108674.0 | wikitext | NULL |
| 572.0 | Agricultural_science | 0.0 | 0.0 | 13186.0 | wikitext | NULL |
| 573.0 | Alchemy | 0.0 | 0.0 | 97130.0 | wikitext | NULL |
| 579.0 | Alien | 0.0 | 0.0 | 6689.0 | wikitext | NULL |
| 580.0 | Astronomer | 0.0 | 0.0 | 8573.0 | wikitext | NULL |
| 583.0 | Ameboid_stage | 1.0 | 0.0 | 20.0 | wikitext | NULL |
| 586.0 | ASCII | 0.0 | 0.0 | 107367.0 | wikitext | NULL |
| 589.0 | Ashmore_And_Cartier_Islands | 1.0 | 0.0 | 106.0 | wikitext | NULL |
| 590.0 | Austin_(disambiguation) | 0.0 | 0.0 | 2455.0 | wikitext | NULL |
| 593.0 | Animation | 0.0 | 0.0 | 69580.0 | wikitext | NULL |
| 594.0 | Apollo | 0.0 | 0.0 | 211059.0 | wikitext | NULL |
| 595.0 | Andre_Agassi | 0.0 | 0.0 | 135589.0 | wikitext | NULL |
| 596.0 | Artificial_languages | 1.0 | 0.0 | 69.0 | wikitext | NULL |
| 597.0 | Austroasiatic_languages | 0.0 | 0.0 | 58664.0 | wikitext | NULL |
| 598.0 | Afro-asiatic_languages | 1.0 | 0.0 | 100.0 | wikitext | NULL |
| 599.0 | Afroasiatic_languages | 0.0 | 0.0 | 69265.0 | wikitext | NULL |
| 600.0 | Andorra | 0.0 | 0.0 | 133270.0 | wikitext | NULL |
| 609.0 | Andorra/Transnational_issues | 1.0 | 0.0 | 135.0 | wikitext | NULL |
| 612.0 | Arithmetic_mean | 0.0 | 0.0 | 13635.0 | wikitext | NULL |
| 615.0 | American_Football_Conference | 0.0 | 0.0 | 22184.0 | wikitext | NULL |
| 617.0 | Albert_Gore | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 618.0 | AnEnquiryConcerningHumanUnderstanding | 1.0 | 0.0 | 109.0 | wikitext | NULL |
| 620.0 | Animal_Farm | 0.0 | 0.0 | 77545.0 | wikitext | NULL |
| 621.0 | Amphibian | 0.0 | 0.0 | 156204.0 | wikitext | NULL |
| 622.0 | Albert_Arnold_Gore/Criticisms | 1.0 | 0.0 | 21.0 | wikitext | NULL |
| 624.0 | Alaska | 0.0 | 0.0 | 172107.0 | wikitext | NULL |
| 626.0 | Auteur_Theory_Film | 1.0 | 0.0 | 20.0 | wikitext | NULL |
| 627.0 | Agriculture | 0.0 | 0.0 | 163082.0 | wikitext | NULL |
| 628.0 | Aldous_Huxley | 0.0 | 0.0 | 57618.0 | wikitext | NULL |
| 629.0 | Abstract_Algebra | 1.0 | 0.0 | 95.0 | wikitext | NULL |
| 630.0 | Ada | 0.0 | 0.0 | 3813.0 | wikitext | NULL |
| 632.0 | Aberdeen_(disambiguation) | 0.0 | 0.0 | 7276.0 | wikitext | NULL |
| 633.0 | Algae | 0.0 | 0.0 | 90619.0 | wikitext | NULL |
| 634.0 | Analysis_of_variance | 0.0 | 0.0 | 55132.0 | wikitext | NULL |
| 635.0 | ANOVA | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 639.0 | Alkane | 0.0 | 0.0 | 73113.0 | wikitext | NULL |
| 640.0 | Appellate_procedure_in_the_United_States | 0.0 | 0.0 | 27615.0 | wikitext | NULL |
| 642.0 | Answer_(law) | 0.0 | 0.0 | 2765.0 | wikitext | NULL |
| 643.0 | Appellate_court | 0.0 | 0.0 | 11978.0 | wikitext | NULL |
| 644.0 | Arithmetic_and_logic_unit | 1.0 | 0.0 | 35.0 | wikitext | NULL |
| 648.0 | Actress | 1.0 | 0.0 | 125.0 | wikitext | NULL |
| 649.0 | Arraignment | 0.0 | 0.0 | 10523.0 | wikitext | NULL |
| 651.0 | America_the_Beautiful | 0.0 | 0.0 | 29339.0 | wikitext | NULL |
| 653.0 | Assistive_technology | 0.0 | 0.0 | 63308.0 | wikitext | NULL |
| 654.0 | Accessible_computing | 1.0 | 0.0 | 36.0 | wikitext | NULL |
| 655.0 | Abacus | 0.0 | 0.0 | 50940.0 | wikitext | NULL |
| 656.0 | Acid | 0.0 | 0.0 | 47523.0 | wikitext | NULL |
| 657.0 | Asphalt | 0.0 | 0.0 | 95749.0 | wikitext | NULL |
| 659.0 | American_National_Standards_Institute | 0.0 | 0.0 | 18089.0 | wikitext | NULL |
| 661.0 | Argument_(disambiguation) | 0.0 | 0.0 | 1710.0 | wikitext | NULL |
| 662.0 | Apollo_11 | 0.0 | 0.0 | 184198.0 | wikitext | NULL |
| 663.0 | Apollo_8 | 0.0 | 0.0 | 95526.0 | wikitext | NULL |
| 664.0 | Astronaut | 0.0 | 0.0 | 80670.0 | wikitext | NULL |
| 665.0 | A_Modest_Proposal | 0.0 | 0.0 | 24728.0 | wikitext | NULL |
| 666.0 | Alkali_metal | 0.0 | 0.0 | 217024.0 | wikitext | NULL |
| 668.0 | Argument_form | 1.0 | 0.0 | 26.0 | wikitext | NULL |
| 669.0 | Allotrope | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 670.0 | Alphabet | 0.0 | 0.0 | 48050.0 | wikitext | NULL |
| 673.0 | Atomic_number | 0.0 | 0.0 | 14031.0 | wikitext | NULL |
| 674.0 | Anatomy | 0.0 | 0.0 | 77631.0 | wikitext | NULL |
| 675.0 | Affirming_the_consequent | 0.0 | 0.0 | 6242.0 | wikitext | NULL |
| 676.0 | Andrei_Tarkovsky | 0.0 | 0.0 | 74800.0 | wikitext | NULL |
| 677.0 | Ambiguity | 0.0 | 0.0 | 31221.0 | wikitext | NULL |
| 678.0 | Abel | 0.0 | 0.0 | 10386.0 | wikitext | NULL |
| 679.0 | Animal_(disambiguation) | 0.0 | 0.0 | 8673.0 | wikitext | NULL |
| 680.0 | Aardvark | 0.0 | 0.0 | 36644.0 | wikitext | NULL |
| 681.0 | Aardwolf | 0.0 | 0.0 | 24478.0 | wikitext | NULL |
| 682.0 | Adobe | 0.0 | 0.0 | 28066.0 | wikitext | NULL |
| 683.0 | Adventure | 0.0 | 0.0 | 9292.0 | wikitext | NULL |
| 686.0 | Amaltheia | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 687.0 | Analysis_of_Variance | 1.0 | 0.0 | 66.0 | wikitext | NULL |
| 689.0 | Asia | 0.0 | 0.0 | 119487.0 | wikitext | NULL |
| 690.0 | Aruba | 0.0 | 0.0 | 77354.0 | wikitext | NULL |
| 691.0 | Articles_of_Confederation | 0.0 | 0.0 | 73929.0 | wikitext | NULL |
| 693.0 | Archaeology/Broch | 1.0 | 0.0 | 71.0 | wikitext | NULL |
| 694.0 | Asia_Minor_(disambiguation) | 0.0 | 0.0 | 520.0 | wikitext | NULL |
| 696.0 | Aa_River | 1.0 | 0.0 | 108.0 | wikitext | NULL |
| 698.0 | Atlantic_Ocean | 0.0 | 0.0 | 114989.0 | wikitext | NULL |
| 700.0 | Arthur_Schopenhauer | 0.0 | 0.0 | 165600.0 | wikitext | NULL |
| 701.0 | Angola | 0.0 | 0.0 | 156923.0 | wikitext | NULL |
| 704.0 | Demographics_of_Angola | 0.0 | 0.0 | 33803.0 | wikitext | NULL |
| 705.0 | Politics_of_Angola | 0.0 | 0.0 | 15087.0 | wikitext | NULL |
| 706.0 | Economy_of_Angola | 0.0 | 0.0 | 45452.0 | wikitext | NULL |
| 708.0 | Transport_in_Angola | 0.0 | 0.0 | 4083.0 | wikitext | NULL |
| 709.0 | Angolan_Armed_Forces | 0.0 | 0.0 | 25218.0 | wikitext | NULL |
| 710.0 | Foreign_relations_of_Angola | 0.0 | 0.0 | 28022.0 | wikitext | NULL |
| 711.0 | Albert_Sidney_Johnston | 0.0 | 0.0 | 53655.0 | wikitext | NULL |
| 713.0 | Android_(robot) | 0.0 | 0.0 | 30791.0 | wikitext | NULL |
| 717.0 | Alberta | 0.0 | 0.0 | 165138.0 | wikitext | NULL |
| 727.0 | Astronomy/History | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 728.0 | List_of_anthropologists | 0.0 | 0.0 | 8657.0 | wikitext | NULL |
| 731.0 | Astronomy_and_Astrophysics/History | 1.0 | 1.0 | 86.0 | wikitext | NULL |
| 734.0 | Actinopterygii | 0.0 | 0.0 | 41677.0 | wikitext | NULL |
| 735.0 | Al_Gore/Criticisms | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 736.0 | Albert_Einstein | 0.0 | 0.0 | 210170.0 | wikitext | NULL |
| 737.0 | Afghanistan | 0.0 | 0.0 | 310005.0 | wikitext | NULL |
| 738.0 | Albania | 0.0 | 0.0 | 277109.0 | wikitext | NULL |
| 740.0 | Allah | 0.0 | 0.0 | 49185.0 | wikitext | NULL |
| 742.0 | Algorithms_(journal) | 0.0 | 0.0 | 3748.0 | wikitext | NULL |
| 743.0 | Antigua_And_Barbuda | 1.0 | 0.0 | 79.0 | wikitext | NULL |
| 746.0 | Azerbaijan | 0.0 | 0.0 | 236389.0 | wikitext | NULL |
| 748.0 | Amateur_astronomy | 0.0 | 0.0 | 36867.0 | wikitext | NULL |
| 749.0 | Astronomers_and_Astrophysicists | 1.0 | 0.0 | 24.0 | wikitext | NULL |
| 751.0 | Aikido | 0.0 | 0.0 | 57246.0 | wikitext | NULL |
| 752.0 | Art | 0.0 | 0.0 | 121171.0 | wikitext | NULL |
| 755.0 | Albania/History | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 758.0 | Albania/Transnational_Issues | 1.0 | 0.0 | 134.0 | wikitext | NULL |
| 759.0 | Albania/People | 1.0 | 0.0 | 89.0 | wikitext | NULL |
| 763.0 | Albania/Foreign_relations | 1.0 | 0.0 | 134.0 | wikitext | NULL |
| 764.0 | Agnostida | 0.0 | 0.0 | 8134.0 | wikitext | NULL |
| 765.0 | Abortion | 0.0 | 0.0 | 194359.0 | wikitext | NULL |
| 766.0 | Abstract_(law) | 0.0 | 0.0 | 2292.0 | wikitext | NULL |
| 767.0 | A.E._van_Vogt | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 771.0 | American_Revolutionary_War | 0.0 | 0.0 | 308703.0 | wikitext | NULL |
| 772.0 | Ampere | 0.0 | 0.0 | 15813.0 | wikitext | NULL |
| 775.0 | Algorithm | 0.0 | 0.0 | 113862.0 | wikitext | NULL |
| 777.0 | Annual_plant | 0.0 | 0.0 | 6058.0 | wikitext | NULL |
| 779.0 | Anthophyta | 0.0 | 0.0 | 3135.0 | wikitext | NULL |
| 780.0 | Atlas_(disambiguation) | 0.0 | 0.0 | 11221.0 | wikitext | NULL |
| 782.0 | Mouthwash | 0.0 | 0.0 | 65648.0 | wikitext | NULL |
| 783.0 | Alexander_the_Great | 0.0 | 0.0 | 227366.0 | wikitext | NULL |
| 784.0 | Alfred_Korzybski | 0.0 | 0.0 | 14772.0 | wikitext | NULL |
| 785.0 | Asteroids_(video_game) | 0.0 | 0.0 | 48246.0 | wikitext | NULL |
| 786.0 | Asparagales | 0.0 | 0.0 | 89674.0 | wikitext | NULL |
| 787.0 | Alismatales | 0.0 | 0.0 | 13634.0 | wikitext | NULL |
| 788.0 | Apiales | 0.0 | 0.0 | 7627.0 | wikitext | NULL |
| 789.0 | Asterales | 0.0 | 0.0 | 11669.0 | wikitext | NULL |
| 791.0 | Asteroid | 0.0 | 0.0 | 155686.0 | wikitext | NULL |
| 794.0 | Allocution | 0.0 | 0.0 | 3884.0 | wikitext | NULL |
| 795.0 | Affidavit | 0.0 | 0.0 | 10052.0 | wikitext | NULL |
| 798.0 | Aries_(constellation) | 0.0 | 0.0 | 50994.0 | wikitext | NULL |
| 799.0 | Aquarius_(constellation) | 0.0 | 0.0 | 36019.0 | wikitext | NULL |
| 800.0 | Anime | 0.0 | 0.0 | 104726.0 | wikitext | NULL |
| 801.0 | Asterism | 0.0 | 0.0 | 357.0 | wikitext | NULL |
| 802.0 | Ankara | 0.0 | 0.0 | 125716.0 | wikitext | NULL |
| 803.0 | Arabic | 0.0 | 0.0 | 174116.0 | wikitext | NULL |
| 807.0 | AlbaniaCommunications | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 808.0 | Alfred_Hitchcock | 0.0 | 0.0 | 179231.0 | wikitext | NULL |
| 809.0 | Anaconda | 0.0 | 0.0 | 8537.0 | wikitext | NULL |
| 813.0 | Afghanistan/History | 1.0 | 0.0 | 88.0 | wikitext | NULL |
| 814.0 | Afghanistan/Geography | 1.0 | 0.0 | 90.0 | wikitext | NULL |
| 815.0 | Afghanistan/Government | 1.0 | 0.0 | 114.0 | wikitext | NULL |
| 816.0 | Afghanistan/People | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 817.0 | Afghanistan/Economy | 1.0 | 0.0 | 88.0 | wikitext | NULL |
| 818.0 | Afghanistan/Communications | 1.0 | 0.0 | 114.0 | wikitext | NULL |
| 820.0 | Afghanistan/Military | 1.0 | 0.0 | 155.0 | wikitext | NULL |
| 821.0 | Afghanistan/Transnational_Issues | 1.0 | 0.0 | 98.0 | wikitext | NULL |
| 822.0 | Afghanistan_(1911_Encyclopedia) | 1.0 | 0.0 | 25.0 | wikitext | NULL |
| 824.0 | Altaic_languages | 0.0 | 0.0 | 63972.0 | wikitext | NULL |
| 825.0 | Austrian_German | 0.0 | 0.0 | 21521.0 | wikitext | NULL |
| 832.0 | Austria/Transnational_issues | 1.0 | 0.0 | 94.0 | wikitext | NULL |
| 839.0 | Anglican_Church | 1.0 | 0.0 | 25.0 | wikitext | NULL |
| 840.0 | Axiom_of_choice | 0.0 | 0.0 | 58996.0 | wikitext | NULL |
| 841.0 | Attila | 0.0 | 0.0 | 65626.0 | wikitext | NULL |
| 842.0 | Aegean_Sea | 0.0 | 0.0 | 47828.0 | wikitext | NULL |
| 843.0 | A_Clockwork_Orange_(novel) | 0.0 | 0.0 | 55097.0 | wikitext | NULL |
| 844.0 | Amsterdam | 0.0 | 0.0 | 196002.0 | wikitext | NULL |
| 846.0 | Museum_of_Work | 0.0 | 0.0 | 7122.0 | wikitext | NULL |
| 848.0 | Audi | 0.0 | 0.0 | 147456.0 | wikitext | NULL |
| 849.0 | Aircraft | 0.0 | 0.0 | 63371.0 | wikitext | NULL |
| 851.0 | Alfred_Nobel | 0.0 | 0.0 | 33680.0 | wikitext | NULL |
| 852.0 | Alexander_Graham_Bell | 0.0 | 0.0 | 143315.0 | wikitext | NULL |
| 854.0 | Anatolia | 0.0 | 0.0 | 72850.0 | wikitext | NULL |
| 855.0 | Abiotic_factors | 1.0 | 0.0 | 31.0 | wikitext | NULL |
| 856.0 | Apple_Inc. | 0.0 | 0.0 | 296242.0 | wikitext | NULL |
| 857.0 | Aberdeenshire | 0.0 | 0.0 | 33434.0 | wikitext | NULL |
| 858.0 | AU | 1.0 | 0.0 | 127.0 | wikitext | NULL |
| 859.0 | Aztlan_Underground | 0.0 | 0.0 | 7876.0 | wikitext | NULL |
| 860.0 | Aland | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 863.0 | American_Civil_War | 0.0 | 0.0 | 252499.0 | wikitext | NULL |
| 864.0 | Andy_Warhol | 0.0 | 0.0 | 159393.0 | wikitext | NULL |
| 868.0 | Alp_Arslan | 0.0 | 0.0 | 27066.0 | wikitext | NULL |
| 869.0 | American_Film_Institute | 0.0 | 0.0 | 23405.0 | wikitext | NULL |
| 872.0 | Akira_Kurosawa | 0.0 | 0.0 | 108667.0 | wikitext | NULL |
| 873.0 | Ancient_civilization | 1.0 | 0.0 | 95.0 | wikitext | NULL |
| 874.0 | Ancient_Egypt | 0.0 | 0.0 | 141823.0 | wikitext | NULL |
| 875.0 | Analog_Brothers | 0.0 | 0.0 | 3787.0 | wikitext | NULL |
| 876.0 | Motor_neuron_disease | 0.0 | 0.0 | 22719.0 | wikitext | NULL |
| 877.0 | Abjad | 0.0 | 0.0 | 22953.0 | wikitext | NULL |
| 878.0 | Abugida | 0.0 | 0.0 | 44096.0 | wikitext | NULL |
| 880.0 | ABBA | 0.0 | 0.0 | 143023.0 | wikitext | NULL |
| 881.0 | Allegiance | 0.0 | 0.0 | 15801.0 | wikitext | NULL |
| 882.0 | Absolute_majority | 1.0 | 0.0 | 121.0 | wikitext | NULL |
| 885.0 | Altenberg | 0.0 | 0.0 | 1824.0 | wikitext | NULL |
| 887.0 | MessagePad | 0.0 | 0.0 | 47725.0 | wikitext | NULL |
| 888.0 | A._E._van_Vogt | 0.0 | 0.0 | 51988.0 | wikitext | NULL |
| 890.0 | Anna_Kournikova | 0.0 | 0.0 | 55901.0 | wikitext | NULL |
| 891.0 | Accountancy | 1.0 | 0.0 | 24.0 | wikitext | NULL |
| 892.0 | Alfons_Maria_Jakob | 0.0 | 0.0 | 5267.0 | wikitext | NULL |
| 894.0 | Agnosticism | 0.0 | 0.0 | 72756.0 | wikitext | NULL |
| 896.0 | Argon | 0.0 | 0.0 | 40086.0 | wikitext | NULL |
| 897.0 | Arsenic | 0.0 | 0.0 | 127483.0 | wikitext | NULL |
| 898.0 | Antimony | 0.0 | 0.0 | 60686.0 | wikitext | NULL |
| 899.0 | Actinium | 0.0 | 0.0 | 39951.0 | wikitext | NULL |
| 900.0 | Americium | 0.0 | 0.0 | 77374.0 | wikitext | NULL |
| 901.0 | Astatine | 0.0 | 0.0 | 81700.0 | wikitext | NULL |
| 902.0 | Atom | 0.0 | 0.0 | 125779.0 | wikitext | NULL |
| 903.0 | Arable_land | 0.0 | 0.0 | 17047.0 | wikitext | NULL |
| 904.0 | Aluminium | 0.0 | 0.0 | 138626.0 | wikitext | NULL |
| 905.0 | Advanced_Chemistry | 0.0 | 0.0 | 12704.0 | wikitext | NULL |
| 907.0 | Awk | 1.0 | 0.0 | 82.0 | wikitext | NULL |
| 908.0 | AgoraNomic | 1.0 | 0.0 | 19.0 | wikitext | NULL |
| 909.0 | Anglican_Communion | 0.0 | 0.0 | 67308.0 | wikitext | NULL |
| 910.0 | Arne_Kaijser | 0.0 | 0.0 | 2754.0 | wikitext | NULL |
| 911.0 | Archipelago | 0.0 | 0.0 | 7267.0 | wikitext | NULL |
| 914.0 | Author | 0.0 | 0.0 | 20404.0 | wikitext | NULL |
| 915.0 | Andrey_Markov | 0.0 | 0.0 | 10528.0 | wikitext | NULL |
| 918.0 | Anti-semitism | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 919.0 | Anti-semitic | 1.0 | 0.0 | 47.0 | wikitext | NULL |
| 921.0 | Angst | 0.0 | 0.0 | 7030.0 | wikitext | NULL |
| 922.0 | Anxiety | 0.0 | 0.0 | 92522.0 | wikitext | NULL |
| 923.0 | A.A._Milne | 1.0 | 0.0 | 25.0 | wikitext | NULL |
| 924.0 | A._A._Milne | 0.0 | 0.0 | 43901.0 | wikitext | NULL |
| 925.0 | Asociación_Alumni | 0.0 | 0.0 | 5890.0 | wikitext | NULL |
| 926.0 | Alumna | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 928.0 | Axiom | 0.0 | 0.0 | 35579.0 | wikitext | NULL |
| 929.0 | Alpha | 0.0 | 0.0 | 11696.0 | wikitext | NULL |
| 930.0 | Alvin_Toffler | 0.0 | 0.0 | 31422.0 | wikitext | NULL |
| 931.0 | The_Amazing_Spider-Man | 0.0 | 0.0 | 86345.0 | wikitext | NULL |
| 933.0 | AM | 0.0 | 0.0 | 4055.0 | wikitext | NULL |
| 935.0 | Automated_Alice/XII | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 936.0 | Automated_Alice/XI | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 937.0 | Automated_Alice/X | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 938.0 | Automated_Alice/IX | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 939.0 | Automated_Alice/VIII | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 940.0 | Automated_Alice/VI | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 941.0 | Automated_Alice/VII | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 942.0 | Automated_Alice/V | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 943.0 | Automated_Alice/IV | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 944.0 | Automated_Alice/II | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 945.0 | Automated_Alice/I | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 946.0 | Automated_Alice/III | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 951.0 | Antigua_and_Barbuda | 0.0 | 0.0 | 69608.0 | wikitext | NULL |
| 953.0 | Azincourt | 0.0 | 0.0 | 7304.0 | wikitext | NULL |
| 954.0 | Albert_Speer | 0.0 | 0.0 | 74955.0 | wikitext | NULL |
| 956.0 | Asteraceae | 0.0 | 0.0 | 52348.0 | wikitext | NULL |
| 957.0 | Apiaceae | 0.0 | 0.0 | 19443.0 | wikitext | NULL |
| 958.0 | Axon | 0.0 | 0.0 | 56358.0 | wikitext | NULL |
| 959.0 | Agma | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 960.0 | Aramaic_alphabet | 0.0 | 0.0 | 39545.0 | wikitext | NULL |
| 963.0 | Arguments_for_the_existence_of_God | 1.0 | 0.0 | 30.0 | wikitext | NULL |
| 966.0 | American_shot | 0.0 | 0.0 | 2475.0 | wikitext | NULL |
| 967.0 | Acute_disseminated_encephalomyelitis | 0.0 | 0.0 | 49156.0 | wikitext | NULL |
| 969.0 | Ataxia | 0.0 | 0.0 | 51374.0 | wikitext | NULL |
| 970.0 | AmbientCalculusOnline | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 972.0 | Abdul_Alhazred | 1.0 | 0.0 | 453.0 | wikitext | NULL |
| 973.0 | A_priori_and_a_posterior_knowledge | 1.0 | 0.0 | 39.0 | wikitext | NULL |
| 974.0 | Ada_Lovelace | 0.0 | 0.0 | 81872.0 | wikitext | NULL |
| 975.0 | AmbientCalculiOnline | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 980.0 | August_Derleth | 0.0 | 0.0 | 36081.0 | wikitext | NULL |
| 981.0 | Alps | 0.0 | 0.0 | 97011.0 | wikitext | NULL |
| 982.0 | A_priori_and_a_posteriori_knowledge | 1.0 | 0.0 | 39.0 | wikitext | NULL |
| 983.0 | Albert_Camus | 0.0 | 0.0 | 60082.0 | wikitext | NULL |
| 984.0 | Agatha_Christie | 0.0 | 0.0 | 157622.0 | wikitext | NULL |
| 986.0 | The_Plague_(novel) | 0.0 | 0.0 | 33756.0 | wikitext | NULL |
| 988.0 | Applied_ethics | 0.0 | 0.0 | 10125.0 | wikitext | NULL |
| 991.0 | Absolute_value | 0.0 | 0.0 | 25672.0 | wikitext | NULL |
| 993.0 | Analog_signal | 0.0 | 0.0 | 4898.0 | wikitext | NULL |
| 994.0 | Arecales | 0.0 | 0.0 | 3408.0 | wikitext | NULL |
| 1000.0 | Hercule_Poirot | 0.0 | 0.0 | 70455.0 | wikitext | NULL |
| 1002.0 | Miss_Marple | 0.0 | 0.0 | 31513.0 | wikitext | NULL |
| 1004.0 | April | 0.0 | 0.0 | 32330.0 | wikitext | NULL |
| 1005.0 | August | 0.0 | 0.0 | 29903.0 | wikitext | NULL |
| 1006.0 | Aaron | 0.0 | 0.0 | 45188.0 | wikitext | NULL |
| 1008.0 | April_6 | 0.0 | 0.0 | 53142.0 | wikitext | NULL |
| 1009.0 | April_12 | 0.0 | 0.0 | 52633.0 | wikitext | NULL |
| 1010.0 | April_15 | 0.0 | 0.0 | 50663.0 | wikitext | NULL |
| 1011.0 | April_30 | 0.0 | 0.0 | 48202.0 | wikitext | NULL |
| 1012.0 | August_22 | 0.0 | 0.0 | 44190.0 | wikitext | NULL |
| 1013.0 | August_27 | 0.0 | 0.0 | 47372.0 | wikitext | NULL |
| 1014.0 | Alcohol_(chemistry) | 0.0 | 0.0 | 34841.0 | wikitext | NULL |
| 1016.0 | Achill_Island | 0.0 | 0.0 | 39863.0 | wikitext | NULL |
| 1017.0 | Allen_Ginsberg | 0.0 | 0.0 | 108507.0 | wikitext | NULL |
| 1018.0 | Algebraically_closed_field | 0.0 | 0.0 | 12639.0 | wikitext | NULL |
| 1019.0 | August_6 | 0.0 | 0.0 | 44883.0 | wikitext | NULL |
| 1020.0 | Anatoly_Karpov | 0.0 | 0.0 | 44732.0 | wikitext | NULL |
| 1021.0 | Aspect_ratio | 0.0 | 0.0 | 5699.0 | wikitext | NULL |
| 1022.0 | Auto_racing | 0.0 | 0.0 | 49738.0 | wikitext | NULL |
| 1023.0 | Anarcho-capitalism | 0.0 | 0.0 | 135375.0 | wikitext | NULL |
| 1026.0 | Anarcho-capitalists | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1027.0 | August_9 | 0.0 | 0.0 | 48531.0 | wikitext | NULL |
| 1028.0 | Aristophanes | 0.0 | 0.0 | 68860.0 | wikitext | NULL |
| 1029.0 | Albert_Schweitzer | 0.0 | 0.0 | 80267.0 | wikitext | NULL |
| 1030.0 | Austrian_School | 0.0 | 0.0 | 71838.0 | wikitext | NULL |
| 1032.0 | Abscess | 0.0 | 0.0 | 32103.0 | wikitext | NULL |
| 1035.0 | Aal | 1.0 | 0.0 | 94.0 | wikitext | NULL |
| 1036.0 | Aalborg_Municipality | 0.0 | 0.0 | 13463.0 | wikitext | NULL |
| 1038.0 | Aarhus | 0.0 | 0.0 | 205789.0 | wikitext | NULL |
| 1043.0 | Northern_cavefish | 0.0 | 0.0 | 2625.0 | wikitext | NULL |
| 1046.0 | Abatement | 0.0 | 0.0 | 1133.0 | wikitext | NULL |
| 1049.0 | Amateur | 0.0 | 0.0 | 15459.0 | wikitext | NULL |
| 1051.0 | Alexis_Carrel | 0.0 | 0.0 | 38802.0 | wikitext | NULL |
| 1055.0 | All_Souls'_Day | 0.0 | 0.0 | 36190.0 | wikitext | NULL |
| 1057.0 | Anatole_France | 0.0 | 0.0 | 16387.0 | wikitext | NULL |
| 1058.0 | André_Gide | 0.0 | 0.0 | 32483.0 | wikitext | NULL |
| 1059.0 | Applied_statistics | 1.0 | 0.0 | 192.0 | wikitext | NULL |
| 1061.0 | Analysis_of_variance/Random_effects_models | 1.0 | 0.0 | 123.0 | wikitext | NULL |
| 1062.0 | Analysis_of_variance/Degrees_of_freedom | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 1063.0 | Algorithms_for_calculating_variance | 0.0 | 0.0 | 30844.0 | wikitext | NULL |
| 1064.0 | Almond | 0.0 | 0.0 | 65298.0 | wikitext | NULL |
| 1069.0 | Demographics_of_Antigua_and_Barbuda | 0.0 | 0.0 | 15988.0 | wikitext | NULL |
| 1070.0 | Politics_of_Antigua_and_Barbuda | 0.0 | 0.0 | 10381.0 | wikitext | NULL |
| 1072.0 | Telecommunications_in_Antigua_and_Barbuda | 0.0 | 0.0 | 5634.0 | wikitext | NULL |
| 1074.0 | Antigua_and_Barbuda_Defence_Force | 0.0 | 0.0 | 6978.0 | wikitext | NULL |
| 1075.0 | Antigua_and_Barbuda/Transnational_issues | 1.0 | 0.0 | 106.0 | wikitext | NULL |
| 1078.0 | Antisemitism | 0.0 | 0.0 | 146605.0 | wikitext | NULL |
| 1081.0 | Economy_of_Azerbaijan | 0.0 | 0.0 | 60281.0 | wikitext | NULL |
| 1082.0 | Geography_of_Azerbaijan | 0.0 | 0.0 | 14609.0 | wikitext | NULL |
| 1083.0 | Azerbaijan/People | 1.0 | 0.0 | 92.0 | wikitext | NULL |
| 1085.0 | Azerbaijan/Communications | 1.0 | 0.0 | 98.0 | wikitext | NULL |
| 1087.0 | Foreign_relations_of_Azerbaijan | 0.0 | 0.0 | 106467.0 | wikitext | NULL |
| 1088.0 | Azerbaijani_Armed_Forces | 0.0 | 0.0 | 86941.0 | wikitext | NULL |
| 1089.0 | Azerbaijan/Foreign_relations | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 1091.0 | Geography_of_Armenia | 0.0 | 0.0 | 9701.0 | wikitext | NULL |
| 1092.0 | Demographics_of_Armenia | 0.0 | 0.0 | 53608.0 | wikitext | NULL |
| 1093.0 | Politics_of_Armenia | 0.0 | 0.0 | 22632.0 | wikitext | NULL |
| 1094.0 | Economy_of_Armenia | 0.0 | 0.0 | 139777.0 | wikitext | NULL |
| 1096.0 | Transport_in_Armenia | 0.0 | 0.0 | 17734.0 | wikitext | NULL |
| 1097.0 | Armed_Forces_of_Armenia | 0.0 | 0.0 | 65462.0 | wikitext | NULL |
| 1098.0 | Foreign_relations_of_Armenia | 0.0 | 0.0 | 166725.0 | wikitext | NULL |
| 1105.0 | Argentina/Transnational_issues | 1.0 | 0.0 | 138.0 | wikitext | NULL |
| 1108.0 | Argentina/Foreign_relations | 1.0 | 0.0 | 138.0 | wikitext | NULL |
| 1109.0 | Geography_of_American_Samoa | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 1110.0 | Demographics_of_American_Samoa | 0.0 | 0.0 | 13354.0 | wikitext | NULL |
| 1111.0 | Politics_of_American_Samoa | 0.0 | 0.0 | 5605.0 | wikitext | NULL |
| 1112.0 | Economy_of_American_Samoa | 0.0 | 0.0 | 6915.0 | wikitext | NULL |
| 1114.0 | Transportation_in_American_Samoa | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 1116.0 | American_Samoa/Military | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 1123.0 | Australia/Transnational_issues | 1.0 | 0.0 | 96.0 | wikitext | NULL |
| 1129.0 | August_13 | 0.0 | 0.0 | 47062.0 | wikitext | NULL |
| 1130.0 | Avicenna | 0.0 | 0.0 | 114907.0 | wikitext | NULL |
| 1132.0 | The_Ashes | 0.0 | 0.0 | 92557.0 | wikitext | NULL |
| 1134.0 | Analysis | 0.0 | 0.0 | 21855.0 | wikitext | NULL |
| 1135.0 | Abner_Doubleday | 0.0 | 0.0 | 28685.0 | wikitext | NULL |
| 1136.0 | America's_National_Game | 0.0 | 0.0 | 1519.0 | wikitext | NULL |
| 1140.0 | Amplitude_modulation | 0.0 | 0.0 | 33937.0 | wikitext | NULL |
| 1141.0 | Augustin-Jean_Fresnel | 0.0 | 0.0 | 207403.0 | wikitext | NULL |
| 1143.0 | Abbot | 0.0 | 0.0 | 34498.0 | wikitext | NULL |
| 1144.0 | Ardipithecus | 0.0 | 0.0 | 31777.0 | wikitext | NULL |
| 1146.0 | Assembly_line | 0.0 | 0.0 | 34686.0 | wikitext | NULL |
| 1148.0 | Adelaide | 0.0 | 0.0 | 165131.0 | wikitext | NULL |
| 1151.0 | AK47 | 1.0 | 0.0 | 84.0 | wikitext | NULL |
| 1152.0 | Alan_Garner | 0.0 | 0.0 | 41348.0 | wikitext | NULL |
| 1153.0 | Amhrann_na_bhFiann | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1154.0 | August_2 | 0.0 | 0.0 | 49532.0 | wikitext | NULL |
| 1155.0 | Atlantic_(disambiguation) | 0.0 | 0.0 | 4980.0 | wikitext | NULL |
| 1158.0 | Algebraic_number | 0.0 | 0.0 | 12611.0 | wikitext | NULL |
| 1160.0 | Automorphism | 0.0 | 0.0 | 11771.0 | wikitext | NULL |
| 1162.0 | Accordion | 0.0 | 0.0 | 66013.0 | wikitext | NULL |
| 1164.0 | Artificial_intelligence | 0.0 | 0.0 | 220426.0 | wikitext | NULL |
| 1166.0 | Afro_Celt_Sound_System | 0.0 | 0.0 | 22290.0 | wikitext | NULL |
| 1167.0 | Ancient_philosophy | 0.0 | 0.0 | 29750.0 | wikitext | NULL |
| 1168.0 | Anaximander | 0.0 | 0.0 | 56067.0 | wikitext | NULL |
| 1169.0 | APL | 0.0 | 0.0 | 2536.0 | wikitext | NULL |
| 1170.0 | Architect | 0.0 | 0.0 | 27793.0 | wikitext | NULL |
| 1171.0 | Abbreviation | 0.0 | 0.0 | 32641.0 | wikitext | NULL |
| 1174.0 | Aphrodite | 0.0 | 0.0 | 141174.0 | wikitext | NULL |
| 1175.0 | April_1 | 0.0 | 0.0 | 49325.0 | wikitext | NULL |
| 1176.0 | Antisymmetric_relation | 0.0 | 0.0 | 4327.0 | wikitext | NULL |
| 1177.0 | Aleister_Crowley | 0.0 | 0.0 | 128082.0 | wikitext | NULL |
| 1178.0 | Afterlife | 0.0 | 0.0 | 114450.0 | wikitext | NULL |
| 1181.0 | Astrometry | 0.0 | 0.0 | 18156.0 | wikitext | NULL |
| 1182.0 | Athena | 0.0 | 0.0 | 117909.0 | wikitext | NULL |
| 1183.0 | Amber_Diceless_Roleplaying_Game | 0.0 | 0.0 | 22788.0 | wikitext | NULL |
| 1184.0 | Athene_(disambiguation) | 0.0 | 0.0 | 1038.0 | wikitext | NULL |
| 1186.0 | AphexTwin | 1.0 | 0.0 | 78.0 | wikitext | NULL |
| 1187.0 | Alloy | 0.0 | 0.0 | 39789.0 | wikitext | NULL |
| 1189.0 | Articles_of_Faith | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 1190.0 | Alternative_history | 1.0 | 0.0 | 31.0 | wikitext | NULL |
| 1192.0 | Artistic_revolution | 0.0 | 0.0 | 9302.0 | wikitext | NULL |
| 1193.0 | Agrarianism | 0.0 | 0.0 | 45044.0 | wikitext | NULL |
| 1194.0 | Atomic | 0.0 | 0.0 | 1655.0 | wikitext | NULL |
| 1195.0 | Allotropes | 1.0 | 0.0 | 42.0 | wikitext | NULL |
| 1196.0 | Angle | 0.0 | 0.0 | 50252.0 | wikitext | NULL |
| 1197.0 | Asa | 0.0 | 0.0 | 1718.0 | wikitext | NULL |
| 1198.0 | Acoustics | 0.0 | 0.0 | 38399.0 | wikitext | NULL |
| 1199.0 | Angle_tribe | 1.0 | 0.0 | 20.0 | wikitext | NULL |
| 1200.0 | Atomic_physics | 0.0 | 0.0 | 9168.0 | wikitext | NULL |
| 1201.0 | American_Sign_Language | 0.0 | 0.0 | 66042.0 | wikitext | NULL |
| 1202.0 | Applet | 0.0 | 0.0 | 8698.0 | wikitext | NULL |
| 1203.0 | Alternate_history | 0.0 | 0.0 | 72917.0 | wikitext | NULL |
| 1205.0 | Atomic_orbitals | 1.0 | 0.0 | 79.0 | wikitext | NULL |
| 1206.0 | Atomic_orbital | 0.0 | 0.0 | 83171.0 | wikitext | NULL |
| 1207.0 | Amino_acid | 0.0 | 0.0 | 105700.0 | wikitext | NULL |
| 1208.0 | Alan_Turing | 0.0 | 0.0 | 139444.0 | wikitext | NULL |
| 1209.0 | Area | 0.0 | 0.0 | 45136.0 | wikitext | NULL |
| 1210.0 | Astronomical_unit | 0.0 | 0.0 | 54620.0 | wikitext | NULL |
| 1212.0 | Artist | 0.0 | 0.0 | 7688.0 | wikitext | NULL |
| 1213.0 | Actaeon | 0.0 | 0.0 | 27501.0 | wikitext | NULL |
| 1214.0 | Anglicanism | 0.0 | 0.0 | 144236.0 | wikitext | NULL |
| 1216.0 | Athens | 0.0 | 0.0 | 181240.0 | wikitext | NULL |
| 1217.0 | Anguilla | 0.0 | 0.0 | 60587.0 | wikitext | NULL |
| 1220.0 | Anguilla/Transnational_issues | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 1221.0 | Anguilla/Military | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 1223.0 | Telecommunications_in_Anguilla | 0.0 | 0.0 | 4827.0 | wikitext | NULL |
| 1227.0 | Ashmore_and_Cartier_Islands | 0.0 | 0.0 | 17896.0 | wikitext | NULL |
| 1228.0 | Ashmore_and_Cartier_Islands/Geography | 1.0 | 0.0 | 118.0 | wikitext | NULL |
| 1229.0 | Ashmore_and_Cartier_Islands/People | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1230.0 | Ashmore_and_Cartier_Islands/Government | 1.0 | 0.0 | 119.0 | wikitext | NULL |
| 1231.0 | Ashmore_and_Cartier_Islands/Transportation | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1232.0 | Ashmore_and_Cartier_Islands/Economy | 1.0 | 0.0 | 130.0 | wikitext | NULL |
| 1233.0 | Ashmore_and_Cartier_Islands/Military | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1234.0 | Acoustic_theory | 0.0 | 0.0 | 11785.0 | wikitext | NULL |
| 1235.0 | Alexander_Mackenzie_(politician) | 0.0 | 0.0 | 31828.0 | wikitext | NULL |
| 1238.0 | Atomic_bomb | 1.0 | 0.0 | 103.0 | wikitext | NULL |
| 1239.0 | Ashoka | 0.0 | 0.0 | 145168.0 | wikitext | NULL |
| 1241.0 | American_(word) | 0.0 | 0.0 | 45428.0 | wikitext | NULL |
| 1242.0 | Ada_(programming_language) | 0.0 | 0.0 | 57549.0 | wikitext | NULL |
| 1245.0 | Alpha_ray | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 1246.0 | Alfonso_Aráu | 1.0 | 0.0 | 26.0 | wikitext | NULL |
| 1247.0 | Alfonso_Cuarón | 0.0 | 0.0 | 27492.0 | wikitext | NULL |
| 1252.0 | Arianism | 0.0 | 0.0 | 80978.0 | wikitext | NULL |
| 1254.0 | August_1 | 0.0 | 0.0 | 52683.0 | wikitext | NULL |
| 1255.0 | Astronomical_Units | 1.0 | 0.0 | 130.0 | wikitext | NULL |
| 1256.0 | Antoninus_Pius | 0.0 | 0.0 | 71848.0 | wikitext | NULL |
| 1259.0 | August_3 | 0.0 | 0.0 | 42085.0 | wikitext | NULL |
| 1260.0 | Advanced_Encryption_Standard | 0.0 | 0.0 | 48743.0 | wikitext | NULL |
| 1261.0 | April_26 | 0.0 | 0.0 | 46939.0 | wikitext | NULL |
| 1262.0 | Argot | 1.0 | 0.0 | 181.0 | wikitext | NULL |
| 1264.0 | Anisotropy | 0.0 | 0.0 | 20704.0 | wikitext | NULL |
| 1267.0 | Alpha_decay | 0.0 | 0.0 | 18823.0 | wikitext | NULL |
| 1268.0 | AI | 1.0 | 0.0 | 157.0 | wikitext | NULL |
| 1270.0 | Extreme_poverty | 0.0 | 0.0 | 59250.0 | wikitext | NULL |
| 1271.0 | Analytical_Engine | 0.0 | 0.0 | 39177.0 | wikitext | NULL |
| 1273.0 | Augustus | 0.0 | 0.0 | 144918.0 | wikitext | NULL |
| 1274.0 | Geography_of_Antarctica | 0.0 | 0.0 | 22878.0 | wikitext | NULL |
| 1276.0 | Economy_of_Antarctica | 1.0 | 0.0 | 243.0 | wikitext | NULL |
| 1277.0 | Government_of_Antarctica | 1.0 | 0.0 | 37.0 | wikitext | NULL |
| 1279.0 | Transport_in_Antarctica | 0.0 | 0.0 | 11873.0 | wikitext | NULL |
| 1280.0 | Military_of_Antarctica | 1.0 | 0.0 | 48.0 | wikitext | NULL |
| 1285.0 | Geography_of_Alabama | 0.0 | 0.0 | 15547.0 | wikitext | NULL |
| 1286.0 | List_of_governors_of_Alabama | 0.0 | 0.0 | 60829.0 | wikitext | NULL |
| 1288.0 | Apocrypha | 0.0 | 0.0 | 60465.0 | wikitext | NULL |
| 1290.0 | Antartic_Treaty | 1.0 | 0.0 | 129.0 | wikitext | NULL |
| 1291.0 | Antarctic_Treaty_System | 0.0 | 0.0 | 42723.0 | wikitext | NULL |
| 1292.0 | Algernon_Swinburne | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1293.0 | Alfred_Lawson | 0.0 | 0.0 | 16942.0 | wikitext | NULL |
| 1295.0 | ALCS | 1.0 | 0.0 | 49.0 | wikitext | NULL |
| 1297.0 | Apocrypha/Tanakh | 1.0 | 0.0 | 78.0 | wikitext | NULL |
| 1298.0 | Ames,_Iowa | 0.0 | 0.0 | 55406.0 | wikitext | NULL |
| 1299.0 | Abbadides | 1.0 | 0.0 | 29.0 | wikitext | NULL |
| 1300.0 | Abalone | 0.0 | 0.0 | 63093.0 | wikitext | NULL |
| 1301.0 | Abbess | 0.0 | 0.0 | 13449.0 | wikitext | NULL |
| 1302.0 | Human_abdomen | 1.0 | 0.0 | 90.0 | wikitext | NULL |
| 1303.0 | Abdominal_surgery | 0.0 | 0.0 | 7650.0 | wikitext | NULL |
| 1304.0 | Abduction | 0.0 | 0.0 | 2669.0 | wikitext | NULL |
| 1305.0 | Abensberg | 0.0 | 0.0 | 16290.0 | wikitext | NULL |
| 1306.0 | Arminianism | 0.0 | 0.0 | 82187.0 | wikitext | NULL |
| 1307.0 | The_Alan_Parsons_Project | 0.0 | 0.0 | 21560.0 | wikitext | NULL |
| 1309.0 | Almost_all | 0.0 | 0.0 | 25415.0 | wikitext | NULL |
| 1311.0 | Ada_Byron's_notes_on_the_analytical_engine | 1.0 | 0.0 | 86.0 | wikitext | NULL |
| 1312.0 | Augustine | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1313.0 | Aromatic_compound | 0.0 | 0.0 | 12131.0 | wikitext | NULL |
| 1315.0 | Abbey | 0.0 | 0.0 | 30916.0 | wikitext | NULL |
| 1316.0 | Annales_school | 0.0 | 0.0 | 37725.0 | wikitext | NULL |
| 1317.0 | Antimatter | 0.0 | 0.0 | 74559.0 | wikitext | NULL |
| 1321.0 | Antonio_Gaudi/Sagrada_Familia | 1.0 | 0.0 | 82.0 | wikitext | NULL |
| 1322.0 | Casa_Batlló | 0.0 | 0.0 | 23318.0 | wikitext | NULL |
| 1324.0 | Park_Güell | 0.0 | 0.0 | 15495.0 | wikitext | NULL |
| 1325.0 | Casa_Milà | 0.0 | 0.0 | 39346.0 | wikitext | NULL |
| 1327.0 | Antiparticle | 0.0 | 0.0 | 20321.0 | wikitext | NULL |
| 1328.0 | A.D. | 1.0 | 0.0 | 80.0 | wikitext | NULL |
| 1331.0 | Arabian_Prince | 0.0 | 0.0 | 12794.0 | wikitext | NULL |
| 1332.0 | August_7 | 0.0 | 0.0 | 55009.0 | wikitext | NULL |
| 1333.0 | August_8 | 0.0 | 0.0 | 49211.0 | wikitext | NULL |
| 1334.0 | April_16 | 0.0 | 0.0 | 54925.0 | wikitext | NULL |
| 1335.0 | Associative_property | 0.0 | 0.0 | 25928.0 | wikitext | NULL |
| 1336.0 | The_Apache_Software_Foundation | 0.0 | 0.0 | 11890.0 | wikitext | NULL |
| 1338.0 | Americans_with_Disabilities_Act_of_1990 | 0.0 | 0.0 | 89286.0 | wikitext | NULL |
| 1339.0 | Americans_with_Disabilities_Act_of_1990/Findings_and_Purposes | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 1340.0 | Americans_with_Disabilities_Act_of_1990/Definitions | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 1341.0 | Americans_with_Disabilities_Act_of_1990/Title_III | 1.0 | 0.0 | 73.0 | wikitext | NULL |
| 1342.0 | A.D | 1.0 | 0.0 | 82.0 | wikitext | NULL |
| 1344.0 | Apple_I | 0.0 | 0.0 | 44379.0 | wikitext | NULL |
| 1345.0 | Apache_webserver | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1346.0 | Apatosaurus | 0.0 | 0.0 | 90670.0 | wikitext | NULL |
| 1347.0 | Allosaurus | 0.0 | 0.0 | 121497.0 | wikitext | NULL |
| 1348.0 | AK-47 | 0.0 | 0.0 | 141766.0 | wikitext | NULL |
| 1349.0 | Atanasoff–Berry_computer | 0.0 | 0.0 | 23497.0 | wikitext | NULL |
| 1354.0 | Andes | 0.0 | 0.0 | 54780.0 | wikitext | NULL |
| 1355.0 | Anderida | 1.0 | 0.0 | 23.0 | wikitext | NULL |
| 1356.0 | Ancylopoda | 0.0 | 0.0 | 2358.0 | wikitext | NULL |
| 1358.0 | Anchor | 0.0 | 0.0 | 52859.0 | wikitext | NULL |
| 1359.0 | Anbar_(town) | 0.0 | 0.0 | 12631.0 | wikitext | NULL |
| 1360.0 | Anazarbus | 0.0 | 0.0 | 17119.0 | wikitext | NULL |
| 1361.0 | Anagram | 0.0 | 0.0 | 33706.0 | wikitext | NULL |
| 1362.0 | Anadyr_(river) | 0.0 | 0.0 | 7337.0 | wikitext | NULL |
| 1363.0 | André-Marie_Ampère | 0.0 | 0.0 | 20216.0 | wikitext | NULL |
| 1365.0 | Ammonia | 0.0 | 0.0 | 148235.0 | wikitext | NULL |
| 1366.0 | Amethyst | 0.0 | 0.0 | 27267.0 | wikitext | NULL |
| 1367.0 | Albertosaurus | 0.0 | 0.0 | 58698.0 | wikitext | NULL |
| 1368.0 | Assembly_language | 0.0 | 0.0 | 90003.0 | wikitext | NULL |
| 1369.0 | Ambrosia | 0.0 | 0.0 | 12915.0 | wikitext | NULL |
| 1370.0 | Ambrose | 0.0 | 0.0 | 103100.0 | wikitext | NULL |
| 1371.0 | Ambracia | 0.0 | 0.0 | 6319.0 | wikitext | NULL |
| 1372.0 | Amber | 0.0 | 0.0 | 59547.0 | wikitext | NULL |
| 1373.0 | Amalaric | 0.0 | 0.0 | 5878.0 | wikitext | NULL |
| 1374.0 | Alphorn | 0.0 | 0.0 | 12956.0 | wikitext | NULL |
| 1376.0 | Army | 0.0 | 0.0 | 30058.0 | wikitext | NULL |
| 1380.0 | Alligatoridae | 0.0 | 0.0 | 20628.0 | wikitext | NULL |
| 1383.0 | Alder | 0.0 | 0.0 | 23813.0 | wikitext | NULL |
| 1384.0 | Amos_Bronson_Alcott | 0.0 | 0.0 | 51959.0 | wikitext | NULL |
| 1386.0 | Arachnophobia | 0.0 | 0.0 | 16131.0 | wikitext | NULL |
| 1387.0 | Alabaster | 0.0 | 0.0 | 31341.0 | wikitext | NULL |
| 1389.0 | Ahab | 0.0 | 0.0 | 16568.0 | wikitext | NULL |
| 1391.0 | ASIC_(disambiguation) | 0.0 | 0.0 | 1189.0 | wikitext | NULL |
| 1392.0 | Dasyproctidae | 0.0 | 0.0 | 4787.0 | wikitext | NULL |
| 1394.0 | Algol | 0.0 | 0.0 | 32666.0 | wikitext | NULL |
| 1395.0 | Amazing_Grace | 0.0 | 0.0 | 64133.0 | wikitext | NULL |
| 1397.0 | AOL | 0.0 | 0.0 | 104064.0 | wikitext | NULL |
| 1399.0 | ADHD | 1.0 | 0.0 | 154.0 | wikitext | NULL |
| 1400.0 | Anno_Domini | 0.0 | 0.0 | 31355.0 | wikitext | NULL |
| 1404.0 | AV | 0.0 | 0.0 | 3210.0 | wikitext | NULL |
| 1406.0 | Amino_group | 1.0 | 0.0 | 19.0 | wikitext | NULL |
| 1407.0 | Antony_van_Leeuwenhook | 1.0 | 0.0 | 98.0 | wikitext | NULL |
| 1408.0 | Alcuin | 0.0 | 0.0 | 41674.0 | wikitext | NULL |
| 1409.0 | Angilbert | 0.0 | 0.0 | 7855.0 | wikitext | NULL |
| 1410.0 | Antony_van_Leeuwenhoek | 1.0 | 0.0 | 102.0 | wikitext | NULL |
| 1412.0 | Amine | 0.0 | 0.0 | 32725.0 | wikitext | NULL |
| 1415.0 | Adrian_I | 1.0 | 0.0 | 27.0 | wikitext | NULL |
| 1416.0 | April_29 | 0.0 | 0.0 | 52049.0 | wikitext | NULL |
| 1417.0 | August_14 | 0.0 | 0.0 | 94093.0 | wikitext | NULL |
| 1418.0 | Absolute_zero | 0.0 | 0.0 | 36868.0 | wikitext | NULL |
| 1419.0 | Adiabatic_process | 0.0 | 0.0 | 40636.0 | wikitext | NULL |
| 1422.0 | Amide | 0.0 | 0.0 | 21607.0 | wikitext | NULL |
| 1423.0 | Animism | 0.0 | 0.0 | 68318.0 | wikitext | NULL |
| 1425.0 | Antonio_Vivaldi | 0.0 | 0.0 | 42116.0 | wikitext | NULL |
| 1426.0 | Adrian_II | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 1428.0 | Adrian | 0.0 | 0.0 | 45416.0 | wikitext | NULL |
| 1429.0 | Adrian_IV | 1.0 | 0.0 | 28.0 | wikitext | NULL |
| 1433.0 | Aare | 0.0 | 0.0 | 13942.0 | wikitext | NULL |
| 1434.0 | Abgar | 1.0 | 0.0 | 21.0 | wikitext | NULL |
| 1435.0 | Abbotsford,_Scottish_Borders | 0.0 | 0.0 | 15773.0 | wikitext | NULL |
| 1436.0 | Abraham | 0.0 | 0.0 | 73358.0 | wikitext | NULL |
| 1437.0 | Abraxas | 0.0 | 0.0 | 46069.0 | wikitext | NULL |
| 1438.0 | Absalom | 0.0 | 0.0 | 32027.0 | wikitext | NULL |
| 1439.0 | Abydos | 0.0 | 0.0 | 534.0 | wikitext | NULL |
| 1440.0 | Abydos,_Egypt | 0.0 | 0.0 | 30139.0 | wikitext | NULL |
| 1441.0 | Abydos_(Hellespont) | 0.0 | 0.0 | 33933.0 | wikitext | NULL |
| 1442.0 | August_15 | 0.0 | 0.0 | 58362.0 | wikitext | NULL |
| 1445.0 | Acacia_sensu_lato | 0.0 | 0.0 | 37833.0 | wikitext | NULL |
| 1446.0 | Acapulco | 0.0 | 0.0 | 93594.0 | wikitext | NULL |
| 1448.0 | August_16 | 0.0 | 0.0 | 51549.0 | wikitext | NULL |
| 1449.0 | Alan_Kay | 0.0 | 0.0 | 23914.0 | wikitext | NULL |
| 1451.0 | APL_(programming_language) | 0.0 | 0.0 | 97258.0 | wikitext | NULL |
| 1453.0 | ALGOL | 0.0 | 0.0 | 37077.0 | wikitext | NULL |
| 1456.0 | AWK | 0.0 | 0.0 | 39479.0 | wikitext | NULL |
| 1457.0 | Alzheimers_disease | 1.0 | 0.0 | 97.0 | wikitext | NULL |
| 1459.0 | Ascorbic_Acid | 1.0 | 0.0 | 75.0 | wikitext | NULL |
| 1460.0 | Asgard | 0.0 | 0.0 | 16979.0 | wikitext | NULL |
| 1461.0 | Apollo_program | 0.0 | 0.0 | 151235.0 | wikitext | NULL |
| 1466.0 | Assault | 0.0 | 0.0 | 47559.0 | wikitext | NULL |
| 1476.0 | Australian_Prime_Ministers | 1.0 | 0.0 | 41.0 | wikitext | NULL |
| 1478.0 | Álfheimr | 0.0 | 0.0 | 2831.0 | wikitext | NULL |
| 1482.0 | Ask_and_Embla | 0.0 | 0.0 | 12669.0 | wikitext | NULL |
| 1484.0 | Alabama_River | 0.0 | 0.0 | 7724.0 | wikitext | NULL |
| 1485.0 | Alain_de_Lille | 0.0 | 0.0 | 15501.0 | wikitext | NULL |
| 1486.0 | Alemanni | 0.0 | 0.0 | 45699.0 | wikitext | NULL |
| 1488.0 | NYSE_American | 0.0 | 0.0 | 28351.0 | wikitext | NULL |
| 1490.0 | August_17 | 0.0 | 0.0 | 50297.0 | wikitext | NULL |
| 1491.0 | August_12 | 0.0 | 0.0 | 49007.0 | wikitext | NULL |
| 1494.0 | Alfred_Russel_Wallace | 0.0 | 0.0 | 116378.0 | wikitext | NULL |
| 1495.0 | Australian_Labor_Party | 0.0 | 0.0 | 97028.0 | wikitext | NULL |
| 1496.0 | August_18 | 0.0 | 0.0 | 46431.0 | wikitext | NULL |
| 1497.0 | August_19 | 0.0 | 0.0 | 52053.0 | wikitext | NULL |
| 1499.0 | August_21 | 0.0 | 0.0 | 42670.0 | wikitext | NULL |
| 1500.0 | Dodo_(Alice's_Adventures_in_Wonderland) | 0.0 | 0.0 | 7678.0 | wikitext | NULL |
| 1501.0 | Lory_(disambiguation) | 0.0 | 0.0 | 773.0 | wikitext | NULL |
| 1502.0 | Eaglet_(Alice's_Adventures_in_Wonderland) | 1.0 | 0.0 | 170.0 | wikitext | NULL |
| 1504.0 | Albert | 0.0 | 0.0 | 3010.0 | wikitext | NULL |
| 1505.0 | Albert_I | 0.0 | 0.0 | 1247.0 | wikitext | NULL |
| 1506.0 | Albert_II | 0.0 | 0.0 | 1483.0 | wikitext | NULL |
| 1507.0 | Albert_III | 0.0 | 0.0 | 653.0 | wikitext | NULL |
| 1508.0 | Albert_Alcibiades,_Margrave_of_Brandenburg-Kulmbach | 0.0 | 0.0 | 6485.0 | wikitext | NULL |
| 1509.0 | Albert_the_Bear | 0.0 | 0.0 | 10108.0 | wikitext | NULL |
| 1511.0 | Albert_I_of_Hapsburg | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1513.0 | Albert_of_Brandenburg | 0.0 | 0.0 | 11903.0 | wikitext | NULL |
| 1514.0 | Albert,_Duke_of_Prussia | 0.0 | 0.0 | 21034.0 | wikitext | NULL |
| 1515.0 | Albert_III,_Elector_of_Saxony | 1.0 | 0.0 | 40.0 | wikitext | NULL |
| 1516.0 | Albert_the_Degenerate | 1.0 | 0.0 | 44.0 | wikitext | NULL |
| 1517.0 | Albert_Of_Aix | 1.0 | 0.0 | 92.0 | wikitext | NULL |
| 1519.0 | August_25 | 0.0 | 0.0 | 50492.0 | wikitext | NULL |
| 1520.0 | Aachen | 0.0 | 0.0 | 98165.0 | wikitext | NULL |
| 1523.0 | Agate | 0.0 | 0.0 | 18500.0 | wikitext | NULL |
| 1525.0 | Aspirin | 0.0 | 0.0 | 148374.0 | wikitext | NULL |
| 1526.0 | Abner | 0.0 | 0.0 | 19935.0 | wikitext | NULL |
| 1527.0 | Ahmed_I | 0.0 | 0.0 | 30959.0 | wikitext | NULL |
| 1528.0 | Ahmed_II | 0.0 | 0.0 | 11022.0 | wikitext | NULL |
| 1529.0 | Ahmed_III | 0.0 | 0.0 | 36489.0 | wikitext | NULL |
| 1530.0 | Ainu_people | 0.0 | 0.0 | 160302.0 | wikitext | NULL |
| 1533.0 | Aix-la-Chapelle | 1.0 | 0.0 | 81.0 | wikitext | NULL |
| 1535.0 | Acorn_(fruit_of_the_oak_tree) | 1.0 | 0.0 | 19.0 | wikitext | NULL |
| 1536.0 | Acropolis | 0.0 | 0.0 | 14773.0 | wikitext | NULL |
| 1537.0 | Acupuncture | 0.0 | 0.0 | 198975.0 | wikitext | NULL |
| 1538.0 | Adder | 0.0 | 0.0 | 760.0 | wikitext | NULL |
| 1539.0 | Adirondacks | 1.0 | 0.0 | 95.0 | wikitext | NULL |
| 1540.0 | Aeneas | 0.0 | 0.0 | 34834.0 | wikitext | NULL |
| 1541.0 | April_13 | 0.0 | 0.0 | 43196.0 | wikitext | NULL |
| 1542.0 | Amaranth | 0.0 | 0.0 | 49948.0 | wikitext | NULL |
| 1543.0 | Agapanthus_africanus | 0.0 | 0.0 | 7739.0 | wikitext | NULL |
| 1544.0 | Agamemnon | 0.0 | 0.0 | 42460.0 | wikitext | NULL |
| 1545.0 | Aga_Khan_I | 0.0 | 0.0 | 15317.0 | wikitext | NULL |
| 1546.0 | Aga_Khan_III | 0.0 | 0.0 | 32478.0 | wikitext | NULL |
| 1547.0 | Agasias | 0.0 | 0.0 | 391.0 | wikitext | NULL |
| 1548.0 | Alexander_Agassiz | 0.0 | 0.0 | 17766.0 | wikitext | NULL |
| 1549.0 | Agathon | 0.0 | 0.0 | 8125.0 | wikitext | NULL |
| 1550.0 | Agesilaus_II | 0.0 | 0.0 | 42002.0 | wikitext | NULL |
| 1551.0 | Agis | 0.0 | 0.0 | 953.0 | wikitext | NULL |
| 1552.0 | Antonio_Agliardi | 0.0 | 0.0 | 6867.0 | wikitext | NULL |
| 1553.0 | Agnes_of_Merania | 0.0 | 0.0 | 3839.0 | wikitext | NULL |
| 1556.0 | Agrippina_the_Elder | 0.0 | 0.0 | 43683.0 | wikitext | NULL |
| 1557.0 | Agrippina_the_Younger | 0.0 | 0.0 | 44097.0 | wikitext | NULL |
| 1558.0 | American_Chinese_cuisine | 0.0 | 0.0 | 54573.0 | wikitext | NULL |
| 1559.0 | Ahenobarbus | 0.0 | 0.0 | 526.0 | wikitext | NULL |
| 1560.0 | Ahmad_Shah_Durrani | 0.0 | 0.0 | 51488.0 | wikitext | NULL |
| 1561.0 | Aidan_of_Dalriada | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 1563.0 | Arthur_Aikin | 0.0 | 0.0 | 5886.0 | wikitext | NULL |
| 1564.0 | Ailanthus | 0.0 | 0.0 | 4778.0 | wikitext | NULL |
| 1565.0 | Aimoin | 0.0 | 0.0 | 2661.0 | wikitext | NULL |
| 1566.0 | Akkadian_Empire | 0.0 | 0.0 | 83572.0 | wikitext | NULL |
| 1567.0 | Ajax_the_Lesser | 0.0 | 0.0 | 15739.0 | wikitext | NULL |
| 1568.0 | Ajax_the_Great | 0.0 | 0.0 | 18066.0 | wikitext | NULL |
| 1569.0 | Ajax | 0.0 | 0.0 | 5793.0 | wikitext | NULL |
| 1570.0 | Alaric_I | 0.0 | 0.0 | 47986.0 | wikitext | NULL |
| 1571.0 | Alaric_II | 0.0 | 0.0 | 9417.0 | wikitext | NULL |
| 1572.0 | Albategnius | 1.0 | 0.0 | 24.0 | wikitext | NULL |
| 1573.0 | Albertus_Magnus | 0.0 | 0.0 | 44055.0 | wikitext | NULL |
| 1575.0 | Alboin | 0.0 | 0.0 | 53199.0 | wikitext | NULL |
| 1576.0 | Afonso_de_Albuquerque | 0.0 | 0.0 | 62412.0 | wikitext | NULL |
| 1577.0 | Alcaeus_of_Mytilene | 0.0 | 0.0 | 29351.0 | wikitext | NULL |
| 1578.0 | Alcamenes | 0.0 | 0.0 | 3848.0 | wikitext | NULL |
| 1579.0 | Alcmene | 0.0 | 0.0 | 13642.0 | wikitext | NULL |
| 1580.0 | Alcidamas | 0.0 | 0.0 | 5568.0 | wikitext | NULL |
| 1581.0 | Aldine_Press | 0.0 | 0.0 | 22393.0 | wikitext | NULL |
| 1583.0 | Ealdred_(archbishop_of_York) | 0.0 | 0.0 | 42133.0 | wikitext | NULL |
| 1585.0 | Alexander_I_of_Epirus | 0.0 | 0.0 | 5238.0 | wikitext | NULL |
| 1586.0 | Alexander_Balas | 0.0 | 0.0 | 21296.0 | wikitext | NULL |
| 1587.0 | Alexander_of_Pherae | 0.0 | 0.0 | 10046.0 | wikitext | NULL |
| 1588.0 | Alexander_II_of_Epirus | 0.0 | 0.0 | 5666.0 | wikitext | NULL |
| 1589.0 | Alexander_Jagiellon | 0.0 | 0.0 | 9403.0 | wikitext | NULL |
| 1592.0 | Alexander_III_of_Russia | 0.0 | 0.0 | 67769.0 | wikitext | NULL |
| 1593.0 | Alexander_I_of_Scotland | 0.0 | 0.0 | 10986.0 | wikitext | NULL |
| 1594.0 | Alexander_II_of_Scotland | 0.0 | 0.0 | 12643.0 | wikitext | NULL |
| 1595.0 | Alexander_I_of_Serbia | 0.0 | 0.0 | 15334.0 | wikitext | NULL |
| 1596.0 | Alexander_III_of_Scotland | 0.0 | 0.0 | 19966.0 | wikitext | NULL |
| 1597.0 | Alexander_of_Greece_(disambiguation) | 0.0 | 0.0 | 444.0 | wikitext | NULL |
| 1599.0 | Alexander_of_Aphrodisias | 0.0 | 0.0 | 23192.0 | wikitext | NULL |
| 1600.0 | Severus_Alexander | 0.0 | 0.0 | 38183.0 | wikitext | NULL |
| 1601.0 | Alexander | 0.0 | 0.0 | 29504.0 | wikitext | NULL |
| 1602.0 | Alexander_I | 0.0 | 0.0 | 1105.0 | wikitext | NULL |
| 1603.0 | Alexander_II | 0.0 | 0.0 | 901.0 | wikitext | NULL |
| 1604.0 | Alexander_III | 0.0 | 0.0 | 948.0 | wikitext | NULL |
| 1605.0 | Alexander_Aetolus | 0.0 | 0.0 | 4109.0 | wikitext | NULL |
| 1606.0 | Alexander_Jannaeus | 0.0 | 0.0 | 19806.0 | wikitext | NULL |
| 1607.0 | Alexander_IV | 0.0 | 0.0 | 367.0 | wikitext | NULL |
| 1608.0 | Alexander_V | 0.0 | 0.0 | 223.0 | wikitext | NULL |
| 1609.0 | Alexander_VI | 1.0 | 0.0 | 31.0 | wikitext | NULL |
| 1610.0 | Alexander_VII | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1611.0 | Alexander_VIII | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1612.0 | Alexandrists | 0.0 | 0.0 | 1609.0 | wikitext | NULL |
| 1613.0 | Alexios_I_Komnenos | 0.0 | 0.0 | 38469.0 | wikitext | NULL |
| 1614.0 | Alexis_(poet) | 0.0 | 0.0 | 10392.0 | wikitext | NULL |
| 1615.0 | Alexios_II_Komnenos | 0.0 | 0.0 | 9228.0 | wikitext | NULL |
| 1616.0 | Alexios_III_Angelos | 0.0 | 0.0 | 13836.0 | wikitext | NULL |
| 1617.0 | Alexios_V_Doukas | 0.0 | 0.0 | 17897.0 | wikitext | NULL |
| 1620.0 | Alexei_Petrovich,_Tsarevich_of_Russia | 0.0 | 0.0 | 15686.0 | wikitext | NULL |
| 1623.0 | Andrew_Jackson | 0.0 | 0.0 | 179696.0 | wikitext | NULL |
| 1624.0 | Andrew_Johnson | 0.0 | 0.0 | 124793.0 | wikitext | NULL |
| 1625.0 | Aleksandr_Solzhenitsyn | 0.0 | 0.0 | 118674.0 | wikitext | NULL |
| 1626.0 | Aleksandr_Isaevich_Solzhenitsyn | 1.0 | 0.0 | 36.0 | wikitext | NULL |
| 1627.0 | Aberdeen | 0.0 | 0.0 | 147083.0 | wikitext | NULL |
| 1628.0 | August_23 | 0.0 | 0.0 | 49176.0 | wikitext | NULL |
| 1629.0 | August_24 | 0.0 | 0.0 | 54501.0 | wikitext | NULL |
| 1633.0 | Antipope | 0.0 | 0.0 | 32370.0 | wikitext | NULL |
| 1634.0 | Aquaculture | 0.0 | 0.0 | 125421.0 | wikitext | NULL |
| 1635.0 | Kolmogorov_complexity | 0.0 | 0.0 | 41353.0 | wikitext | NULL |
| 1636.0 | Antoine_de_Saint-Exupery | 1.0 | 0.0 | 125.0 | wikitext | NULL |
| 1637.0 | Hymn_to_Proserpine | 0.0 | 0.0 | 2710.0 | wikitext | NULL |
| 1638.0 | The_Triumph_of_Time | 0.0 | 0.0 | 1751.0 | wikitext | NULL |
| 1639.0 | April_28 | 0.0 | 0.0 | 42485.0 | wikitext | NULL |
| 1640.0 | Alfred_the_Great | 0.0 | 0.0 | 121065.0 | wikitext | NULL |
| 1641.0 | Alfred_Ernest_Albert | 1.0 | 0.0 | 51.0 | wikitext | NULL |
| 1642.0 | Alessandro_Algardi | 0.0 | 0.0 | 14639.0 | wikitext | NULL |
| 1643.0 | Alger_of_Liège | 0.0 | 0.0 | 3139.0 | wikitext | NULL |
| 1644.0 | Algiers | 0.0 | 0.0 | 70559.0 | wikitext | NULL |
| 1645.0 | Ibn_al-Haytham | 0.0 | 0.0 | 120924.0 | wikitext | NULL |
| 1647.0 | Alessandro_Allori | 0.0 | 0.0 | 9650.0 | wikitext | NULL |
| 1649.0 | Almoravid_dynasty | 0.0 | 0.0 | 83925.0 | wikitext | NULL |
| 1650.0 | Aloe | 0.0 | 0.0 | 21387.0 | wikitext | NULL |
| 1651.0 | Alured_of_Berkeley | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1652.0 | Alyattes | 0.0 | 0.0 | 40930.0 | wikitext | NULL |
| 1653.0 | Age_of_consent | 0.0 | 0.0 | 56722.0 | wikitext | NULL |
| 1654.0 | Alypius_of_Antioch | 0.0 | 0.0 | 1756.0 | wikitext | NULL |
| 1655.0 | Amalasuintha | 0.0 | 0.0 | 11115.0 | wikitext | NULL |
| 1656.0 | Amalric_of_Bena | 0.0 | 0.0 | 6659.0 | wikitext | NULL |
| 1657.0 | Afonso_I_of_Portugal | 0.0 | 0.0 | 32525.0 | wikitext | NULL |
| 1658.0 | Afonso_II_of_Portugal | 0.0 | 0.0 | 9807.0 | wikitext | NULL |
| 1659.0 | Afonso_III_of_Portugal | 0.0 | 0.0 | 12744.0 | wikitext | NULL |
| 1660.0 | Afonso_IV_of_Portugal | 0.0 | 0.0 | 14233.0 | wikitext | NULL |
| 1661.0 | Afonso_V_of_Portugal | 0.0 | 0.0 | 19540.0 | wikitext | NULL |
| 1662.0 | Afonso_VI_of_Portugal | 0.0 | 0.0 | 8372.0 | wikitext | NULL |
| 1663.0 | Alphonso_I_of_Spain | 0.0 | 0.0 | 539.0 | wikitext | NULL |
| 1664.0 | Alfonso_II_of_Asturias | 0.0 | 0.0 | 5949.0 | wikitext | NULL |
| 1669.0 | Amarasimha | 0.0 | 0.0 | 3546.0 | wikitext | NULL |
| 1672.0 | Alphonso_VIII_of_Spain | 1.0 | 0.0 | 37.0 | wikitext | NULL |
| 1673.0 | Alfonso_IX_of_Spain | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1676.0 | Alfonso_XII | 0.0 | 0.0 | 27559.0 | wikitext | NULL |
| 1677.0 | Alfonso_XIII | 0.0 | 0.0 | 67834.0 | wikitext | NULL |
| 1678.0 | Alphonsus_a_Sancta_Maria | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 1679.0 | Alfonso_the_Battler | 0.0 | 0.0 | 27719.0 | wikitext | NULL |
| 1680.0 | Amaryllis | 0.0 | 0.0 | 17681.0 | wikitext | NULL |
| 1682.0 | Amasis_I | 1.0 | 0.0 | 22.0 | wikitext | NULL |
| 1683.0 | Alfonso_III_of_Aragon | 0.0 | 0.0 | 5951.0 | wikitext | NULL |
| 1684.0 | Alfonso_IV_of_Aragon | 0.0 | 0.0 | 9985.0 | wikitext | NULL |
| 1685.0 | Amasis_II | 0.0 | 0.0 | 17642.0 | wikitext | NULL |
| 1686.0 | Alfonso_V_of_Aragon | 0.0 | 0.0 | 22331.0 | wikitext | NULL |
| 1687.0 | Amathus | 0.0 | 0.0 | 17228.0 | wikitext | NULL |
| 1688.0 | Alphons | 0.0 | 0.0 | 11520.0 | wikitext | NULL |
| 1689.0 | Alfonso_I | 0.0 | 0.0 | 620.0 | wikitext | NULL |
| 1690.0 | Amati | 0.0 | 0.0 | 9132.0 | wikitext | NULL |
| 1691.0 | Alfonso_II | 0.0 | 0.0 | 504.0 | wikitext | NULL |
| 1692.0 | Alfonso_III | 0.0 | 0.0 | 320.0 | wikitext | NULL |
| 1694.0 | Alfonso_IV | 0.0 | 0.0 | 232.0 | wikitext | NULL |
| 1695.0 | Amazons | 0.0 | 0.0 | 72183.0 | wikitext | NULL |
| 1696.0 | Alfonso_V | 0.0 | 0.0 | 200.0 | wikitext | NULL |
| 1697.0 | Ambergris | 0.0 | 0.0 | 20295.0 | wikitext | NULL |
| 1698.0 | Ambiorix | 0.0 | 0.0 | 11792.0 | wikitext | NULL |
| 1699.0 | Alfonso_VI | 1.0 | 0.0 | 128.0 | wikitext | NULL |
| 1700.0 | August_Wilhelm_Ambros | 0.0 | 0.0 | 3510.0 | wikitext | NULL |
| 1701.0 | Amazon_River | 0.0 | 0.0 | 101421.0 | wikitext | NULL |
| 1702.0 | Alfred_of_Beverley | 0.0 | 0.0 | 3400.0 | wikitext | NULL |
| 1703.0 | Alphonso_VII | 1.0 | 0.0 | 46.0 | wikitext | NULL |
| 1704.0 | Alphonso_VIII | 1.0 | 0.0 | 37.0 | wikitext | NULL |
| 1705.0 | Alphonso_IX | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1706.0 | Alphonso_X | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 1707.0 | Alphonso_XI | 1.0 | 0.0 | 35.0 | wikitext | NULL |
| 1708.0 | Alphonso_XII | 1.0 | 0.0 | 25.0 | wikitext | NULL |
| 1709.0 | Alphonso_XIII | 1.0 | 0.0 | 26.0 | wikitext | NULL |
| 1710.0 | April_22 | 0.0 | 0.0 | 35182.0 | wikitext | NULL |
| 1711.0 | August_31 | 0.0 | 0.0 | 45180.0 | wikitext | NULL |
| 1714.0 | Autpert_Ambrose | 0.0 | 0.0 | 1669.0 | wikitext | NULL |
| 1715.0 | Abu_Bakr | 0.0 | 0.0 | 70130.0 | wikitext | NULL |
| 1716.0 | Ambrose_Traversari | 0.0 | 0.0 | 8920.0 | wikitext | NULL |
| 1717.0 | Ambrosians | 0.0 | 0.0 | 7217.0 | wikitext | NULL |
| 1718.0 | Ambrosiaster | 0.0 | 0.0 | 12639.0 | wikitext | NULL |
| 1719.0 | Ambrosius_Aurelianus | 0.0 | 0.0 | 47081.0 | wikitext | NULL |
| 1722.0 | Ammon | 0.0 | 0.0 | 28089.0 | wikitext | NULL |
| 1723.0 | Ammonius_Hermiae | 0.0 | 0.0 | 10918.0 | wikitext | NULL |
| 1724.0 | Ammonius_Saccas | 0.0 | 0.0 | 19454.0 | wikitext | NULL |
| 1726.0 | Book_of_Amos | 0.0 | 0.0 | 14545.0 | wikitext | NULL |
| 1727.0 | Amphipolis | 0.0 | 0.0 | 25676.0 | wikitext | NULL |
| 1728.0 | Amram | 0.0 | 0.0 | 10144.0 | wikitext | NULL |
| 1729.0 | Amyntas_I_of_Macedon | 0.0 | 0.0 | 5010.0 | wikitext | NULL |
| 1730.0 | Amyntas_III_of_Macedon | 0.0 | 0.0 | 8817.0 | wikitext | NULL |
| 1732.0 | Anacharsis | 0.0 | 0.0 | 10183.0 | wikitext | NULL |
| 1733.0 | Anacreon_(poet) | 1.0 | 0.0 | 22.0 | wikitext | NULL |
| 1734.0 | Anah | 0.0 | 0.0 | 16082.0 | wikitext | NULL |
| 1735.0 | Ānanda | 0.0 | 0.0 | 126619.0 | wikitext | NULL |
| 1737.0 | Anaxagoras | 0.0 | 0.0 | 25323.0 | wikitext | NULL |
| 1738.0 | Anaxarchus | 0.0 | 0.0 | 4932.0 | wikitext | NULL |
| 1740.0 | Ancyra_(planthopper) | 0.0 | 0.0 | 3357.0 | wikitext | NULL |
| 1742.0 | Anastasius_I | 0.0 | 0.0 | 271.0 | wikitext | NULL |
| 1743.0 | Anastasius_II | 0.0 | 0.0 | 271.0 | wikitext | NULL |
| 1744.0 | Anastasius_III | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1745.0 | Anastasius_IV | 1.0 | 0.0 | 32.0 | wikitext | NULL |
| 1746.0 | Anaximenes_of_Lampsacus | 0.0 | 0.0 | 9465.0 | wikitext | NULL |
| 1747.0 | Anastasius | 0.0 | 0.0 | 4795.0 | wikitext | NULL |
| 1748.0 | Anaximenes_of_Miletus | 0.0 | 0.0 | 24822.0 | wikitext | NULL |
| 1749.0 | Ancus_Marcius | 0.0 | 0.0 | 12201.0 | wikitext | NULL |
| 1750.0 | Andaman_Islands | 0.0 | 0.0 | 51900.0 | wikitext | NULL |
| 1751.0 | Alexander_Anderson_(mathematician) | 0.0 | 0.0 | 6103.0 | wikitext | NULL |
| 1752.0 | Andocides | 0.0 | 0.0 | 12142.0 | wikitext | NULL |
| 1754.0 | Andrea_Andreani | 0.0 | 0.0 | 7733.0 | wikitext | NULL |
| 1755.0 | Andrew_II_of_Hungary | 0.0 | 0.0 | 60429.0 | wikitext | NULL |
| 1756.0 | An_Enquiry_Concerning_Human_Understanding | 0.0 | 0.0 | 24073.0 | wikitext | NULL |
| 1758.0 | André_de_Longjumeau | 0.0 | 0.0 | 8241.0 | wikitext | NULL |
| 1759.0 | Andriscus | 0.0 | 0.0 | 25446.0 | wikitext | NULL |
| 1760.0 | Andronikos_III_Palaiologos | 0.0 | 0.0 | 15960.0 | wikitext | NULL |
| 1761.0 | Andronikos_II_Palaiologos | 0.0 | 0.0 | 21319.0 | wikitext | NULL |
| 1762.0 | Andronikos_I_Komnenos | 0.0 | 0.0 | 26966.0 | wikitext | NULL |
| 1763.0 | Andronicus_of_Cyrrhus | 0.0 | 0.0 | 2105.0 | wikitext | NULL |
| 1764.0 | Andronicus_of_Rhodes | 0.0 | 0.0 | 3687.0 | wikitext | NULL |
| 1765.0 | Andronicus | 0.0 | 0.0 | 2282.0 | wikitext | NULL |
| 1766.0 | Asteroid_Belt | 1.0 | 0.0 | 92.0 | wikitext | NULL |
| 1767.0 | Ammianus_Marcellinus | 0.0 | 0.0 | 22026.0 | wikitext | NULL |
| 1768.0 | ALICE | 1.0 | 0.0 | 171.0 | wikitext | NULL |
| 1769.0 | An_Enquiry_Concerning_Human_Understanding/Text | 1.0 | 0.0 | 55.0 | wikitext | NULL |
| 1770.0 | Apollo_13 | 0.0 | 0.0 | 116154.0 | wikitext | NULL |
| 1771.0 | Apollo_Program | 1.0 | 0.0 | 93.0 | wikitext | NULL |
| 1772.0 | Arthritus | 1.0 | 0.0 | 23.0 | wikitext | NULL |
| 1773.0 | Apollo_7 | 0.0 | 0.0 | 59737.0 | wikitext | NULL |
| 1774.0 | Apollo_9 | 0.0 | 0.0 | 59547.0 | wikitext | NULL |
| 1775.0 | Applied_discrete_math | 1.0 | 0.0 | 34.0 | wikitext | NULL |
| 1776.0 | Arthritis | 0.0 | 0.0 | 60256.0 | wikitext | NULL |
| 1777.0 | April_2 | 0.0 | 0.0 | 50691.0 | wikitext | NULL |
| 1778.0 | Acetylene | 0.0 | 0.0 | 43280.0 | wikitext | NULL |
| 1779.0 | Alfred | 0.0 | 0.0 | 1890.0 | wikitext | NULL |
| 1781.0 | August_28 | 0.0 | 0.0 | 46125.0 | wikitext | NULL |
| 1786.0 | Arabic_numerals | 0.0 | 0.0 | 31303.0 | wikitext | NULL |
| 1787.0 | April_9 | 0.0 | 0.0 | 55129.0 | wikitext | NULL |
| 1788.0 | ABM | 0.0 | 0.0 | 1563.0 | wikitext | NULL |
| 1789.0 | Apuleius | 0.0 | 0.0 | 21943.0 | wikitext | NULL |
| 1790.0 | Alexander_Selkirk | 0.0 | 0.0 | 30796.0 | wikitext | NULL |
| 1791.0 | Anti-ballistic_missile | 0.0 | 0.0 | 88548.0 | wikitext | NULL |
| 1793.0 | August_29 | 0.0 | 0.0 | 47528.0 | wikitext | NULL |
| 1794.0 | August_30 | 0.0 | 0.0 | 44669.0 | wikitext | NULL |
| 1797.0 | Acre | 0.0 | 0.0 | 35055.0 | wikitext | NULL |
| 1799.0 | ATP | 0.0 | 0.0 | 2186.0 | wikitext | NULL |
| 1800.0 | Adenosine_triphosphate | 0.0 | 0.0 | 44099.0 | wikitext | NULL |
| 1802.0 | Ægir | 0.0 | 0.0 | 19706.0 | wikitext | NULL |
| 1805.0 | Antibiotic | 0.0 | 0.0 | 142427.0 | wikitext | NULL |
| 1806.0 | Arnold_Schwarzenegger | 0.0 | 0.0 | 225011.0 | wikitext | NULL |
| 1807.0 | ASA | 0.0 | 0.0 | 4995.0 | wikitext | NULL |
| 1809.0 | Aquinas | 1.0 | 0.0 | 99.0 | wikitext | NULL |
| 1810.0 | Actium | 0.0 | 0.0 | 3562.0 | wikitext | NULL |
| 1811.0 | Amide_hydrolysis | 1.0 | 0.0 | 68.0 | wikitext | NULL |
| 1812.0 | Amway | 0.0 | 0.0 | 106066.0 | wikitext | NULL |
| 1814.0 | Adam_Smith | 0.0 | 0.0 | 107560.0 | wikitext | NULL |
| 1821.0 | Antoine_Laurent_Lavoisier | 1.0 | 0.0 | 85.0 | wikitext | NULL |
| 1822.0 | Antoine_Lavoisier | 0.0 | 0.0 | 75434.0 | wikitext | NULL |
| 1824.0 | A_roll | 1.0 | 0.0 | 21.0 | wikitext | NULL |
| 1825.0 | Hermann_Kolbe | 0.0 | 0.0 | 16697.0 | wikitext | NULL |
| 1826.0 | April_18 | 0.0 | 0.0 | 33597.0 | wikitext | NULL |
| 1827.0 | April_23 | 0.0 | 0.0 | 46616.0 | wikitext | NULL |
| 1828.0 | Amitabh_Bachchan | 0.0 | 0.0 | 127861.0 | wikitext | NULL |
| 1830.0 | Air_Pollution | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 1831.0 | Antarctic-Environmental_Protocol | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 1832.0 | Allomorph | 0.0 | 0.0 | 8722.0 | wikitext | NULL |
| 1833.0 | American_bias | 1.0 | 0.0 | 27.0 | wikitext | NULL |
| 1834.0 | Allophone | 0.0 | 0.0 | 24419.0 | wikitext | NULL |
| 1835.0 | Affix | 0.0 | 0.0 | 11897.0 | wikitext | NULL |
| 1837.0 | Allegory | 0.0 | 0.0 | 28072.0 | wikitext | NULL |
| 1838.0 | Amazon_river | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 1839.0 | Allotropy | 0.0 | 0.0 | 23378.0 | wikitext | NULL |
| 1840.0 | Agathocles_of_Syracuse | 0.0 | 0.0 | 14651.0 | wikitext | NULL |
| 1841.0 | Economy_of_Alberta | 0.0 | 0.0 | 96497.0 | wikitext | NULL |
| 1842.0 | Augustin-Louis_Cauchy | 0.0 | 0.0 | 42923.0 | wikitext | NULL |
| 1844.0 | Archimedes | 0.0 | 0.0 | 99429.0 | wikitext | NULL |
| 1845.0 | Alternative_medicine | 0.0 | 0.0 | 202195.0 | wikitext | NULL |
| 1847.0 | Archimedean_solid | 0.0 | 0.0 | 26171.0 | wikitext | NULL |
| 1851.0 | Antiprism | 0.0 | 0.0 | 18676.0 | wikitext | NULL |
| 1852.0 | Ancient_Greeks | 1.0 | 0.0 | 91.0 | wikitext | NULL |
| 1853.0 | Natural_history_of_Africa | 0.0 | 0.0 | 7885.0 | wikitext | NULL |
| 1854.0 | Geography_of_Africa | 0.0 | 0.0 | 37335.0 | wikitext | NULL |
| 1855.0 | Africa/History | 1.0 | 0.0 | 31.0 | wikitext | NULL |
| 1857.0 | Approval_voting | 0.0 | 0.0 | 67712.0 | wikitext | NULL |
| 1858.0 | Aromatic_hydrocarbon | 1.0 | 0.0 | 183.0 | wikitext | NULL |
| 1859.0 | Arizona_State_University | 0.0 | 0.0 | 190519.0 | wikitext | NULL |
| 1862.0 | April_14 | 0.0 | 0.0 | 60593.0 | wikitext | NULL |
| 1864.0 | Astoria,_Oregon | 0.0 | 0.0 | 71881.0 | wikitext | NULL |
| 1866.0 | Alarums_and_Excursions | 0.0 | 0.0 | 8592.0 | wikitext | NULL |
| 1869.0 | Alfred_Jarry | 0.0 | 0.0 | 18108.0 | wikitext | NULL |
| 1870.0 | Amalric | 0.0 | 0.0 | 3036.0 | wikitext | NULL |
| 1871.0 | Amalric_of_Jerusalem | 0.0 | 0.0 | 18148.0 | wikitext | NULL |
| 1872.0 | Aimery_of_Cyprus | 0.0 | 0.0 | 30136.0 | wikitext | NULL |
| 1873.0 | Anthemius_of_Tralles | 0.0 | 0.0 | 5750.0 | wikitext | NULL |
| 1874.0 | Absalon | 0.0 | 0.0 | 16050.0 | wikitext | NULL |
| 1875.0 | Adhemar_of_Le_Puy | 0.0 | 0.0 | 10074.0 | wikitext | NULL |
| 1876.0 | Adhemar_de_Chabannes | 1.0 | 0.0 | 103.0 | wikitext | NULL |
| 1877.0 | Albigenses | 1.0 | 0.0 | 23.0 | wikitext | NULL |
| 1878.0 | Alphonse,_Count_of_Poitiers | 0.0 | 0.0 | 9075.0 | wikitext | NULL |
| 1879.0 | Alfonso_Jordan | 0.0 | 0.0 | 9688.0 | wikitext | NULL |
| 1880.0 | Ambroise | 0.0 | 0.0 | 3356.0 | wikitext | NULL |
| 1881.0 | Art_Deco | 0.0 | 0.0 | 148950.0 | wikitext | NULL |
| 1884.0 | ASCII_art | 0.0 | 0.0 | 53155.0 | wikitext | NULL |
| 1885.0 | Autoerotic_asphyxiation | 1.0 | 0.0 | 33.0 | wikitext | NULL |
| 1887.0 | Alexius | 0.0 | 0.0 | 2739.0 | wikitext | NULL |
| 1889.0 | Ban_on_assault_rifles | 1.0 | 0.0 | 74.0 | wikitext | NULL |
| 1890.0 | American_English | 0.0 | 0.0 | 78621.0 | wikitext | NULL |
| 1893.0 | Albert_Spalding | 0.0 | 0.0 | 22801.0 | wikitext | NULL |
| 1894.0 | Africa_Alphabet | 0.0 | 0.0 | 3512.0 | wikitext | NULL |
| 1896.0 | Acquire | 0.0 | 0.0 | 8701.0 | wikitext | NULL |
| 1897.0 | Australian_English | 0.0 | 0.0 | 70859.0 | wikitext | NULL |
| 1902.0 | American_Airlines_Flight_77 | 0.0 | 0.0 | 85249.0 | wikitext | NULL |
| 1903.0 | American_Airlines_flight_77 | 1.0 | 0.0 | 106.0 | wikitext | NULL |
| 1904.0 | American_Airlines_flight_11 | 1.0 | 0.0 | 106.0 | wikitext | NULL |
| 1905.0 | Ambush | 0.0 | 0.0 | 16289.0 | wikitext | NULL |
| 1906.0 | Astronomical_aberration | 1.0 | 0.0 | 36.0 | wikitext | NULL |
| 1908.0 | Abzyme | 0.0 | 0.0 | 6959.0 | wikitext | NULL |
| 1909.0 | Adaptive_radiation | 0.0 | 0.0 | 37579.0 | wikitext | NULL |
| 1910.0 | Agarose_gel_electrophoresis | 0.0 | 0.0 | 34925.0 | wikitext | NULL |
| 1911.0 | Allele | 0.0 | 0.0 | 16991.0 | wikitext | NULL |
| 1912.0 | Ampicillin | 0.0 | 0.0 | 35148.0 | wikitext | NULL |
| 1913.0 | Annealing | 0.0 | 0.0 | 460.0 | wikitext | NULL |
| 1914.0 | Antimicrobial_resistance | 0.0 | 0.0 | 150266.0 | wikitext | NULL |
| 1915.0 | Antigen | 0.0 | 0.0 | 19203.0 | wikitext | NULL |
| 1916.0 | Autosome | 0.0 | 0.0 | 11003.0 | wikitext | NULL |
| 1919.0 | Antwerp_(disambiguation) | 0.0 | 0.0 | 651.0 | wikitext | NULL |
| 1920.0 | Aquila | 0.0 | 0.0 | 3896.0 | wikitext | NULL |
| 1921.0 | Al-Qaeda | 0.0 | 0.0 | 284997.0 | wikitext | NULL |
| 1923.0 | Alessandro_Volta | 0.0 | 0.0 | 26430.0 | wikitext | NULL |
| 1924.0 | Argo_Navis | 0.0 | 0.0 | 13465.0 | wikitext | NULL |
| 1925.0 | Andromeda_(mythology) | 0.0 | 0.0 | 43392.0 | wikitext | NULL |
| 1926.0 | Antlia | 0.0 | 0.0 | 32732.0 | wikitext | NULL |
| 1927.0 | Ara_(constellation) | 0.0 | 0.0 | 29562.0 | wikitext | NULL |
| 1928.0 | Auriga | 0.0 | 0.0 | 754.0 | wikitext | NULL |
| 1930.0 | Arkansas | 0.0 | 0.0 | 153605.0 | wikitext | NULL |
| 1931.0 | Atmosphere_(disambiguation) | 0.0 | 0.0 | 2260.0 | wikitext | NULL |
| 1933.0 | Apus | 0.0 | 0.0 | 28135.0 | wikitext | NULL |
| 1934.0 | Abadan,_Iran | 0.0 | 0.0 | 36915.0 | wikitext | NULL |
| 1935.0 | Attorney | 0.0 | 0.0 | 508.0 | wikitext | NULL |
| 1936.0 | Astronomical_Unit | 1.0 | 0.0 | 96.0 | wikitext | NULL |
| 1937.0 | Alexander_Fleming | 0.0 | 0.0 | 69600.0 | wikitext | NULL |
| 1938.0 | Andrew_Carnegie | 0.0 | 0.0 | 113066.0 | wikitext | NULL |
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| 2.2158133e7 | !Kheis_Local_Municipality |
| 2.4178869e7 | !Kheis_Local_Municipality |
| 2.6513401e7 | !Kheis_Local_Municipality |
| 3.0209956e7 | !Kheis_Local_Municipality |
| 3.0210406e7 | !Kheis_Local_Municipality |
| 3.0241218e7 | !Kheis_Local_Municipality |
| 3.0254618e7 | !Kheis_Local_Municipality |
| 3.0321534e7 | !Kheis_Local_Municipality |
| 3.4773828e7 | !Kheis_Local_Municipality |
| 3.477392e7 | !Kheis_Local_Municipality |
| 3.477696e7 | !Kheis_Local_Municipality |
| 3.4785572e7 | !Kheis_Local_Municipality |
| 3.4785949e7 | !Kheis_Local_Municipality |
| 3.4785957e7 | !Kheis_Local_Municipality |
| 3.6081936e7 | !Kheis_Local_Municipality |
| 3.6841224e7 | !Kheis_Local_Municipality |
| 4.0001939e7 | !Kheis_Local_Municipality |
| 4.0091659e7 | !Kheis_Local_Municipality |
| 4.0352602e7 | !Kheis_Local_Municipality |
| 4.0440224e7 | !Kheis_Local_Municipality |
| 4.1295714e7 | !Kheis_Local_Municipality |
| 4.6292286e7 | !Kheis_Local_Municipality |
| 4.7080308e7 | !Kheis_Local_Municipality |
| 5.0781996e7 | !Kheis_Local_Municipality |
| 5.5137495e7 | !Kheis_Local_Municipality |
| 5.5143403e7 | !Kheis_Local_Municipality |
| 5.5144551e7 | !Kheis_Local_Municipality |
| 6.0925074e7 | !Kheis_Local_Municipality |
| 7.0333648e7 | !Kheis_Local_Municipality |
| 3.0254618e7 | !Kheis_Local_Municipality_elections |
| 6.9394817e7 | !Kheis_Local_Municipality_elections |
| 7.0292153e7 | !Kheis_Local_Municipality_elections |
| 7.0292295e7 | !Kheis_Local_Municipality_elections |
| 7.0333617e7 | !Kheis_Local_Municipality_elections |
| 7.0337955e7 | !Kheis_Local_Municipality_elections |
| 7.0337977e7 | !Kheis_Local_Municipality_elections |
| 7.0344395e7 | !Kheis_Local_Municipality_elections |
| 7.0344424e7 | !Kheis_Local_Municipality_elections |
| 7.0345364e7 | !Kheis_Local_Municipality_elections |
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| 7.0408226e7 | !Kheis_Local_Municipality_elections |
| 7.0408771e7 | !Kheis_Local_Municipality_elections |
| 7.0507872e7 | !Kheis_Local_Municipality_elections |
| 7.0507907e7 | !Kheis_Local_Municipality_elections |
| 7.0507968e7 | !Kheis_Local_Municipality_elections |
| 7.0544366e7 | !Kheis_Local_Municipality_elections |
| 7.0544416e7 | !Kheis_Local_Municipality_elections |
| 7.0544779e7 | !Kheis_Local_Municipality_elections |
| 7.0551674e7 | !Kheis_Local_Municipality_elections |
| 7.0551867e7 | !Kheis_Local_Municipality_elections |
| 7.0551907e7 | !Kheis_Local_Municipality_elections |
| 7.0565113e7 | !Kheis_Local_Municipality_elections |
| 7.0565152e7 | !Kheis_Local_Municipality_elections |
| 7.0565273e7 | !Kheis_Local_Municipality_elections |
| 7.056534e7 | !Kheis_Local_Municipality_elections |
| 4.3198829e7 | !Kheis_Municipality |
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| 2.2334799e7 | !Kora |
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| 4062928.0 | !Kora_Wars |
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| 2.2334799e7 | !Kora_language |
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| 3375586.0 | !Kung |
| 3508393.0 | !Kung |
| 4923690.0 | !Kung |
| 6687339.0 | !Kung |
| 1.0638871e7 | !Kung |
| 1.1548185e7 | !Kung |
| 2.3266996e7 | !Kung |
| 3.8830844e7 | !Kung |
| 4.3349537e7 | !Kung |
| 4.9050655e7 | !Kung |
| 5.5868925e7 | !Kung |
| 391097.0 | !Kung_San |
| 3656651.0 | !Kung_San |
| 2.7664456e7 | !Kung_San |
| 17333.0 | !Kung_language |
| 21292.0 | !Kung_language |
| 22980.0 | !Kung_language |
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| 1.1403668e7 | !Kung_language |
| 1.1871524e7 | !Kung_language |
| 2.0173128e7 | !Kung_language |
| 2.5537629e7 | !Kung_language |
| 2.7164415e7 | !Kung_language |
| 3.4338728e7 | !Kung_language |
| 9992.0 | !Kung_languages |
| 151336.0 | !Kung_languages |
| 364060.0 | !Kung_languages |
| 453953.0 | !Kung_languages |
| 453979.0 | !Kung_languages |
| 453991.0 | !Kung_languages |
| 1210028.0 | !Kung_languages |
| 2303025.0 | !Kung_languages |
| 2319716.0 | !Kung_languages |
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| 2609977.0 | !Kung_languages |
| 4697698.0 | !Kung_languages |
| 6145891.0 | !Kung_languages |
| 9959546.0 | !Kung_languages |
| 1.0455944e7 | !Kung_languages |
| 1.2883888e7 | !Kung_languages |
| 1.387966e7 | !Kung_languages |
| 1.5214566e7 | !Kung_languages |
| 1.7764754e7 | !Kung_languages |
| 3.1485302e7 | !Kung_languages |
| 3.4173762e7 | !Kung_languages |
| 3.9214513e7 | !Kung_languages |
| 3.9214938e7 | !Kung_languages |
| 3.9215665e7 | !Kung_languages |
| 3.9249762e7 | !Kung_languages |
| 3.9673059e7 | !Kung_languages |
| 4.0515986e7 | !Kung_languages |
| 6.0292402e7 | !Kung_languages |
| 6.8165414e7 | !Kung_languages |
| 6.8167777e7 | !Kung_languages |
| 561726.0 | !Kung_mythology |
| 5388.0 | !Kung_people |
| 9992.0 | !Kung_people |
| 15474.0 | !Kung_people |
| 22860.0 | !Kung_people |
| 102262.0 | !Kung_people |
| 215509.0 | !Kung_people |
| 552168.0 | !Kung_people |
| 641840.0 | !Kung_people |
| 816362.0 | !Kung_people |
| 1227748.0 | !Kung_people |
| 3121665.0 | !Kung_people |
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| 5227591.0 | !Kung_people |
| 6299108.0 | !Kung_people |
| 6618605.0 | !Kung_people |
| 7264431.0 | !Kung_people |
| 1.3280233e7 | !Kung_people |
| 1.3285831e7 | !Kung_people |
| 1.7200325e7 | !Kung_people |
| 2.0290838e7 | !Kung_people |
| 2.3221293e7 | !Kung_people |
| 2.6451793e7 | !Kung_people |
| 2.6854262e7 | !Kung_people |
| 3.8905381e7 | !Kung_people |
| 4.9182508e7 | !Kung_people |
| 87801.0 | !Kweiten-ta-Ken |
| 87801.0 | !Kweiten-ta-ǀǀKen |
| 317886.0 | !Kweiten-ta-ǀǀKen |
| 712892.0 | !Kweiten-ta-ǀǀKen |
| 3288888.0 | !Kweiten-ta-ǀǀKen |
| 9895473.0 | !Kweiten-ta-ǀǀKen |
| 3.7361147e7 | !Kweiten_ta_//ken |
| 17333.0 | !Kwi_language |
| 1.3219614e7 | !Les |
| 2.2918304e7 | !Mayday¡ |
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| 2222598.0 | !Oka_Tokat |
| 3720524.0 | !Oka_Tokat |
| 3784152.0 | !Oka_Tokat |
| 5526638.0 | !Oka_Tokat |
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| 7360687.0 | !Oka_Tokat |
| 8127304.0 | !Oka_Tokat |
| 8500474.0 | !Oka_Tokat |
| 8917884.0 | !Oka_Tokat |
| 9497214.0 | !Oka_Tokat |
| 1.2453883e7 | !Oka_Tokat |
| 1.2474872e7 | !Oka_Tokat |
| 1.3712471e7 | !Oka_Tokat |
| 1.6059647e7 | !Oka_Tokat |
| 1.6083903e7 | !Oka_Tokat |
| 1.6498971e7 | !Oka_Tokat |
| 2.1551816e7 | !Oka_Tokat |
| 2.2976571e7 | !Oka_Tokat |
| 2.3353652e7 | !Oka_Tokat |
| 2.3568338e7 | !Oka_Tokat |
| 2.3711745e7 | !Oka_Tokat |
| 2.576427e7 | !Oka_Tokat |
| 3.1928345e7 | !Oka_Tokat |
| 3.437659e7 | !Oka_Tokat |
| 3.5978874e7 | !Oka_Tokat |
| 3.8307891e7 | !Oka_Tokat |
| 3.8519079e7 | !Oka_Tokat |
| 3.8721976e7 | !Oka_Tokat |
| 3.8740597e7 | !Oka_Tokat |
| 3.8925613e7 | !Oka_Tokat |
| 3.9070026e7 | !Oka_Tokat |
| 3.9799916e7 | !Oka_Tokat |
| 4.1067896e7 | !Oka_Tokat |
| 4.1344723e7 | !Oka_Tokat |
| 4.1572046e7 | !Oka_Tokat |
| 4.258094e7 | !Oka_Tokat |
| 4.3271848e7 | !Oka_Tokat |
| 4.4210193e7 | !Oka_Tokat |
| 4.4354474e7 | !Oka_Tokat |
| 4.7091754e7 | !Oka_Tokat |
| 4.7754955e7 | !Oka_Tokat |
| 5.064316e7 | !Oka_Tokat |
| 5.4502138e7 | !Oka_Tokat |
| 6.3943912e7 | !Oka_Tokat |
| 6.5715741e7 | !Oka_Tokat |
| 6.8457421e7 | !Oka_Tokat |
| 7.1389693e7 | !Oka_Tokat |
| 1210028.0 | !Ora_language |
| 5.3108404e7 | !Ora_people |
| 8199349.0 | !Oye_Esteban! |
| 2322480.0 | !PAUS3 |
| 7847796.0 | !PAUS3 |
| 4.7876909e7 | !PAUS3 |
| 901742.0 | !Que_viva_la_musica! |
| 3.9115606e7 | !Que_viva_la_musica! |
| 1248325.0 | !Shin_Chan:_Flipa_en_colores! |
| 6653.0 | !Uriǁ’aekua |
| 373641.0 | !WOWOW! |
| 1759656.0 | !WOWOW! |
| 1.0570502e7 | !WOWOW! |
| 1.4408717e7 | !WOWOW! |
| 2.0140534e7 | !WOWOW! |
| 2.9055465e7 | !WOWOW! |
| 3.1869291e7 | !WOWOW! |
| 4.963566e7 | !WOWOW! |
| 34350.0 | !Women_Art_Revolution |
| 98957.0 | !Women_Art_Revolution |
| 153926.0 | !Women_Art_Revolution |
| 238326.0 | !Women_Art_Revolution |
| 309846.0 | !Women_Art_Revolution |
| 311615.0 | !Women_Art_Revolution |
| 420777.0 | !Women_Art_Revolution |
| 476217.0 | !Women_Art_Revolution |
| 512045.0 | !Women_Art_Revolution |
| 570145.0 | !Women_Art_Revolution |
| 584664.0 | !Women_Art_Revolution |
| 604117.0 | !Women_Art_Revolution |
| 621018.0 | !Women_Art_Revolution |
| 703371.0 | !Women_Art_Revolution |
| 764030.0 | !Women_Art_Revolution |
| 783731.0 | !Women_Art_Revolution |
| 904930.0 | !Women_Art_Revolution |
| 933512.0 | !Women_Art_Revolution |
| 1090517.0 | !Women_Art_Revolution |
| 1104576.0 | !Women_Art_Revolution |
| 1110133.0 | !Women_Art_Revolution |
| 1246709.0 | !Women_Art_Revolution |
| 1423146.0 | !Women_Art_Revolution |
| 1544909.0 | !Women_Art_Revolution |
| 1548731.0 | !Women_Art_Revolution |
| 1577589.0 | !Women_Art_Revolution |
| 1605832.0 | !Women_Art_Revolution |
| 1673775.0 | !Women_Art_Revolution |
| 1686995.0 | !Women_Art_Revolution |
| 1788279.0 | !Women_Art_Revolution |
| 1870977.0 | !Women_Art_Revolution |
| 2171817.0 | !Women_Art_Revolution |
| 2225246.0 | !Women_Art_Revolution |
| 2332995.0 | !Women_Art_Revolution |
| 2468856.0 | !Women_Art_Revolution |
| 2508428.0 | !Women_Art_Revolution |
| 2600313.0 | !Women_Art_Revolution |
| 2626651.0 | !Women_Art_Revolution |
| 2870025.0 | !Women_Art_Revolution |
| 3075695.0 | !Women_Art_Revolution |
| 3083317.0 | !Women_Art_Revolution |
| 3083872.0 | !Women_Art_Revolution |
| 3139082.0 | !Women_Art_Revolution |
| 3207413.0 | !Women_Art_Revolution |
| 3207655.0 | !Women_Art_Revolution |
| 3251080.0 | !Women_Art_Revolution |
| 3257715.0 | !Women_Art_Revolution |
| 3324292.0 | !Women_Art_Revolution |
| 3367162.0 | !Women_Art_Revolution |
| 3487779.0 | !Women_Art_Revolution |
| 3510891.0 | !Women_Art_Revolution |
| 3639548.0 | !Women_Art_Revolution |
| 4029639.0 | !Women_Art_Revolution |
| 4843444.0 | !Women_Art_Revolution |
| 4943835.0 | !Women_Art_Revolution |
| 5237837.0 | !Women_Art_Revolution |
| 5296735.0 | !Women_Art_Revolution |
| 5341253.0 | !Women_Art_Revolution |
| 5441158.0 | !Women_Art_Revolution |
| 5929235.0 | !Women_Art_Revolution |
| 5958428.0 | !Women_Art_Revolution |
| 5958918.0 | !Women_Art_Revolution |
| 5964167.0 | !Women_Art_Revolution |
| 5967032.0 | !Women_Art_Revolution |
| 6025441.0 | !Women_Art_Revolution |
| 6062357.0 | !Women_Art_Revolution |
| 6062759.0 | !Women_Art_Revolution |
| 6320392.0 | !Women_Art_Revolution |
| 6727570.0 | !Women_Art_Revolution |
| 6814223.0 | !Women_Art_Revolution |
| 7365243.0 | !Women_Art_Revolution |
| 7606126.0 | !Women_Art_Revolution |
| 7624796.0 | !Women_Art_Revolution |
| 7634036.0 | !Women_Art_Revolution |
| 7779578.0 | !Women_Art_Revolution |
| 7780000.0 | !Women_Art_Revolution |
| 8010624.0 | !Women_Art_Revolution |
| 8320171.0 | !Women_Art_Revolution |
| 8723702.0 | !Women_Art_Revolution |
| 8938738.0 | !Women_Art_Revolution |
| 9994165.0 | !Women_Art_Revolution |
| 1.0033919e7 | !Women_Art_Revolution |
| 1.0467222e7 | !Women_Art_Revolution |
| 1.0997115e7 | !Women_Art_Revolution |
| 1.1044549e7 | !Women_Art_Revolution |
| 1.1571844e7 | !Women_Art_Revolution |
| 1.1922944e7 | !Women_Art_Revolution |
| 1.2590877e7 | !Women_Art_Revolution |
| 1.4263353e7 | !Women_Art_Revolution |
| 1.499971e7 | !Women_Art_Revolution |
| 1.6262412e7 | !Women_Art_Revolution |
| 1.635418e7 | !Women_Art_Revolution |
| 1.6864608e7 | !Women_Art_Revolution |
| 1.7140025e7 | !Women_Art_Revolution |
| 1.7144223e7 | !Women_Art_Revolution |
| 1.8041205e7 | !Women_Art_Revolution |
| 1.8190768e7 | !Women_Art_Revolution |
| 1.8584072e7 | !Women_Art_Revolution |
| 1.8895392e7 | !Women_Art_Revolution |
| 2.0382258e7 | !Women_Art_Revolution |
| 2.2053497e7 | !Women_Art_Revolution |
| 2.4633688e7 | !Women_Art_Revolution |
| 2.4966052e7 | !Women_Art_Revolution |
| 2.6746723e7 | !Women_Art_Revolution |
| 2.6775679e7 | !Women_Art_Revolution |
| 2.7569626e7 | !Women_Art_Revolution |
| 2.8145279e7 | !Women_Art_Revolution |
| 2.8336165e7 | !Women_Art_Revolution |
| 2.865032e7 | !Women_Art_Revolution |
| 3.1000159e7 | !Women_Art_Revolution |
| 3.1025072e7 | !Women_Art_Revolution |
| 3.1217457e7 | !Women_Art_Revolution |
| 3.2192717e7 | !Women_Art_Revolution |
| 3.2224512e7 | !Women_Art_Revolution |
| 3.222481e7 | !Women_Art_Revolution |
| 3.2381705e7 | !Women_Art_Revolution |
| 3.2726935e7 | !Women_Art_Revolution |
| 3.2983293e7 | !Women_Art_Revolution |
| 3.3096698e7 | !Women_Art_Revolution |
| 3.3244298e7 | !Women_Art_Revolution |
| 3.4313454e7 | !Women_Art_Revolution |
| 3.4329777e7 | !Women_Art_Revolution |
| 3.489663e7 | !Women_Art_Revolution |
| 3.5522968e7 | !Women_Art_Revolution |
| 3.5798892e7 | !Women_Art_Revolution |
| 3.6609348e7 | !Women_Art_Revolution |
| 3.6620963e7 | !Women_Art_Revolution |
| 3.6710267e7 | !Women_Art_Revolution |
| 3.6836715e7 | !Women_Art_Revolution |
| 3.7880276e7 | !Women_Art_Revolution |
| 3.7892711e7 | !Women_Art_Revolution |
| 3.8737833e7 | !Women_Art_Revolution |
| 3.8769205e7 | !Women_Art_Revolution |
| 3.92278e7 | !Women_Art_Revolution |
| 3.9307755e7 | !Women_Art_Revolution |
| 3.9623516e7 | !Women_Art_Revolution |
| 3.9637647e7 | !Women_Art_Revolution |
| 4.0384325e7 | !Women_Art_Revolution |
| 4.0454162e7 | !Women_Art_Revolution |
| 4.0851576e7 | !Women_Art_Revolution |
| 4.0968093e7 | !Women_Art_Revolution |
| 4.1189906e7 | !Women_Art_Revolution |
| 4.160635e7 | !Women_Art_Revolution |
| 4.1654366e7 | !Women_Art_Revolution |
| 4.181221e7 | !Women_Art_Revolution |
| 4.1812283e7 | !Women_Art_Revolution |
| 4.1813613e7 | !Women_Art_Revolution |
| 4.1955805e7 | !Women_Art_Revolution |
| 4.2261534e7 | !Women_Art_Revolution |
| 4.2351781e7 | !Women_Art_Revolution |
| 4.2534246e7 | !Women_Art_Revolution |
| 4.2552874e7 | !Women_Art_Revolution |
| 4.2622068e7 | !Women_Art_Revolution |
| 4.2796539e7 | !Women_Art_Revolution |
| 4.2879461e7 | !Women_Art_Revolution |
| 4.2886934e7 | !Women_Art_Revolution |
| 4.3036442e7 | !Women_Art_Revolution |
| 4.371949e7 | !Women_Art_Revolution |
| 4.56049e7 | !Women_Art_Revolution |
| 4.5635805e7 | !Women_Art_Revolution |
| 4.6185928e7 | !Women_Art_Revolution |
| 4.6903191e7 | !Women_Art_Revolution |
| 4.7303084e7 | !Women_Art_Revolution |
| 4.7705423e7 | !Women_Art_Revolution |
| 4.8736855e7 | !Women_Art_Revolution |
| 4.9595632e7 | !Women_Art_Revolution |
| 4.9651273e7 | !Women_Art_Revolution |
| 4.9651914e7 | !Women_Art_Revolution |
| 4.9653051e7 | !Women_Art_Revolution |
| 5.2704891e7 | !Women_Art_Revolution |
| 5.3454432e7 | !Women_Art_Revolution |
| 5.3631933e7 | !Women_Art_Revolution |
| 5.3933938e7 | !Women_Art_Revolution |
| 5.6752589e7 | !Women_Art_Revolution |
| 5.9974067e7 | !Women_Art_Revolution |
| 6.4577622e7 | !Women_Art_Revolution |
| 6.4907515e7 | !Women_Art_Revolution |
| 6.8072601e7 | !Women_Art_Revolution |
| 6.8228244e7 | !Women_Art_Revolution |
| 6.8229346e7 | !Women_Art_Revolution |
| 6.8340058e7 | !Women_Art_Revolution |
| 6.9627747e7 | !Women_Art_Revolution |
| 6.9840155e7 | !Women_Art_Revolution |
| 6.9966187e7 | !Women_Art_Revolution |
| 6.9971066e7 | !Women_Art_Revolution |
| 43492.0 | !Wowow! |
| 143570.0 | !Wowow! |
| 145606.0 | !Wowow! |
| 277812.0 | !Wowow! |
| 308392.0 | !Wowow! |
| 584739.0 | !Wowow! |
| 597785.0 | !Wowow! |
| 643525.0 | !Wowow! |
| 1451373.0 | !Wowow! |
| 1586277.0 | !Wowow! |
| 1613892.0 | !Wowow! |
| 1778942.0 | !Wowow! |
| 2217738.0 | !Wowow! |
| 2930511.0 | !Wowow! |
| 3076713.0 | !Wowow! |
| 3180565.0 | !Wowow! |
| 4034653.0 | !Wowow! |
| 6171459.0 | !Wowow! |
| 6852388.0 | !Wowow! |
| 7135479.0 | !Wowow! |
| 7325676.0 | !Wowow! |
| 9062463.0 | !Wowow! |
| 9268023.0 | !Wowow! |
| 9278679.0 | !Wowow! |
| 1.0043098e7 | !Wowow! |
| 1.1089239e7 | !Wowow! |
| 1.3442201e7 | !Wowow! |
| 1.5008004e7 | !Wowow! |
| 1.5065487e7 | !Wowow! |
| 1.796197e7 | !Wowow! |
| 1.8743234e7 | !Wowow! |
| 1.9415992e7 | !Wowow! |
| 2.0140534e7 | !Wowow! |
| 2.2388425e7 | !Wowow! |
| 2.4481226e7 | !Wowow! |
| 2.5878053e7 | !Wowow! |
| 2.7499303e7 | !Wowow! |
| 2.8637104e7 | !Wowow! |
| 2.8668067e7 | !Wowow! |
| 2.9055465e7 | !Wowow! |
| 3.1869291e7 | !Wowow! |
| 3.3418768e7 | !Wowow! |
| 3.4547132e7 | !Wowow! |
| 3.8781885e7 | !Wowow! |
| 3.8968881e7 | !Wowow! |
| 3.8975533e7 | !Wowow! |
| 4.0855562e7 | !Wowow! |
| 4.8426345e7 | !Wowow! |
| 5.1707855e7 | !Wowow! |
| 5.3476625e7 | !Wowow! |
| 5.8755622e7 | !Wowow! |
val edgesNoSrcTitle = spark.sql("""SELECT enwiki_pagelinks.pl_from AS src,
enwiki_page.page_id AS dst,
enwiki_pagelinks.pl_title AS dst_title
FROM enwiki_page INNER JOIN enwiki_pagelinks
ON enwiki_pagelinks.pl_title = enwiki_page.page_title""")
edgesNoSrcTitle.createOrReplaceTempView("edges_no_src_title")
val edges = spark.sql("""SELECT edges_no_src_title.src,
edges_no_src_title.dst,
enwiki_page.page_title AS src_title,
edges_no_src_title.dst_title
FROM edges_no_src_title INNER JOIN enwiki_page
ON enwiki_page.page_id = edges_no_src_title.src""")
display(edges)
| src | dst | src_title | dst_title |
|---|---|---|---|
| 1088.0 | 3.1030978e7 | Azerbaijani_Armed_Forces | Azerbaijani_mythology |
| 1088.0 | 3.0322787e7 | Azerbaijani_Armed_Forces | Chief_of_General_Staff_of_Azerbaijani_Armed_Forces |
| 1088.0 | 46530.0 | Azerbaijani_Armed_Forces | Human_Rights_Watch |
| 1088.0 | 2.1447694e7 | Azerbaijani_Armed_Forces | List_of_companies_of_Azerbaijan |
| 1088.0 | 3.7897147e7 | Azerbaijani_Armed_Forces | National_symbols_of_Azerbaijan |
| 1088.0 | 4.5061575e7 | Azerbaijani_Armed_Forces | Qajar_Iran |
| 1088.0 | 873945.0 | Azerbaijani_Armed_Forces | Soviet_Air_Defence_Forces |
| 1088.0 | 774820.0 | Azerbaijani_Armed_Forces | List_of_Azerbaijanis |
| 1088.0 | 6.5939927e7 | Azerbaijani_Armed_Forces | Nakhchivan_Separate_Combined_Arms_Army |
| 1088.0 | 6.8702564e7 | Azerbaijani_Armed_Forces | Non-Aligned_Movement |
| 1088.0 | 2867590.0 | Azerbaijani_Armed_Forces | Royal_Cambodian_Armed_Forces |
| 1088.0 | 4.144269e7 | Azerbaijani_Armed_Forces | Corps_of_Drums |
| 1088.0 | 5.8693917e7 | Azerbaijani_Armed_Forces | Foreign_Intelligence_Service_(Azerbaijan) |
| 1088.0 | 2648922.0 | Azerbaijani_Armed_Forces | Hydroelectric_power_station |
| 1088.0 | 34252.0 | Azerbaijani_Armed_Forces | Republic_of_Yemen_Armed_Forces |
| 1088.0 | 1036235.0 | Azerbaijani_Armed_Forces | Zand_dynasty |
| 1088.0 | 1.3427826e7 | Azerbaijani_Armed_Forces | Cabinet_of_Azerbaijan |
| 1088.0 | 1.0927665e7 | Azerbaijani_Armed_Forces | Internal_Troops_of_Azerbaijan |
| 1088.0 | 39237.0 | Azerbaijani_Armed_Forces | Israel_Defense_Forces |
| 1088.0 | 5.7994574e7 | Azerbaijani_Armed_Forces | Military_Band_Service_of_the_Armed_Forces_of_Azerbaijan |
| 1088.0 | 2.6157272e7 | Azerbaijani_Armed_Forces | Azerbaijani_art |
| 1088.0 | 2.7172367e7 | Azerbaijani_Armed_Forces | Azerbaijani_folklore |
| 1088.0 | 385358.0 | Azerbaijani_Armed_Forces | Nakhchivan_Autonomous_Republic |
| 1088.0 | 7.0680595e7 | Azerbaijani_Armed_Forces | 227th_Rifle_Division |
| 1088.0 | 2192452.0 | Azerbaijani_Armed_Forces | 4th_Army_(Soviet_Union) |
| 1088.0 | 2.3411067e7 | Azerbaijani_Armed_Forces | Ganja_Air_Base |
| 1088.0 | 6.6149221e7 | Azerbaijani_Armed_Forces | 1st_Army_Corps_(Azerbaijan) |
| 1088.0 | 7150649.0 | Azerbaijani_Armed_Forces | Environmental_issues_in_Azerbaijan |
| 1088.0 | 6.5910861e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Lachin_Medal |
| 1088.0 | 1.0287296e7 | Azerbaijani_Armed_Forces | Otokar_Cobra |
| 1088.0 | 3.84555e7 | Azerbaijani_Armed_Forces | Safavid_Iran |
| 1088.0 | 6.6150419e7 | Azerbaijani_Armed_Forces | 3rd_Army_Corps_(Azerbaijan) |
| 1088.0 | 3457.0 | Azerbaijani_Armed_Forces | Belarus |
| 1088.0 | 1.1505052e7 | Azerbaijani_Armed_Forces | National_Hero_of_Azerbaijan |
| 1088.0 | 897352.0 | Azerbaijani_Armed_Forces | Singapore_Armed_Forces |
| 1088.0 | 3.3872653e7 | Azerbaijani_Armed_Forces | Jar-Burial_Culture |
| 1088.0 | 7174933.0 | Azerbaijani_Armed_Forces | List_of_countries_with_nuclear_weapons |
| 1088.0 | 3.0323393e7 | Azerbaijani_Armed_Forces | Minister_of_Defense_(Azerbaijan) |
| 1088.0 | 1492790.0 | Azerbaijani_Armed_Forces | Shusha |
| 1088.0 | 31975.0 | Azerbaijani_Armed_Forces | United_States_Department_of_State |
| 1088.0 | 6.6016006e7 | Azerbaijani_Armed_Forces | Victory_Day_(Azerbaijan) |
| 1088.0 | 16650.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Republic_of_Kazakhstan |
| 1088.0 | 2409969.0 | Azerbaijani_Armed_Forces | Azerbaijan_Democratic_Republic |
| 1088.0 | 1.1886584e7 | Azerbaijani_Armed_Forces | Baku_Air_Defence_Army |
| 1088.0 | 3.5527299e7 | Azerbaijani_Armed_Forces | For_Heroism_Medal |
| 1088.0 | 6.5910757e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Aghdam_Medal |
| 1088.0 | 6.5910908e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Zangilan_Medal |
| 1088.0 | 7171338.0 | Azerbaijani_Armed_Forces | Indian_Armed_Forces |
| 1088.0 | 5.1886693e7 | Azerbaijani_Armed_Forces | S-300_(missile) |
| 1088.0 | 877164.0 | Azerbaijani_Armed_Forces | Arran_(Caucasus) |
| 1088.0 | 67538.0 | Azerbaijani_Armed_Forces | Australian_Defence_Force |
| 1088.0 | 8371628.0 | Azerbaijani_Armed_Forces | Battle_of_Baku |
| 1088.0 | 7427466.0 | Azerbaijani_Armed_Forces | Petya-class_frigate |
| 1088.0 | 25709.0 | Azerbaijani_Armed_Forces | Russian_Armed_Forces |
| 1088.0 | 7105996.0 | Azerbaijani_Armed_Forces | State_reserves_of_Azerbaijan |
| 1088.0 | 2.1376046e7 | Azerbaijani_Armed_Forces | Wehrmacht |
| 1088.0 | 6.1912686e7 | Azerbaijani_Armed_Forces | \"95th_Anniversary_of_the_Armed_Forces_of_Azerbaijan_(1918–2013)\"_Medal |
| 1088.0 | 6.4718117e7 | Azerbaijani_Armed_Forces | List_of_modern_equipment_of_the_Azerbaijani_Air_Force |
| 1088.0 | 5.0021902e7 | Azerbaijani_Armed_Forces | 2016_Nagorno-Karabakh_conflict |
| 1088.0 | 1.1288692e7 | Azerbaijani_Armed_Forces | 7th_Guards_Army |
| 1088.0 | 6.5911067e7 | Azerbaijani_Armed_Forces | Brave_Warrior_Medal |
| 1088.0 | 6922486.0 | Azerbaijani_Armed_Forces | Extreme_points_of_Azerbaijan |
| 1088.0 | 19115.0 | Azerbaijani_Armed_Forces | Malaysian_Armed_Forces |
| 1088.0 | 6.8977021e7 | Azerbaijani_Armed_Forces | Wedding_tradition_in_Azerbaijan |
| 1088.0 | 3.8429228e7 | Azerbaijani_Armed_Forces | Yevgenya_class_minesweeper |
| 1088.0 | 6.6176862e7 | Azerbaijani_Armed_Forces | 2nd_Army_Corps_(Azerbaijan) |
| 1088.0 | 6.5787844e7 | Azerbaijani_Armed_Forces | Battle_of_Shusha_(2020) |
| 1088.0 | 16692.0 | Azerbaijani_Armed_Forces | Kuwait_Military_Forces |
| 1088.0 | 5.7836785e7 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Islamic_Emirate_of_Afghanistan |
| 1088.0 | 30116.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Republic_of_Tajikistan |
| 1088.0 | 612372.0 | Azerbaijani_Armed_Forces | Midget_submarine |
| 1088.0 | 1322733.0 | Azerbaijani_Armed_Forces | Black_January |
| 1088.0 | 3.5450533e7 | Azerbaijani_Armed_Forces | Day_of_the_Armed_Forces_of_Azerbaijan |
| 1088.0 | 309778.0 | Azerbaijani_Armed_Forces | Music_of_Azerbaijan |
| 1088.0 | 30136.0 | Azerbaijani_Armed_Forces | Royal_Thai_Armed_Forces |
| 1088.0 | 26748.0 | Azerbaijani_Armed_Forces | Switzerland |
| 1088.0 | 7469136.0 | Azerbaijani_Armed_Forces | Vietnam_People's_Armed_Forces |
| 1088.0 | 2.8096514e7 | Azerbaijani_Armed_Forces | Jamshid_Nakhchivanski_Military_Lyceum |
| 1088.0 | 3.2850702e7 | Azerbaijani_Armed_Forces | List_of_World_Heritage_Sites_in_Azerbaijan |
| 1088.0 | 6.3975362e7 | Azerbaijani_Armed_Forces | OC_Media |
| 1088.0 | 2.023768e7 | Azerbaijani_Armed_Forces | Russian_Ministry_of_Defence |
| 1088.0 | 6672192.0 | Azerbaijani_Armed_Forces | Sajid_dynasty |
| 1088.0 | 1908551.0 | Azerbaijani_Armed_Forces | Aid |
| 1088.0 | 5122310.0 | Azerbaijani_Armed_Forces | March_Days |
| 1088.0 | 2.3538754e7 | Azerbaijani_Armed_Forces | Wayback_Machine |
| 1088.0 | 4941803.0 | Azerbaijani_Armed_Forces | Azerbaijani_Navy |
| 1088.0 | 5876413.0 | Azerbaijani_Armed_Forces | Sasanian_Empire |
| 1088.0 | 2.3575502e7 | Azerbaijani_Armed_Forces | Tourism_in_Azerbaijan |
| 1088.0 | 1.0934404e7 | Azerbaijani_Armed_Forces | Wildlife_of_Azerbaijan |
| 1088.0 | 6.72382e7 | Azerbaijani_Armed_Forces | Chief_of_the_General_Staff_(Azerbaijan) |
| 1088.0 | 339643.0 | Azerbaijani_Armed_Forces | Flag_of_Azerbaijan |
| 1088.0 | 6.6096407e7 | Azerbaijani_Armed_Forces | Heydar_Aliyev_Military_Lyceum |
| 1088.0 | 3.5252903e7 | Azerbaijani_Armed_Forces | Nuclear_Non-Proliferation_Treaty |
| 1088.0 | 5844475.0 | Azerbaijani_Armed_Forces | Palestinian_National_Security_Forces |
| 1088.0 | 1.1125639e7 | Azerbaijani_Armed_Forces | Turkey |
| 1088.0 | 187660.0 | Azerbaijani_Armed_Forces | Yakovlev |
| 1088.0 | 6.6828259e7 | Azerbaijani_Armed_Forces | Afsharid_Iran |
| 1088.0 | 6.5910891e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Gubadly_Medal |
| 1088.0 | 3249318.0 | Azerbaijani_Armed_Forces | Shaddadids |
| 1088.0 | 6.7101223e7 | Azerbaijani_Armed_Forces | Training_and_Education_Center_of_the_Armed_Forces |
| 1088.0 | 6.5804585e7 | Azerbaijani_Armed_Forces | 2020_Nagorno-Karabakh_ceasefire_agreement |
| 1088.0 | 7.03133e7 | Azerbaijani_Armed_Forces | 223rd_Rifle_Division |
| 1088.0 | 6.6185091e7 | Azerbaijani_Armed_Forces | 4th_Army_Corps_(Azerbaijan) |
| 1088.0 | 2.5137672e7 | Azerbaijani_Armed_Forces | Energy_in_Azerbaijan |
| 1088.0 | 4.1349212e7 | Azerbaijani_Armed_Forces | Minesweeper_(ship) |
| 1088.0 | 67639.0 | Azerbaijani_Armed_Forces | Politics_of_Azerbaijan |
| 1088.0 | 1.9376957e7 | Azerbaijani_Armed_Forces | Sanqacal |
| 1088.0 | 27318.0 | Azerbaijani_Armed_Forces | Singapore |
| 1088.0 | 1.7416221e7 | Azerbaijani_Armed_Forces | South_Africa |
| 1088.0 | 32927.0 | Azerbaijani_Armed_Forces | World_War_II |
| 1088.0 | 7095335.0 | Azerbaijani_Armed_Forces | Climate_of_Azerbaijan |
| 1088.0 | 6.5910824e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Shusha_Medal |
| 1088.0 | 1887429.0 | Azerbaijani_Armed_Forces | IISS |
| 1088.0 | 2.5278391e7 | Azerbaijani_Armed_Forces | List_of_protected_areas_of_Azerbaijan |
| 1088.0 | 5215751.0 | Azerbaijani_Armed_Forces | Multi-National_Force_–_Iraq |
| 1088.0 | 5.6441648e7 | Azerbaijani_Armed_Forces | Mughan_culture |
| 1088.0 | 368530.0 | Azerbaijani_Armed_Forces | Partnership_for_Peace |
| 1088.0 | 2.0823682e7 | Azerbaijani_Armed_Forces | Rovnag_Abdullayev |
| 1088.0 | 2.3916399e7 | Azerbaijani_Armed_Forces | Sport_in_Azerbaijan |
| 1088.0 | 3.6945373e7 | Azerbaijani_Armed_Forces | Theatre_in_Azerbaijan |
| 1088.0 | 7.0393652e7 | Azerbaijani_Armed_Forces | 641st_Special_Warfare_Naval_Unit |
| 1088.0 | 2152685.0 | Azerbaijani_Armed_Forces | Cypriot_National_Guard |
| 1088.0 | 3.6926008e7 | Azerbaijani_Armed_Forces | For_military_services_medal |
| 1088.0 | 182664.0 | Azerbaijani_Armed_Forces | Surface-to-air_missile |
| 1088.0 | 6.4343188e7 | Azerbaijani_Armed_Forces | Azerbaijan_High_Military_Aviation_School |
| 1088.0 | 69007.0 | Azerbaijani_Armed_Forces | Military_of_Bhutan |
| 1088.0 | 1.9859966e7 | Azerbaijani_Armed_Forces | Nasosnaya_Air_Base |
| 1088.0 | 1022955.0 | Azerbaijani_Armed_Forces | Supreme_Soviet_of_the_USSR |
| 1088.0 | 6.5451828e7 | Azerbaijani_Armed_Forces | 2020_Nagorno-Karabakh_War |
| 1088.0 | 6.9447715e7 | Azerbaijani_Armed_Forces | 402nd_Rifle_Division |
| 1088.0 | 5.224123e7 | Azerbaijani_Armed_Forces | Borders_of_Azerbaijan |
| 1088.0 | 6.5910916e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Fuzuli_Medal |
| 1088.0 | 27027.0 | Azerbaijani_Armed_Forces | Republic_of_Korea_Armed_Forces |
| 1088.0 | 1.0942678e7 | Azerbaijani_Armed_Forces | Samedbey_Mehmandarov |
| 1088.0 | 30205.0 | Azerbaijani_Armed_Forces | Turkish_Armed_Forces |
| 1088.0 | 6.1609086e7 | Azerbaijani_Armed_Forces | Bronze_and_Iron_Age_in_Azerbaijan |
| 1088.0 | 4.7845161e7 | Azerbaijani_Armed_Forces | Nakhchivan_Airport |
| 1088.0 | 9874605.0 | Azerbaijani_Armed_Forces | Turkish_Air_Force_Academy |
| 1088.0 | 5639884.0 | Azerbaijani_Armed_Forces | Armenian–Azerbaijani_war_(1918–1920) |
| 1088.0 | 7107998.0 | Azerbaijani_Armed_Forces | Bodies_of_water_of_Azerbaijan |
| 1088.0 | 5843419.0 | Azerbaijani_Armed_Forces | France |
| 1088.0 | 877182.0 | Azerbaijani_Armed_Forces | Shirvan |
| 1088.0 | 6.0555433e7 | Azerbaijani_Armed_Forces | War_College_of_the_Azerbaijani_Armed_Forces |
| 1088.0 | 1.9079143e7 | Azerbaijani_Armed_Forces | Armed_Forces_of_South_Ossetia |
| 1088.0 | 2.3207406e7 | Azerbaijani_Armed_Forces | Azerbaijan_Border_Guard |
| 1088.0 | 2.1189576e7 | Azerbaijani_Armed_Forces | Azerbaijani_rug |
| 1088.0 | 5.5636355e7 | Azerbaijani_Armed_Forces | Baku_Higher_All-Arms_Command_School |
| 1088.0 | 25391.0 | Azerbaijani_Armed_Forces | Russia |
| 1088.0 | 40196.0 | Azerbaijani_Armed_Forces | Transport_in_Azerbaijan |
| 1088.0 | 4764461.0 | Azerbaijani_Armed_Forces | World_War_I |
| 1088.0 | 6.5911124e7 | Azerbaijani_Armed_Forces | For_Services_in_the_Rear_in_the_Patriotic_War_Medal |
| 1088.0 | 2.1659771e7 | Azerbaijani_Armed_Forces | Military_history_of_Azerbaijan |
| 1088.0 | 3.8392125e7 | Azerbaijani_Armed_Forces | Sonya_class_minesweeper |
| 1088.0 | 493727.0 | Azerbaijani_Armed_Forces | Aero_L-39_Albatros |
| 1088.0 | 6.7120883e7 | Azerbaijani_Armed_Forces | Armenian_Army |
| 1088.0 | 401606.0 | Azerbaijani_Armed_Forces | Index_of_Azerbaijan-related_articles |
| 1088.0 | 1.1169023e7 | Azerbaijani_Armed_Forces | Ministry_of_Defence_Industry_of_Azerbaijan |
| 1088.0 | 5.829427e7 | Azerbaijani_Armed_Forces | Mountains_of_Azerbaijan |
| 1088.0 | 638594.0 | Azerbaijani_Armed_Forces | Non-belligerent |
| 1088.0 | 3.2945088e7 | Azerbaijani_Armed_Forces | Red_Army_invasion_of_Azerbaijan |
| 1088.0 | 31750.0 | Azerbaijani_Armed_Forces | Ukraine |
| 1088.0 | 1.4465664e7 | Azerbaijani_Armed_Forces | Absheron_Peninsula |
| 1088.0 | 3.363915e7 | Azerbaijani_Armed_Forces | Azerbaijani_tea_culture |
| 1088.0 | 31730.0 | Azerbaijani_Armed_Forces | British_Armed_Forces |
| 1088.0 | 7105894.0 | Azerbaijani_Armed_Forces | Flora_of_Azerbaijan |
| 1088.0 | 68253.0 | Azerbaijani_Armed_Forces | List_of_sovereign_states |
| 1088.0 | 1.7935711e7 | Azerbaijani_Armed_Forces | Shirvanshah |
| 1088.0 | 1.2835793e7 | Azerbaijani_Armed_Forces | Azerbaijani_cuisine |
| 1088.0 | 2.1634642e7 | Azerbaijani_Armed_Forces | Novruz_in_Azerbaijan |
| 1088.0 | 1127085.0 | Azerbaijani_Armed_Forces | Stockholm_International_Peace_Research_Institute |
| 1088.0 | 6.6058582e7 | Azerbaijani_Armed_Forces | Hero_of_the_Patriotic_War |
| 1088.0 | 19279.0 | Azerbaijani_Armed_Forces | Mongolian_Armed_Forces |
| 1088.0 | 6.6404258e7 | Azerbaijani_Armed_Forces | Azerbaijani_Red_Army |
| 1088.0 | 6.614052e7 | Azerbaijani_Armed_Forces | Karim_Valiyev |
| 1088.0 | 6.2201975e7 | Azerbaijani_Armed_Forces | Nakhchivan_culture |
| 1088.0 | 5.4147626e7 | Azerbaijani_Armed_Forces | State_Security_Service_(Azerbaijan) |
| 1088.0 | 161087.0 | Azerbaijani_Armed_Forces | Timor_Leste_Defence_Force |
| 1088.0 | 5321.0 | Azerbaijani_Armed_Forces | Czech_Republic |
| 1088.0 | 67638.0 | Azerbaijani_Armed_Forces | Demographics_of_Azerbaijan |
| 1088.0 | 1.1776466e7 | Azerbaijani_Armed_Forces | Ethnic_minorities_in_Azerbaijan |
| 1088.0 | 5.3412468e7 | Azerbaijani_Armed_Forces | Military_ranks_of_Azerbaijan |
| 1088.0 | 457051.0 | Azerbaijani_Armed_Forces | National_emblem_of_Azerbaijan |
| 1088.0 | 2.3290453e7 | Azerbaijani_Armed_Forces | Peacekeeping_forces_of_Azerbaijan |
| 1088.0 | 4.4208502e7 | Azerbaijani_Armed_Forces | SA-3_Goa |
| 1088.0 | 3.3949683e7 | Azerbaijani_Armed_Forces | Air_Force_Day |
| 1088.0 | 6.0544953e7 | Azerbaijani_Armed_Forces | Azerbaijan_Higher_Military_Academy |
| 1088.0 | 79745.0 | Azerbaijani_Armed_Forces | Cluster_munition |
| 1088.0 | 21263.0 | Azerbaijani_Armed_Forces | Korean_People's_Army |
| 1088.0 | 2.2462867e7 | Azerbaijani_Armed_Forces | Soviet_Ground_Forces |
| 1088.0 | 382302.0 | Azerbaijani_Armed_Forces | Su-25 |
| 1088.0 | 6.4334706e7 | Azerbaijani_Armed_Forces | Azerbaijan_Higher_Naval_Academy |
| 1088.0 | 7077602.0 | Azerbaijani_Armed_Forces | Environment_of_Azerbaijan |
| 1088.0 | 865389.0 | Azerbaijani_Armed_Forces | International_Crisis_Group |
| 1088.0 | 1.8947898e7 | Azerbaijani_Armed_Forces | Amnesty_International |
| 1088.0 | 746.0 | Azerbaijani_Armed_Forces | Azerbaijan |
| 1088.0 | 1.9859938e7 | Azerbaijani_Armed_Forces | Baku_Kala_Air_Base |
| 1088.0 | 1610018.0 | Azerbaijani_Armed_Forces | Hong_Kong_Garrison |
| 1088.0 | 1478175.0 | Azerbaijani_Armed_Forces | Public_holidays_in_Azerbaijan |
| 1088.0 | 69328.0 | Azerbaijani_Armed_Forces | United_Arab_Emirates |
| 1088.0 | 3.9780666e7 | Azerbaijani_Armed_Forces | History_of_the_name_Azerbaijan |
| 1088.0 | 6.7122586e7 | Azerbaijani_Armed_Forces | 777th_Special_Forces_Regiment |
| 1088.0 | 2.2576829e7 | Azerbaijani_Armed_Forces | Agriculture_in_Azerbaijan |
| 1088.0 | 1081.0 | Azerbaijani_Armed_Forces | Economy_of_Azerbaijan |
| 1088.0 | 3764215.0 | Azerbaijani_Armed_Forces | Prime_Minister_of_Azerbaijan |
| 1088.0 | 27276.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_Saudi_Arabia |
| 1088.0 | 4566.0 | Azerbaijani_Armed_Forces | Baku |
| 1088.0 | 40195.0 | Azerbaijani_Armed_Forces | Telecommunications_in_Azerbaijan |
| 1088.0 | 6.7538996e7 | Azerbaijani_Armed_Forces | Şəmkir |
| 1088.0 | 2.5131731e7 | Azerbaijani_Armed_Forces | Azerbaijani_Army |
| 1088.0 | 2.9149908e7 | Azerbaijani_Armed_Forces | Coat_of_arms_of_Azerbaijan |
| 1088.0 | 1.6569312e7 | Azerbaijani_Armed_Forces | Education_in_Azerbaijan |
| 1088.0 | 22489.0 | Azerbaijani_Armed_Forces | Oklahoma |
| 1088.0 | 2.7167856e7 | Azerbaijani_Armed_Forces | Azerbaijani_Air_Force |
| 1088.0 | 15166.0 | Azerbaijani_Armed_Forces | Infantry_fighting_vehicle |
| 1088.0 | 7015198.0 | Azerbaijani_Armed_Forces | LGBT_rights_in_Azerbaijan |
| 1088.0 | 7.1994248e7 | Azerbaijani_Armed_Forces | Defense_Forces_of_Georgia |
| 1088.0 | 2.7911049e7 | Azerbaijani_Armed_Forces | Ministry_of_Defence_(Azerbaijan) |
| 1088.0 | 1.2975707e7 | Azerbaijani_Armed_Forces | Safar_Abiyev |
| 1088.0 | 26779.0 | Azerbaijani_Armed_Forces | Soviet_Union |
| 1088.0 | 6.1362503e7 | Azerbaijani_Armed_Forces | Stone_Age_in_Azerbaijan |
| 1088.0 | 17760.0 | Azerbaijani_Armed_Forces | Lao_People's_Armed_Forces |
| 1088.0 | 192825.0 | Azerbaijani_Armed_Forces | Azerbaijani_language |
| 1088.0 | 194200.0 | Azerbaijani_Armed_Forces | International_Security_Assistance_Force |
| 1088.0 | 2.0222257e7 | Azerbaijani_Armed_Forces | MKEK |
| 1088.0 | 21133.0 | Azerbaijani_Armed_Forces | NATO |
| 1088.0 | 4116970.0 | Azerbaijani_Armed_Forces | Central_Bank_of_Azerbaijan |
| 1088.0 | 400853.0 | Azerbaijani_Armed_Forces | Hero_of_the_Soviet_Union |
| 1088.0 | 1.6278429e7 | Azerbaijani_Armed_Forces | Outline_of_Azerbaijan |
| 1088.0 | 2.1288922e7 | Azerbaijani_Armed_Forces | Azerbaijan_during_World_War_II |
| 1088.0 | 1.0927351e7 | Azerbaijani_Armed_Forces | Azerbaijani_National_Guard |
| 1088.0 | 4020775.0 | Azerbaijani_Armed_Forces | First_Nagorno-Karabakh_War |
| 1088.0 | 5.515162e7 | Azerbaijani_Armed_Forces | ISSN_(identifier) |
| 1088.0 | 14532.0 | Azerbaijani_Armed_Forces | Italy |
| 1088.0 | 1986639.0 | Azerbaijani_Armed_Forces | Languages_of_Azerbaijan |
| 1088.0 | 4.1471871e7 | Azerbaijani_Armed_Forces | List_of_lakes_of_Azerbaijan |
| 1088.0 | 917076.0 | Azerbaijani_Armed_Forces | Ayaz_Mutallibov |
| 1088.0 | 213173.0 | Azerbaijani_Armed_Forces | Conscripts |
| 1088.0 | 1082.0 | Azerbaijani_Armed_Forces | Geography_of_Azerbaijan |
| 1088.0 | 9526432.0 | Azerbaijani_Armed_Forces | MRAP |
| 1088.0 | 386742.0 | Azerbaijani_Armed_Forces | SA-2 |
| 1088.0 | 412390.0 | Azerbaijani_Armed_Forces | Administrative_divisions_of_Azerbaijan |
| 1088.0 | 30215.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_Turkmenistan |
| 1088.0 | 5.0716679e7 | Azerbaijani_Armed_Forces | Nasosnaya_(air_base) |
| 1088.0 | 25194.0 | Azerbaijani_Armed_Forces | Qatar_Armed_Forces |
| 1088.0 | 3206857.0 | Azerbaijani_Armed_Forces | Religion_in_Azerbaijan |
| 1088.0 | 3.5663369e7 | Azerbaijani_Armed_Forces | Sitalchay_Military_Airbase |
| 1088.0 | 3.8938602e7 | Azerbaijani_Armed_Forces | \"For_Faultless_Service\"_medal |
| 1088.0 | 6.7262853e7 | Azerbaijani_Armed_Forces | Ali-Agha_Shikhlinski |
| 1088.0 | 1351138.0 | Azerbaijani_Armed_Forces | Elections_in_Azerbaijan |
| 1088.0 | 14650.0 | Azerbaijani_Armed_Forces | Indonesian_National_Armed_Forces |
| 1088.0 | 6.6168931e7 | Azerbaijani_Armed_Forces | Marine_Infantry_of_Azerbaijan |
| 1088.0 | 1300375.0 | Azerbaijani_Armed_Forces | Treaty_on_Conventional_Armed_Forces_in_Europe |
| 1088.0 | 31861.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Republic_of_Uzbekistan |
| 1088.0 | 7658483.0 | Azerbaijani_Armed_Forces | Section_907 |
| 1088.0 | 1.0927815e7 | Azerbaijani_Armed_Forces | Caspian_Guard_Initiative |
| 1088.0 | 1.9653787e7 | Azerbaijani_Armed_Forces | Caspian_Sea |
| 1088.0 | 3.4048567e7 | Azerbaijani_Armed_Forces | Marauder_(vehicle) |
| 1088.0 | 5.5829912e7 | Azerbaijani_Armed_Forces | Natural_resources_of_Azerbaijan |
| 1088.0 | 877787.0 | Azerbaijani_Armed_Forces | Azerbaijani_literature |
| 1088.0 | 3708.0 | Azerbaijani_Armed_Forces | Brussels |
| 1088.0 | 214529.0 | Azerbaijani_Armed_Forces | Dependent_territory |
| 1088.0 | 5.5049264e7 | Azerbaijani_Armed_Forces | ISBN_(identifier) |
| 1088.0 | 9282173.0 | Azerbaijani_Armed_Forces | Israel |
| 1088.0 | 1.2085342e7 | Azerbaijani_Armed_Forces | Khanates_of_the_Caucasus |
| 1088.0 | 1.9374465e7 | Azerbaijani_Armed_Forces | Xətai_raion |
| 1088.0 | 4059749.0 | Azerbaijani_Armed_Forces | Artsakh_Defence_Army |
| 1088.0 | 8696322.0 | Azerbaijani_Armed_Forces | Azerbaijan_National_Academy_of_Sciences |
| 1088.0 | 4941797.0 | Azerbaijani_Armed_Forces | Azerbaijani_Land_Forces |
| 1088.0 | 188675.0 | Azerbaijani_Armed_Forces | Baltic_states |
| 1088.0 | 3.0322746e7 | Azerbaijani_Armed_Forces | General_Staff_of_Azerbaijani_Armed_Forces |
| 1088.0 | 2785204.0 | Azerbaijani_Armed_Forces | Japan_Self-Defense_Forces |
| 1088.0 | 16702.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Kyrgyz_Republic |
| 1088.0 | 539716.0 | Azerbaijani_Armed_Forces | Landing_craft |
| 1088.0 | 2.3269917e7 | Azerbaijani_Armed_Forces | Military_of_Azerbaijan |
| 1088.0 | 7193518.0 | Azerbaijani_Armed_Forces | Special_Forces_Command_(Turkey) |
| 1088.0 | 1.3634062e7 | Azerbaijani_Armed_Forces | Constitution_of_Azerbaijan |
| 1088.0 | 7.1858151e7 | Azerbaijani_Armed_Forces | Dollyar_Air_Base |
| 1088.0 | 2.812722e7 | Azerbaijani_Armed_Forces | List_of_earthquakes_in_Azerbaijan |
| 1088.0 | 23235.0 | Azerbaijani_Armed_Forces | Pakistan |
| 1088.0 | 1049084.0 | Azerbaijani_Armed_Forces | U.S._National_Guard |
| 1088.0 | 6.1912815e7 | Azerbaijani_Armed_Forces | \"90th_Anniversary_of_the_Armed_Forces_of_Azerbaijan_(1918–2008)\"_Medal |
| 1088.0 | 698454.0 | Azerbaijani_Armed_Forces | Azerbaijanis |
| 1088.0 | 9322682.0 | Azerbaijani_Armed_Forces | Karabakh |
| 1088.0 | 802.0 | Azerbaijani_Armed_Forces | Ankara |
| 1088.0 | 23369.0 | Azerbaijani_Armed_Forces | Pakistan_Armed_Forces |
| 1088.0 | 4501200.0 | Azerbaijani_Armed_Forces | Parthian_Empire |
| 1088.0 | 4.190231e7 | Azerbaijani_Armed_Forces | Special_Forces_of_Azerbaijan |
| 1088.0 | 3.1022059e7 | Azerbaijani_Armed_Forces | State_Oil_Company_of_Azerbaijan_Republic |
| 1088.0 | 380322.0 | Azerbaijani_Armed_Forces | Il-76 |
| 1088.0 | 1492960.0 | Azerbaijani_Armed_Forces | Nakhchivan_(city) |
| 1088.0 | 6.2087908e7 | Azerbaijani_Armed_Forces | Russo-Persian_War_(1804–13) |
| 1088.0 | 6446390.0 | Azerbaijani_Armed_Forces | State_Partnership_Program |
| 1088.0 | 905795.0 | Azerbaijani_Armed_Forces | Treaty_of_Gulistan |
| 1088.0 | 6.6016112e7 | Azerbaijani_Armed_Forces | Memorial_Day_(Azerbaijan) |
| 1088.0 | 3.1854531e7 | Azerbaijani_Armed_Forces | Namer_(vehicle) |
| 1088.0 | 30095.0 | Azerbaijani_Armed_Forces | Republic_of_China_Armed_Forces |
| 1088.0 | 4.3825422e7 | Azerbaijani_Armed_Forces | S-200_Angara/Vega/Dubna |
| 1088.0 | 2.3408142e7 | Azerbaijani_Armed_Forces | Sri_Lanka_Armed_Forces |
| 1088.0 | 6.6317677e7 | Azerbaijani_Armed_Forces | YARASA_Special_Forces |
| 1088.0 | 1.0254803e7 | Azerbaijani_Armed_Forces | Cinema_of_Azerbaijan |
| 1088.0 | 17779.0 | Azerbaijani_Armed_Forces | Lebanese_Armed_Forces |
| 1088.0 | 5.9921988e7 | Azerbaijani_Armed_Forces | Metallurgy_in_Azerbaijan |
| 1088.0 | 27479.0 | Azerbaijani_Armed_Forces | Syrian_Armed_Forces |
| 1088.0 | 6.7613776e7 | Azerbaijani_Armed_Forces | TecSAR |
| 1088.0 | 1.2339349e7 | Azerbaijani_Armed_Forces | Architecture_of_Azerbaijan |
| 1088.0 | 6.6286297e7 | Azerbaijani_Armed_Forces | Karam_Mustafayev |
| 1088.0 | 26295.0 | Azerbaijani_Armed_Forces | Russian_Civil_War |
| 1088.0 | 5424688.0 | Azerbaijani_Armed_Forces | Jordanian_Armed_Forces |
| 1088.0 | 956689.0 | Azerbaijani_Armed_Forces | Kura–Araxes_culture |
| 1088.0 | 5024972.0 | Azerbaijani_Armed_Forces | Operation_Edelweiss |
| 1088.0 | 3.6369933e7 | Azerbaijani_Armed_Forces | Orders,_decorations,_and_medals_of_Azerbaijan |
| 1088.0 | 2.6964606e7 | Azerbaijani_Armed_Forces | Austria |
| 1088.0 | 6.4611227e7 | Azerbaijani_Armed_Forces | Azerbaijani_Air_and_Air_Defence_Force |
| 1088.0 | 6.698864e7 | Azerbaijani_Armed_Forces | Caves_of_Azerbaijan |
| 1088.0 | 1.8846287e7 | Azerbaijani_Armed_Forces | Jabrayil |
| 1088.0 | 7.18581e7 | Azerbaijani_Armed_Forces | Kyurdamir_Air_Base |
| 1088.0 | 7115553.0 | Azerbaijani_Armed_Forces | Barda,_Azerbaijan |
| 1088.0 | 6.7740154e7 | Azerbaijani_Armed_Forces | Jane's_Information_Group |
| 1088.0 | 3.0455197e7 | Azerbaijani_Armed_Forces | Khojaly–Gadabay_culture |
| 1088.0 | 1519005.0 | Azerbaijani_Armed_Forces | Sultan_of_Oman's_Armed_Forces |
| 1088.0 | 581195.0 | Azerbaijani_Armed_Forces | Copyright_status_of_works_by_the_federal_government_of_the_United_States |
| 1088.0 | 7077806.0 | Azerbaijani_Armed_Forces | Orography_of_Azerbaijan |
| 1088.0 | 1177214.0 | Azerbaijani_Armed_Forces | Pennon |
| 1088.0 | 740508.0 | Azerbaijani_Armed_Forces | Republic_of_Azerbaijan |
| 1088.0 | 1.0928518e7 | Azerbaijani_Armed_Forces | Azerbaijani_Coast_Guard |
| 1088.0 | 4016533.0 | Azerbaijani_Armed_Forces | National_Assembly_(Azerbaijan) |
| 1088.0 | 1.9510878e7 | Azerbaijani_Armed_Forces | Sitalcay |
| 1088.0 | 7.1403487e7 | Azerbaijani_Armed_Forces | State_Border_Service_(Azerbaijan) |
| 1088.0 | 5366487.0 | Azerbaijani_Armed_Forces | Human_rights_in_Azerbaijan |
| 1088.0 | 1.1356544e7 | Azerbaijani_Armed_Forces | Law_enforcement_in_Azerbaijan |
| 1088.0 | 214413.0 | Azerbaijani_Armed_Forces | Armenian_diaspora |
| 1088.0 | 6.591061e7 | Azerbaijani_Armed_Forces | Hero_of_the_Patriotic_War_Medal |
| 1088.0 | 123503.0 | Azerbaijani_Armed_Forces | MiG-21 |
| 1088.0 | 3.3570513e7 | Azerbaijani_Armed_Forces | Russians_in_Azerbaijan |
| 1088.0 | 7.1286679e7 | Azerbaijani_Armed_Forces | Shulaveri-Shomu_culture |
| 1088.0 | 4.5541218e7 | Azerbaijani_Armed_Forces | 295th_Motor_Rifle_Division |
| 1088.0 | 1.8838818e7 | Azerbaijani_Armed_Forces | Bibiheybət |
| 1088.0 | 5.5095974e7 | Azerbaijani_Armed_Forces | Healthcare_in_Azerbaijan |
| 1088.0 | 20282.0 | Azerbaijani_Armed_Forces | Mechanized_infantry |
| 1088.0 | 3.7721373e7 | Azerbaijani_Armed_Forces | Medicine_in_Azerbaijan |
| 1088.0 | 20394.0 | Azerbaijani_Armed_Forces | Tatmadaw |
| 1088.0 | 4.2543864e7 | Azerbaijani_Armed_Forces | United_States_Air_Forces_in_Europe |
| 1088.0 | 4.0963939e7 | Azerbaijani_Armed_Forces | Zakir_Hasanov |
| 1088.0 | 2.8119649e7 | Azerbaijani_Armed_Forces | Special_Purpose_Police_Unit |
| 1088.0 | 31717.0 | Azerbaijani_Armed_Forces | United_Kingdom |
| 1088.0 | 6064651.0 | Azerbaijani_Armed_Forces | Eldiguzids |
| 1088.0 | 6.5911037e7 | Azerbaijani_Armed_Forces | For_Distinction_in_Battle_Medal |
| 1088.0 | 14939.0 | Azerbaijani_Armed_Forces | Intercontinental_ballistic_missile |
| 1088.0 | 1.9360365e7 | Azerbaijani_Armed_Forces | North_Atlantic_Treaty_Organization |
| 1088.0 | 1097.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_Armenia |
| 1088.0 | 23448.0 | Azerbaijani_Armed_Forces | Armed_Forces_of_the_Philippines |
| 1088.0 | 4380486.0 | Azerbaijani_Armed_Forces | Armenian–Tatar_massacres_of_1905–1907 |
| 1088.0 | 407062.0 | Azerbaijani_Armed_Forces | Azərbaycan_marşı |
| 1088.0 | 2.3465971e7 | Azerbaijani_Armed_Forces | Government_of_Azerbaijan |
| 1088.0 | 7877570.0 | Azerbaijani_Armed_Forces | Individual_Partnership_Action_Plan |
| 1088.0 | 5.5289023e7 | Azerbaijani_Armed_Forces | Red_Army_invasion_of_Armenia |
| 1088.0 | 6.4783403e7 | Azerbaijani_Armed_Forces | 396th_Rifle_Division |
| 1088.0 | 6.9019186e7 | Azerbaijani_Armed_Forces | 416th_Rifle_Division_(Soviet_Union) |
| 1088.0 | 2.2469823e7 | Azerbaijani_Armed_Forces | Azerbaijani_peacekeeping_forces |
| 1088.0 | 3.5079877e7 | Azerbaijani_Armed_Forces | Azerbaijani_traditional_clothing |
| 1088.0 | 5043324.0 | Azerbaijani_Armed_Forces | Iraq_War |
| 1088.0 | 4627429.0 | Azerbaijani_Armed_Forces | Iraqi_Armed_Forces |
| 1088.0 | 1.905571e7 | Azerbaijani_Armed_Forces | Jebrayil |
| 1088.0 | 1.3969214e7 | Azerbaijani_Armed_Forces | Main_Agency_of_Missiles_and_Artillery_of_the_Ministry_of_Defense_of_the_Russian_Federation |
| 1088.0 | 6040932.0 | Azerbaijani_Armed_Forces | Security_Forces_Command |
| 1088.0 | 1151523.0 | Azerbaijani_Armed_Forces | Azerbaijani_manat |
| 1088.0 | 213497.0 | Azerbaijani_Armed_Forces | Caucasian_Albania |
| 1088.0 | 6.5910879e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Kalbajar_Medal |
| 1088.0 | 5245787.0 | Azerbaijani_Armed_Forces | GUAM |
| 1088.0 | 6.7667374e7 | Azerbaijani_Armed_Forces | Kara_Koyunlu |
| 1088.0 | 1.8933221e7 | Azerbaijani_Armed_Forces | Royal_Brunei_Armed_Forces |
| 1088.0 | 31841.0 | Azerbaijani_Armed_Forces | United_Arab_Emirates_Armed_Forces |
| 1088.0 | 68932.0 | Azerbaijani_Armed_Forces | Bangladesh_Armed_Forces |
| 1088.0 | 1115368.0 | Azerbaijani_Armed_Forces | Maldives_National_Defence_Force |
| 1088.0 | 8417589.0 | Azerbaijani_Armed_Forces | Sallarid_dynasty |
| 1088.0 | 6.5431221e7 | Azerbaijani_Armed_Forces | 2020_Nagorno-Karabakh_war |
| 1088.0 | 661551.0 | Azerbaijani_Armed_Forces | Ganja,_Azerbaijan |
| 1088.0 | 2.6217562e7 | Azerbaijani_Armed_Forces | History_of_Azerbaijani_animation |
| 1088.0 | 4562230.0 | Azerbaijani_Armed_Forces | Oklahoma_National_Guard |
| 1088.0 | 6.7228635e7 | Azerbaijani_Armed_Forces | Rovshan_Akbarov |
| 1088.0 | 8486749.0 | Azerbaijani_Armed_Forces | Russian_Space_Forces |
| 1088.0 | 382305.0 | Azerbaijani_Armed_Forces | Su-24 |
| 1088.0 | 7940585.0 | Azerbaijani_Armed_Forces | Aq_Qoyunlu |
| 1088.0 | 5042916.0 | Azerbaijani_Armed_Forces | Canada |
| 1088.0 | 510603.0 | Azerbaijani_Armed_Forces | Jane's_Fighting_Ships |
| 1088.0 | 1.1447628e7 | Azerbaijani_Armed_Forces | Abkhazian_Armed_Forces |
| 1088.0 | 5731277.0 | Azerbaijani_Armed_Forces | Fauna_of_Azerbaijan |
| 1088.0 | 2.2765442e7 | Azerbaijani_Armed_Forces | Ilham_Aliyev |
| 1088.0 | 542300.0 | Azerbaijani_Armed_Forces | Ilkhanate |
| 1088.0 | 5.5284726e7 | Azerbaijani_Armed_Forces | Judiciary_of_Azerbaijan |
| 1088.0 | 3.4024533e7 | Azerbaijani_Armed_Forces | Leyla-Tepe_culture |
| 1088.0 | 4674848.0 | Azerbaijani_Armed_Forces | Russo-Persian_War_(1826–1828) |
| 1088.0 | 2.3207828e7 | Azerbaijani_Armed_Forces | Azerbaijan_Defense_Industry |
| 1088.0 | 1.1670391e7 | Azerbaijani_Armed_Forces | Gabala_Radar_Station |
| 1088.0 | 2.8087409e7 | Azerbaijani_Armed_Forces | Khosrov_bey_Sultanov |
| 1088.0 | 343356.0 | Azerbaijani_Armed_Forces | List_of_cities_in_Azerbaijan |
| 1088.0 | 1.7967625e7 | Azerbaijani_Armed_Forces | Mineral_industry_of_Azerbaijan |
| 1088.0 | 6.59111e7 | Azerbaijani_Armed_Forces | Participant_of_the_Patriotic_War_Medal |
| 1088.0 | 66890.0 | Azerbaijani_Armed_Forces | People's_Liberation_Army |
| 1088.0 | 4247739.0 | Azerbaijani_Armed_Forces | U.S._Navy_SEALs |
| 1088.0 | 2.8017536e7 | Azerbaijani_Armed_Forces | Valeh_Barshadli |
| 1088.0 | 9609093.0 | Azerbaijani_Armed_Forces | Beylagan_(city) |
| 1088.0 | 519489.0 | Azerbaijani_Armed_Forces | Eastern_Front_(World_War_II) |
| 1088.0 | 1087.0 | Azerbaijani_Armed_Forces | Foreign_relations_of_Azerbaijan |
| 1088.0 | 1.1197435e7 | Azerbaijani_Armed_Forces | Maciej_Sulkiewicz |
| 1088.0 | 938372.0 | Azerbaijani_Armed_Forces | President_of_Azerbaijan |
| 1088.0 | 32817.0 | Azerbaijani_Armed_Forces | Vladimir_Putin |
| 1088.0 | 6.5624452e7 | Azerbaijani_Armed_Forces | 2016_Nagorno-Karabakh_clashes |
| 1088.0 | 2.3597901e7 | Azerbaijani_Armed_Forces | Azadliq_Square,_Baku |
| 1088.0 | 6131588.0 | Azerbaijani_Armed_Forces | Petroleum_industry_in_Azerbaijan |
| 1088.0 | 6.631834e7 | Azerbaijani_Armed_Forces | Second_Karabakh_War |
| 1088.0 | 1.156279e7 | Azerbaijani_Armed_Forces | Second_World_War |
| 1088.0 | 2884207.0 | Azerbaijani_Armed_Forces | Advanced_Research_and_Assessment_Group |
| 1088.0 | 2.0024921e7 | Azerbaijani_Armed_Forces | Armenian-occupied_territories_surrounding_Nagorno-Karabakh |
| 1088.0 | 408283.0 | Azerbaijani_Armed_Forces | Azerbaijani_Popular_Front_Party |
| 1088.0 | 6.5910929e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Khojavend_Medal |
| 1088.0 | 1.1623685e7 | Azerbaijani_Armed_Forces | Freedom_Support_Act |
| 1088.0 | 27019.0 | Azerbaijani_Armed_Forces | South_Korea |
| 1088.0 | 2.3207385e7 | Azerbaijani_Armed_Forces | Azerbaijan_Navy |
| 1088.0 | 6367906.0 | Azerbaijani_Armed_Forces | Azerbaijani_dances |
| 1088.0 | 704623.0 | Azerbaijani_Armed_Forces | CIA |
| 1088.0 | 3.7265091e7 | Azerbaijani_Armed_Forces | Caspian_Sea_Flotilla |
| 1088.0 | 2071240.0 | Azerbaijani_Armed_Forces | Culture_of_Azerbaijan |
| 1088.0 | 7761715.0 | Azerbaijani_Armed_Forces | Red_Army_invasion_of_Georgia |
| 1088.0 | 19076.0 | Azerbaijani_Armed_Forces | Macao_Garrison |
| 1088.0 | 6.4207973e7 | Azerbaijani_Armed_Forces | Media_of_Azerbaijan |
| 1088.0 | 182309.0 | Azerbaijani_Armed_Forces | MiG-29 |
| 1088.0 | 59510.0 | Azerbaijani_Armed_Forces | Russians |
| 1088.0 | 4363966.0 | Azerbaijani_Armed_Forces | History_of_Azerbaijan |
| 1088.0 | 65220.0 | Azerbaijani_Armed_Forces | Nagorno-Karabakh |
| 1088.0 | 6.1170719e7 | Azerbaijani_Armed_Forces | Azerbaijani_Army_100th_anniversary_medal |
| 1088.0 | 380320.0 | Azerbaijani_Armed_Forces | MiG-25 |
| 1088.0 | 21330.0 | Azerbaijani_Armed_Forces | Nepalese_Armed_Forces |
| 1088.0 | 25682.0 | Azerbaijani_Armed_Forces | Red_Army |
| 1088.0 | 5.2597609e7 | Azerbaijani_Armed_Forces | Swietochowski,_Tadeusz |
| 1088.0 | 1711234.0 | Azerbaijani_Armed_Forces | United_States_European_Command |
| 1088.0 | 6.5910946e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Sugovushan_Medal |
| 1088.0 | 523670.0 | Azerbaijani_Armed_Forces | List_of_states_with_limited_recognition |
| 1088.0 | 3.0314065e7 | Azerbaijani_Armed_Forces | Najmeddin_Sadikov |
| 1088.0 | 4.3202421e7 | Azerbaijani_Armed_Forces | The_Land_of_Fire |
| 1088.0 | 6.6065854e7 | Azerbaijani_Armed_Forces | Baku_Victory_Parade_of_2020 |
| 1088.0 | 3434750.0 | Azerbaijani_Armed_Forces | United_States |
| 1088.0 | 3.9140285e7 | Azerbaijani_Armed_Forces | 23rd_Guards_Motor_Rifle_Division |
| 1088.0 | 5.7562858e7 | Azerbaijani_Armed_Forces | Elta |
| 1088.0 | 5306394.0 | Azerbaijani_Armed_Forces | Haditha,_Iraq |
| 1088.0 | 1.4305018e7 | Azerbaijani_Armed_Forces | Islamic_Republic_of_Iran_Armed_Forces |
| 1088.0 | 7150805.0 | Azerbaijani_Armed_Forces | National_parks_of_Azerbaijan |
| 1088.0 | 3295318.0 | Azerbaijani_Armed_Forces | Patrol_craft |
| 1088.0 | 1340560.0 | Azerbaijani_Armed_Forces | Treaty_of_Turkmenchay |
| 1088.0 | 2563036.0 | Azerbaijani_Armed_Forces | Hazi_Aslanov |
| 1088.0 | 381496.0 | Azerbaijani_Armed_Forces | JF-17 |
| 1088.0 | 3.5482625e7 | Azerbaijani_Armed_Forces | Armavir_Radar_Station |
| 1088.0 | 404448.0 | Azerbaijani_Armed_Forces | Azerbaijan_Soviet_Socialist_Republic |
| 1088.0 | 2.1653069e7 | Azerbaijani_Armed_Forces | Geology_of_Azerbaijan |
| 1088.0 | 4.0503488e7 | Azerbaijani_Armed_Forces | List_of_equipment_of_the_Azerbaijani_Land_Forces |
| 1088.0 | 408284.0 | Azerbaijani_Armed_Forces | List_of_political_parties_in_Azerbaijan |
| 1088.0 | 3.0927438e7 | Azerbaijani_Armed_Forces | Achaemenid_Empire |
| 1088.0 | 6.5910935e7 | Azerbaijani_Armed_Forces | For_the_Liberation_of_Jabrayil_Medal |
| 1088.0 | 162017.0 | Azerbaijani_Armed_Forces | Rayon |
| 1088.0 | 67658.0 | Azerbaijani_Armed_Forces | Bahrain_Defence_Force |
| 1088.0 | 4788086.0 | Azerbaijani_Armed_Forces | Azerbaijan_Medical_University |
| 1088.0 | 5.415509e7 | Azerbaijani_Armed_Forces | State_Service_for_Mobilization_and_Conscription_of_Azerbaijan |
| 1238.0 | 44975.0 | Atomic_bomb | Phrase |
| 1238.0 | 21785.0 | Atomic_bomb | Nuclear_weapon |
| 1342.0 | 1400.0 | A.D | Anno_Domini |
| 1580.0 | 6.5715134e7 | Alcidamas | RERO_(identifier) |
| 1580.0 | 13621.0 | Alcidamas | Hadrian |
| 1580.0 | 6.3435015e7 | Alcidamas | JSTOR_(identifier) |
| 1580.0 | 5.5017667e7 | Alcidamas | Muse |
| 1580.0 | 22022.0 | Alcidamas | Nietzsche |
| 1580.0 | 99665.0 | Alcidamas | Friedrich_Blass |
| 1580.0 | 168260.0 | Alcidamas | Isocrates |
| 1580.0 | 22537.0 | Alcidamas | Odysseus |
| 1580.0 | 2093019.0 | Alcidamas | Palamedes_(mythology) |
| 1580.0 | 1715161.0 | Alcidamas | Aeolis |
| 1580.0 | 4682035.0 | Alcidamas | Martin_Litchfield_West |
| 1580.0 | 4633006.0 | Alcidamas | Elaea_(Aeolis) |
| 1580.0 | 1216.0 | Alcidamas | Athens |
| 1580.0 | 5.5049264e7 | Alcidamas | ISBN_(identifier) |
| 1580.0 | 1.8935551e7 | Alcidamas | Public_domain |
| 1580.0 | 2.6273281e7 | Alcidamas | Messenia_(ancient_region) |
| 1580.0 | 1.5103874e7 | Alcidamas | Contest_of_Homer_and_Hesiod |
| 1580.0 | 66540.0 | Alcidamas | Ancient_Greece |
| 1580.0 | 98394.0 | Alcidamas | Gorgias |
| 1580.0 | 6771651.0 | Alcidamas | Teubner |
| 1580.0 | 25447.0 | Alcidamas | Rhetoric |
| 1580.0 | 49646.0 | Alcidamas | Sophist |
| 1580.0 | 6.3717472e7 | Alcidamas | SUDOC_(identifier) |
| 1580.0 | 6.371749e7 | Alcidamas | VIAF_(identifier) |
| 1580.0 | 1692816.0 | Alcidamas | Rhetoric_(Aristotle) |
| 1580.0 | 308.0 | Alcidamas | Aristotle |
| 1580.0 | 72624.0 | Alcidamas | Encyclopædia_Britannica_Eleventh_Edition |
| 1580.0 | 6.3826803e7 | Alcidamas | ISNI_(identifier) |
| 1580.0 | 11887.0 | Alcidamas | Greek_language |
| 1580.0 | 100109.0 | Alcidamas | John_Pentland_Mahaffy |
| 1580.0 | 30059.0 | Alcidamas | Troy |
| 1580.0 | 2.4392429e7 | Alcidamas | Commentaria_in_Aristotelem_Graeca |
| 1645.0 | 5.2933884e7 | Ibn_al-Haytham | Abu'l-Hasan_Bayhaqi |
| 1645.0 | 3.1562331e7 | Ibn_al-Haytham | Al-Kashkari |
| 1645.0 | 5280356.0 | Ibn_al-Haytham | Ibn_Sab'in |
| 1645.0 | 998087.0 | Ibn_al-Haytham | Ibn_Yunus |
| 1645.0 | 5.6795161e7 | Ibn_al-Haytham | Ibn_al-A'lam |
| 1645.0 | 1845906.0 | Ibn_al-Haytham | Jaghmini |
| 1645.0 | 4.2199619e7 | Ibn_al-Haytham | Schools_of_Islamic_theology |
| 1645.0 | 2042047.0 | Ibn_al-Haytham | Burhan-ud-din_Kermani |
| 1645.0 | 6596725.0 | Ibn_al-Haytham | Equatorium |
| 1645.0 | 5553121.0 | Ibn_al-Haytham | Latin_translations_of_the_12th_century |
| 1645.0 | 21664.0 | Ibn_al-Haytham | Nebula |
| 1645.0 | 4.7324624e7 | Ibn_al-Haytham | Sadr_ad-Din_Dashtaki |
| 1645.0 | 1.0536691e7 | Ibn_al-Haytham | Thabit_ibn_Qurra |
| 1645.0 | 3.7091344e7 | Ibn_al-Haytham | Al-Harith_ibn_Kalada |
| 1645.0 | 3.5633616e7 | Ibn_al-Haytham | Gholamhossein_Ebrahimi_Dinani |
| 1645.0 | 3022468.0 | Ibn_al-Haytham | Qalb |
| 1645.0 | 5334607.0 | Ibn_al-Haytham | Africa |
| 1645.0 | 4849167.0 | Ibn_al-Haytham | Brethren_of_Purity |
| 1645.0 | 2710259.0 | Ibn_al-Haytham | Jonah_ibn_Janah |
| 1645.0 | 1973599.0 | Ibn_al-Haytham | Martin_Lings |
| 1645.0 | 6.5715134e7 | Ibn_al-Haytham | RERO_(identifier) |
| 1645.0 | 3.7489481e7 | Ibn_al-Haytham | Transactions_of_the_American_Philosophical_Society |
| 1645.0 | 5.1215718e7 | Ibn_al-Haytham | Witelo |
| 1645.0 | 1.2765677e7 | Ibn_al-Haytham | Da'ud_Abu_al-Fadl |
| 1645.0 | 1591728.0 | Ibn_al-Haytham | Emission_theory_(vision) |
| 1645.0 | 13758.0 | Ibn_al-Haytham | History_of_physics |
| 1645.0 | 5.0899261e7 | Ibn_al-Haytham | Ibrahim_ibn_Said_al-Sahli |
| 1645.0 | 6.4412472e7 | Ibn_al-Haytham | Qazi_Sa’id_Qumi |
| 1645.0 | 9117159.0 | Ibn_al-Haytham | Sahl_ibn_Bishr |
| 1645.0 | 1766908.0 | Ibn_al-Haytham | 'Ali_ibn_al-'Abbas_al-Majusi |
| 1645.0 | 4.7317789e7 | Ibn_al-Haytham | 1001_Inventions_and_the_World_of_Ibn_Al-Haytham |
| 1645.0 | 5367777.0 | Ibn_al-Haytham | Abu_Sa'id_al-Afif |
| 1645.0 | 506138.0 | Ibn_al-Haytham | Ali_ibn_Sahl_Rabban_al-Tabari |
| 1645.0 | 317238.0 | Ibn_al-Haytham | Book_of_Fixed_Stars |
| 1645.0 | 14400.0 | Ibn_al-Haytham | History_of_science |
| 1645.0 | 1070221.0 | Ibn_al-Haytham | Human_eye |
| 1645.0 | 14909.0 | Ibn_al-Haytham | Inertia |
| 1645.0 | 7898478.0 | Ibn_al-Haytham | Jamal_ad-Din_Bukhari |
| 1645.0 | 482938.0 | Ibn_al-Haytham | Medieval_medicine_of_Western_Europe |
| 1645.0 | 4.6320983e7 | Ibn_al-Haytham | Shah_Waliullah_Dehlawi |
| 1645.0 | 1593115.0 | Ibn_al-Haytham | Shams_Tabrizi |
| 1645.0 | 32127.0 | Ibn_al-Haytham | University_of_Chicago |
| 1645.0 | 884495.0 | Ibn_al-Haytham | Bibliothèque_nationale |
| 1645.0 | 86728.0 | Ibn_al-Haytham | Bodleian_Library |
| 1645.0 | 3515519.0 | Ibn_al-Haytham | Lambert_quadrilateral |
| 1645.0 | 3.3383114e7 | Ibn_al-Haytham | Muhammad_ibn_Abi_Bakr_al‐Farisi |
| 1645.0 | 201359.0 | Ibn_al-Haytham | Squaring_the_circle |
| 1645.0 | 7.2112001e7 | Ibn_al-Haytham | Abdollah_ibn_Bukhtishu |
| 1645.0 | 196242.0 | Ibn_al-Haytham | Averroism |
| 1645.0 | 1864889.0 | Ibn_al-Haytham | Cosmology |
| 1645.0 | 302794.0 | Ibn_al-Haytham | Depth_perception |
| 1645.0 | 1.6770522e7 | Ibn_al-Haytham | Fathullah_Shirazi |
| 1645.0 | 2.8645073e7 | Ibn_al-Haytham | Ibn_al-Yasamin |
| 1645.0 | 142601.0 | Ibn_al-Haytham | John_Peckham |
| 1645.0 | 5762980.0 | Ibn_al-Haytham | Nisba_(onomastics) |
| 1645.0 | 6.2773262e7 | Ibn_al-Haytham | Sadr_al-Shari'a_al-Asghar |
| 1645.0 | 1741183.0 | Ibn_al-Haytham | Yaʿqūb_ibn_Ṭāriq |
| 1645.0 | 64203.0 | Ibn_al-Haytham | Zaragoza |
| 1645.0 | 4.6781843e7 | Ibn_al-Haytham | Abu_Bakr_Rabee_Ibn_Ahmad_Al-Akhawyni_Bokhari |
| 1645.0 | 6.469612e7 | Ibn_al-Haytham | Al-Badi'_al-Asturlabi |
| 1645.0 | 3510457.0 | Ibn_al-Haytham | Hadi_Sabzavari |
| 1645.0 | 13771.0 | Ibn_al-Haytham | Hellenistic_civilization |
| 1645.0 | 420409.0 | Ibn_al-Haytham | Jami |
| 1645.0 | 1.3226237e7 | Ibn_al-Haytham | Mural_instrument |
| 1645.0 | 2042430.0 | Ibn_al-Haytham | Qumri |
| 1645.0 | 33603.0 | Ibn_al-Haytham | Wrocław |
| 1645.0 | 1.0713305e7 | Ibn_al-Haytham | Abu_Mansur_al-Baghdadi |
| 1645.0 | 5.3368843e7 | Ibn_al-Haytham | Al-Ashraf_Umar_II |
| 1645.0 | 179645.0 | Ibn_al-Haytham | Ash'ari |
| 1645.0 | 5.3093385e7 | Ibn_al-Haytham | Athīr_al-Dīn_al-Abharī |
| 1645.0 | 2666097.0 | Ibn_al-Haytham | Ayn_al-Quzat_Hamadani |
| 1645.0 | 4621330.0 | Ibn_al-Haytham | Banū_Mūsā |
| 1645.0 | 1524517.0 | Ibn_al-Haytham | Chinese_astronomy |
| 1645.0 | 4.2421061e7 | Ibn_al-Haytham | Hiding_in_the_Light |
| 1645.0 | 929147.0 | Ibn_al-Haytham | Moonlight |
| 1645.0 | 8510733.0 | Ibn_al-Haytham | Muhyi_al-Din_al-Maghribi |
| 1645.0 | 22989.0 | Ibn_al-Haytham | Paris |
| 1645.0 | 3.2111866e7 | Ibn_al-Haytham | Ibn_Abi_Ramtha_al-Tamimi |
| 1645.0 | 6.3435015e7 | Ibn_al-Haytham | JSTOR_(identifier) |
| 1645.0 | 6.1571532e7 | Ibn_al-Haytham | Lens_(optics) |
| 1645.0 | 39098.0 | Ibn_al-Haytham | Physical_law |
| 1645.0 | 7.1245601e7 | Ibn_al-Haytham | Shahab_al-Din_Yahya_ibn_Habash_Suhrawardi |
| 1645.0 | 2.4923294e7 | Ibn_al-Haytham | Ulugh_Beg_Observatory |
| 1645.0 | 1253603.0 | Ibn_al-Haytham | Abu_Ma'shar_al-Balkhi |
| 1645.0 | 1782729.0 | Ibn_al-Haytham | Al-Mahani |
| 1645.0 | 1587482.0 | Ibn_al-Haytham | Al-Qabisi |
| 1645.0 | 1.1089309e7 | Ibn_al-Haytham | Al-Ḥajjāj_ibn_Yūsuf_ibn_Maṭar |
| 1645.0 | 1271962.0 | Ibn_al-Haytham | Billiard_table |
| 1645.0 | 1.1828715e7 | Ibn_al-Haytham | Book_of_Optics |
| 1645.0 | 5286621.0 | Ibn_al-Haytham | Ephraim_ibn_al-Za'faran |
| 1645.0 | 3.0864628e7 | Ibn_al-Haytham | European_science_in_the_Middle_Ages |
| 1645.0 | 719601.0 | Ibn_al-Haytham | MIT_Press |
| 1645.0 | 1.3692155e7 | Ibn_al-Haytham | Philosophy |
| 1645.0 | 3.5216988e7 | Ibn_al-Haytham | Rajab_Ali_Tabrizi |
| 1645.0 | 39420.0 | Ibn_al-Haytham | Right_triangle |
| 1645.0 | 2538627.0 | Ibn_al-Haytham | Yusuf_al-Mu'taman_ibn_Hud |
| 1645.0 | 1189485.0 | Ibn_al-Haytham | Abu_al-Wafa'_Buzjani |
| 1645.0 | 1.0879533e7 | Ibn_al-Haytham | Aja'ib_al-Makhluqat |
| 1645.0 | 355643.0 | Ibn_al-Haytham | Al-Andalus |
| 1645.0 | 4.3402124e7 | Ibn_al-Haytham | Commentary_on_Anatomy_in_Avicenna's_Canon |
| 1645.0 | 8451005.0 | Ibn_al-Haytham | Haji_Bayram_Veli |
| 1645.0 | 1.0082768e7 | Ibn_al-Haytham | Hockney–Falco_thesis |
| 1645.0 | 2.6634144e7 | Ibn_al-Haytham | Ibn_Sina_Academy_of_Medieval_Medicine_and_Sciences |
| 1645.0 | 1741520.0 | Ibn_al-Haytham | Kamāl_al-Dīn_al-Fārisī |
| 1645.0 | 3.2078146e7 | Ibn_al-Haytham | Muhammad_ibn_Aslam_Al-Ghafiqi |
| 1645.0 | 439770.0 | Ibn_al-Haytham | Abu_Nasr_Mansur |
| 1645.0 | 4158200.0 | Ibn_al-Haytham | Asabiyyah |
| 1645.0 | 236674.0 | Ibn_al-Haytham | Ayurveda |
| 1645.0 | 380406.0 | Ibn_al-Haytham | Comparative_psychology |
| 1645.0 | 13450.0 | Ibn_al-Haytham | Hebrew_language |
| 1645.0 | 8066479.0 | Ibn_al-Haytham | Maslaha |
| 1645.0 | 2042612.0 | Ibn_al-Haytham | Masʽud_ibn_Muhammad_Sijzi |
| 1645.0 | 19323.0 | Ibn_al-Haytham | Middle_East |
| 1645.0 | 53497.0 | Ibn_al-Haytham | Optical_illusion |
| 1645.0 | 1.6926318e7 | Ibn_al-Haytham | Equatorial_ring |
| 1645.0 | 6330034.0 | Ibn_al-Haytham | Eutychius_of_Alexandria |
| 1645.0 | 12558.0 | Ibn_al-Haytham | Galaxy |
| 1645.0 | 2.1854422e7 | Ibn_al-Haytham | Lune_of_Hippocrates |
| 1645.0 | 1.0827654e7 | Ibn_al-Haytham | Mir_Fendereski |
| 1645.0 | 20545.0 | Ibn_al-Haytham | Mirror |
| 1645.0 | 1.5309628e7 | Ibn_al-Haytham | Muhammad_Ali_Astarabadi |
| 1645.0 | 48334.0 | Ibn_al-Haytham | Retina |
| 1645.0 | 3450546.0 | Ibn_al-Haytham | Sufi_metaphysics |
| 1645.0 | 2.1691772e7 | Ibn_al-Haytham | Yahya_ibn_Sarafyun |
| 1645.0 | 1560514.0 | Ibn_al-Haytham | Ahmad_ibn_Yusuf |
| 1645.0 | 8230922.0 | Ibn_al-Haytham | Hamid_al-Din_al-Kirmani |
| 1645.0 | 6785051.0 | Ibn_al-Haytham | History_of_trigonometry |
| 1645.0 | 5.7151342e7 | Ibn_al-Haytham | Ibn_Ishaq_al-Tunisi |
| 1645.0 | 1.5515167e7 | Ibn_al-Haytham | Ibn_al-Kattani |
| 1645.0 | 1830000.0 | Ibn_al-Haytham | Inundation |
| 1645.0 | 3035257.0 | Ibn_al-Haytham | Masarjawaih |
| 1645.0 | 6.4652504e7 | Ibn_al-Haytham | Zaynab_al-Awadiya |
| 1645.0 | 272065.0 | Ibn_al-Haytham | Al-Kindi |
| 1645.0 | 2.2509814e7 | Ibn_al-Haytham | Al-Qifti |
| 1645.0 | 6.5419249e7 | Ibn_al-Haytham | Ali_ibn_Khalaf |
| 1645.0 | 283120.0 | Ibn_al-Haytham | American_Philosophical_Society |
| 1645.0 | 47836.0 | Ibn_al-Haytham | Averroes |
| 1645.0 | 8425211.0 | Ibn_al-Haytham | Dictionary_of_Scientific_Biography |
| 1645.0 | 3143150.0 | Ibn_al-Haytham | History_of_scientific_method |
| 1645.0 | 6.5706161e7 | Ibn_al-Haytham | Ibn_Abi_Usaibia |
| 1645.0 | 2.7578277e7 | Ibn_al-Haytham | Ibn_al-Kammad |
| 1645.0 | 464693.0 | Ibn_al-Haytham | Mathworld |
| 1645.0 | 144553.0 | Ibn_al-Haytham | Projectile |
| 1645.0 | 3871014.0 | Ibn_al-Haytham | Rainbow |
| 1645.0 | 1.4835428e7 | Ibn_al-Haytham | Temporal_finitism |
| 1645.0 | 3.5861222e7 | Ibn_al-Haytham | Abū_al‐ʿUqūl |
| 1645.0 | 274751.0 | Ibn_al-Haytham | Al-Azhar_University |
| 1645.0 | 803.0 | Ibn_al-Haytham | Arabic |
| 1645.0 | 5839243.0 | Ibn_al-Haytham | Flooding_of_the_Nile |
| 1645.0 | 3013733.0 | Ibn_al-Haytham | Ibn_Abi_Usaybi'a |
| 1645.0 | 2.0407945e7 | Ibn_al-Haytham | Ibn_al-Wafid |
| 1645.0 | 2589714.0 | Ibn_al-Haytham | Milky_Way |
| 1645.0 | 1972777.0 | Ibn_al-Haytham | Neil_deGrasse_Tyson |
| 1645.0 | 1.0712582e7 | Ibn_al-Haytham | Sinan_ibn_Thabit |
| 1645.0 | 175040.0 | Ibn_al-Haytham | Al-Farabi |
| 1645.0 | 2.2341957e7 | Ibn_al-Haytham | Ali_al-Ridha |
| 1645.0 | 1778258.0 | Ibn_al-Haytham | Alī_ibn_Ahmad_al-Nasawī |
| 1645.0 | 4831143.0 | Ibn_al-Haytham | Ancient_Greek_medicine |
| 1645.0 | 157898.0 | Ibn_al-Haytham | Eye |
| 1645.0 | 1782358.0 | Ibn_al-Haytham | Ibn_Abi_Sadiq |
| 1645.0 | 6328738.0 | Ibn_al-Haytham | Ibn_Mu'adh_al-Jayyani |
| 1645.0 | 5.6793125e7 | Ibn_al-Haytham | Ibn_al‐Raqqam |
| 1645.0 | 23231.0 | Ibn_al-Haytham | Parabola |
| 1645.0 | 7211548.0 | Ibn_al-Haytham | Predestination_in_Islam |
| 1645.0 | 44328.0 | Ibn_al-Haytham | Ulugh_Beg |
| 1645.0 | 2.3538754e7 | Ibn_al-Haytham | Wayback_Machine |
| 1645.0 | 1740825.0 | Ibn_al-Haytham | Athir_al-Din_al-Abhari |
| 1645.0 | 536739.0 | Ibn_al-Haytham | Avempace |
| 1645.0 | 577287.0 | Ibn_al-Haytham | Buyid_dynasty |
| 1645.0 | 92550.0 | Ibn_al-Haytham | Omar_Khayyam |
| 1645.0 | 1.854116e7 | Ibn_al-Haytham | Abū_Rayhān_al-Bīrūnī |
| 1645.0 | 804218.0 | Ibn_al-Haytham | Astronomical_clock |
| 1645.0 | 6.3434964e7 | Ibn_al-Haytham | CiteSeerX_(identifier) |
| 1645.0 | 316410.0 | Ibn_al-Haytham | Compass_rose |
| 1645.0 | 9417.0 | Ibn_al-Haytham | Euclidean_geometry |
| 1645.0 | 8232680.0 | Ibn_al-Haytham | Ibn_al-Jazzar |
| 1645.0 | 2742403.0 | Ibn_al-Haytham | Mathematical_Association |
| 1645.0 | 3.1327881e7 | Ibn_al-Haytham | Na'im_ibn_Musa |
| 1645.0 | 25948.0 | Ibn_al-Haytham | Refraction |
| 1645.0 | 1766960.0 | Ibn_al-Haytham | Abu_al-Hasan_al-Tabari |
| 1645.0 | 3393371.0 | Ibn_al-Haytham | Hindu–Arabic_numeral_system |
| 1645.0 | 1162119.0 | Ibn_al-Haytham | Mohammed_Arkoun |
| 1645.0 | 1099413.0 | Ibn_al-Haytham | Orbital_eccentricity |
| 1645.0 | 1015358.0 | Ibn_al-Haytham | Vitello |
| 1645.0 | 2041982.0 | Ibn_al-Haytham | Al-Shahrazuri |
| 1645.0 | 1720221.0 | Ibn_al-Haytham | Alfonsine_tables |
| 1645.0 | 1.1048781e7 | Ibn_al-Haytham | Charles_M._Falco |
| 1645.0 | 6.3562501e7 | Ibn_al-Haytham | Hossein_Nasr |
| 1645.0 | 17902.0 | Ibn_al-Haytham | Leonhard_Euler |
| 1645.0 | 7.1598945e7 | Ibn_al-Haytham | National_Library_of_the_Argentine_Republic |
| 1645.0 | 23670.0 | Ibn_al-Haytham | Perfect_number |
| 1645.0 | 1.5927465e7 | Ibn_al-Haytham | The_Remaining_Signs_of_Past_Centuries |
| 1645.0 | 1461001.0 | Ibn_al-Haytham | University_of_al-Qarawiyyin |
| 1645.0 | 3.4781942e7 | Ibn_al-Haytham | Abd_al‐Wajid |
| 1645.0 | 1766622.0 | Ibn_al-Haytham | Abolfadl_Harawi |
| 1645.0 | 6.082025e7 | Ibn_al-Haytham | Al-Hawi |
| 1645.0 | 665027.0 | Ibn_al-Haytham | Fourth_power |
| 1645.0 | 2.3568467e7 | Ibn_al-Haytham | Rashidun_al-Suri |
| 1645.0 | 4647532.0 | Ibn_al-Haytham | Shams_al-Din_Abu_Abd_Allah_al-Khalili |
| 1645.0 | 2.4712247e7 | Ibn_al-Haytham | Ya'ish_ibn_Ibrahim_al-Umawi |
| 1645.0 | 5.5495903e7 | Ibn_al-Haytham | 1001_Inventions |
| 1645.0 | 1.0730931e7 | Ibn_al-Haytham | Al-Mu'taman_ibn_Hud |
| 1645.0 | 1174529.0 | Ibn_al-Haytham | Al-Tasrif |
| 1645.0 | 4.3350725e7 | Ibn_al-Haytham | Euclid–Euler_theorem |
| 1645.0 | 360726.0 | Ibn_al-Haytham | Planisphere |
| 1645.0 | 6.0782023e7 | Ibn_al-Haytham | Shmuel_Sambursky |
| 1645.0 | 2.1800807e7 | Ibn_al-Haytham | Zakhireye_Khwarazmshahi |
| 1645.0 | 1.6424083e7 | Ibn_al-Haytham | 59239_Alhazen |
| 1645.0 | 5089990.0 | Ibn_al-Haytham | Ahmad_al-Buni |
| 1645.0 | 482939.0 | Ibn_al-Haytham | Al-Hakim_bi-Amr_Allah |
| 1645.0 | 2.7361247e7 | Ibn_al-Haytham | Al-Kharaqī |
| 1645.0 | 4.9712277e7 | Ibn_al-Haytham | Al-Ruhawi |
| 1645.0 | 5.6447306e7 | Ibn_al-Haytham | Alam_al-Din_al-Hanafi |
| 1645.0 | 51518.0 | Ibn_al-Haytham | Dam |
| 1645.0 | 1602822.0 | Ibn_al-Haytham | Haji_Bektash_Veli |
| 1645.0 | 1467830.0 | Ibn_al-Haytham | Ibn_Hazm |
| 1645.0 | 14810.0 | Ibn_al-Haytham | Islamic_calendar |
| 1645.0 | 1.3861753e7 | Ibn_al-Haytham | Said_al-Andalusi |
| 1645.0 | 1917134.0 | Ibn_al-Haytham | Sultan_Ali_Khorasani |
| 1645.0 | 30503.0 | Ibn_al-Haytham | Theology |
| 1645.0 | 2674.0 | Ibn_al-Haytham | Abd_al-Latif_al-Baghdadi |
| 1645.0 | 4849234.0 | Ibn_al-Haytham | Encyclopedia_of_the_Brethren_of_Purity |
| 1645.0 | 9239.0 | Ibn_al-Haytham | Europe |
| 1645.0 | 150257.0 | Ibn_al-Haytham | Feigned_madness |
| 1645.0 | 577201.0 | Ibn_al-Haytham | Frithjof_Schuon |
| 1645.0 | 2.6482067e7 | Ibn_al-Haytham | Kepler |
| 1645.0 | 17730.0 | Ibn_al-Haytham | Latin |
| 1645.0 | 145845.0 | Ibn_al-Haytham | Paraboloid |
| 1645.0 | 25525.0 | Ibn_al-Haytham | René_Descartes |
| 1645.0 | 2042576.0 | Ibn_al-Haytham | Amin_al-Din_Rashid_al-Din_Vatvat |
| 1645.0 | 361897.0 | Ibn_al-Haytham | Astrophysics |
| 1645.0 | 1.3083487e7 | Ibn_al-Haytham | Baha'_al-din_al-'Amili |
| 1645.0 | 17939.0 | Ibn_al-Haytham | Light |
| 1645.0 | 1731800.0 | Ibn_al-Haytham | Triquetrum_(astronomy) |
| 1645.0 | 5.3082933e7 | Ibn_al-Haytham | Abu_al-Hasan_al-Ahwazi |
| 1645.0 | 7718539.0 | Ibn_al-Haytham | Al-'Adudi_Hospital |
| 1645.0 | 3.1076646e7 | Ibn_al-Haytham | Al_Achsasi_al_Mouakket |
| 1645.0 | 1.0923902e7 | Ibn_al-Haytham | Dream_Pool_Essays |
| 1645.0 | 3467826.0 | Ibn_al-Haytham | House_of_Knowledge |
| 1645.0 | 1.327905e7 | Ibn_al-Haytham | Ibn_Butlan |
| 1645.0 | 5741464.0 | Ibn_al-Haytham | Ibn_al-Baytar |
| 1645.0 | 685895.0 | Ibn_al-Haytham | René_Guénon |
| 1645.0 | 2.3477491e7 | Ibn_al-Haytham | Sadr_al-Din_al-Qunawi |
| 1645.0 | 1768580.0 | Ibn_al-Haytham | Sharaf_al-Din_al-Tusi |
| 1645.0 | 2.4703916e7 | Ibn_al-Haytham | Sullam_al-sama' |
| 1645.0 | 1245987.0 | Ibn_al-Haytham | Ziauddin_Sardar |
| 1645.0 | 7.1370184e7 | Ibn_al-Haytham | Ali_ibn_Yusuf_al-Ilaqi |
| 1645.0 | 102182.0 | Ibn_al-Haytham | Celestial_mechanics |
| 1645.0 | 2695116.0 | Ibn_al-Haytham | Contemporary_Islamic_philosophy |
| 1645.0 | 6733941.0 | Ibn_al-Haytham | Friedrich_Risner |
| 1645.0 | 12326.0 | Ibn_al-Haytham | Galen |
| 1645.0 | 1232660.0 | Ibn_al-Haytham | Syed_Muhammad_Naquib_al-Attas |
| 1645.0 | 5676618.0 | Ibn_al-Haytham | Al-Mawrid |
| 1645.0 | 1130.0 | Ibn_al-Haytham | Avicenna |
| 1645.0 | 1.4918546e7 | Ibn_al-Haytham | Avicennism |
| 1645.0 | 4536514.0 | Ibn_al-Haytham | Babylonian_astronomy |
| 1645.0 | 432591.0 | Ibn_al-Haytham | CNRS |
| 1645.0 | 1.5820227e7 | Ibn_al-Haytham | History_of_Science_and_Technology_in_China |
| 1645.0 | 2041938.0 | Ibn_al-Haytham | Mansur_ibn_Ilyas |
| 1645.0 | 2412780.0 | Ibn_al-Haytham | Sic |
| 1645.0 | 8768926.0 | Ibn_al-Haytham | Aguilonius |
| 1645.0 | 8284152.0 | Ibn_al-Haytham | Bimaristan |
| 1645.0 | 6293.0 | Ibn_al-Haytham | Cairo |
| 1645.0 | 326595.0 | Ibn_al-Haytham | Fakhr_al-Din_al-Razi |
| 1645.0 | 2.1573591e7 | Ibn_al-Haytham | Islamic_geometric_patterns |
| 1645.0 | 389418.0 | Ibn_al-Haytham | John_Pecham |
| 1645.0 | 4.5387547e7 | Ibn_al-Haytham | Physics_in_medieval_Islam |
| 1645.0 | 2226012.0 | Ibn_al-Haytham | Shambhala_Publications |
| 1645.0 | 2.755431e7 | Ibn_al-Haytham | Abu_Jafar_ibn_Harun_al-Turjali |
| 1645.0 | 353215.0 | Ibn_al-Haytham | Al-Zahrawi |
| 1645.0 | 39316.0 | Ibn_al-Haytham | Compass |
| 1645.0 | 241528.0 | Ibn_al-Haytham | Jacob_Bronowski |
| 1645.0 | 3.9127918e7 | Ibn_al-Haytham | Mohammed_ibn_Abdun_al-Jabali |
| 1645.0 | 204511.0 | Ibn_al-Haytham | Scientific_skepticism |
| 1645.0 | 5.4447016e7 | Ibn_al-Haytham | Victor_J._Katz |
| 1645.0 | 2.3442952e7 | Ibn_al-Haytham | Yang_Guangxian |
| 1645.0 | 1019879.0 | Ibn_al-Haytham | Alhazen_(crater) |
| 1645.0 | 257242.0 | Ibn_al-Haytham | Apollonius_of_Perga |
| 1645.0 | 57580.0 | Ibn_al-Haytham | Basra |
| 1645.0 | 2.4893445e7 | Ibn_al-Haytham | Book_of_the_Ten_Treatises_of_the_Eye |
| 1645.0 | 143608.0 | Ibn_al-Haytham | Deferent_and_epicycle |
| 1645.0 | 5.5808289e7 | Ibn_al-Haytham | Janus_(journal) |
| 1645.0 | 1.0780372e7 | Ibn_al-Haytham | Muhammad_Baqir_Yazdi |
| 1645.0 | 1766702.0 | Ibn_al-Haytham | Nazif_ibn_Yumn |
| 1645.0 | 4.8253059e7 | Ibn_al-Haytham | Salat |
| 1645.0 | 1696685.0 | Ibn_al-Haytham | Tacuinum_Sanitatis |
| 1645.0 | 3861353.0 | Ibn_al-Haytham | Babylonian_mathematics |
| 1645.0 | 1.7365905e7 | Ibn_al-Haytham | Dawūd_al-Qayṣarī |
| 1645.0 | 6.0347068e7 | Ibn_al-Haytham | Jalaladdin_Davani |
| 1645.0 | 18836.0 | Ibn_al-Haytham | Middle_Ages |
| 1645.0 | 3022453.0 | Ibn_al-Haytham | Nafs |
| 1645.0 | 5186903.0 | Ibn_al-Haytham | Tusi_couple |
| 1645.0 | 2428.0 | Ibn_al-Haytham | Analog_computer |
| 1645.0 | 1.1453823e7 | Ibn_al-Haytham | Byzantine_science |
| 1645.0 | 6.7975278e7 | Ibn_al-Haytham | History_of_science_in_the_Renaissance |
| 1645.0 | 166162.0 | Ibn_al-Haytham | Islamic_philosophy |
| 1645.0 | 645208.0 | Ibn_al-Haytham | Equant |
| 1645.0 | 7.1175005e7 | Ibn_al-Haytham | Mahmud_Hudayi |
| 1645.0 | 1.5233821e7 | Ibn_al-Haytham | Psychology_in_the_medieval_Islamic_world |
| 1645.0 | 2.1691805e7 | Ibn_al-Haytham | Serapion_the_Younger |
| 1645.0 | 7627.0 | Ibn_al-Haytham | The_Canterbury_Tales |
| 1645.0 | 5.549544e7 | Ibn_al-Haytham | Alhazen_(disambiguation) |
| 1645.0 | 2.4464339e7 | Ibn_al-Haytham | Arab |
| 1645.0 | 2.8700369e7 | Ibn_al-Haytham | Ibn_Ghazi_al-Miknasi |
| 1645.0 | 199169.0 | Ibn_al-Haytham | Ibn_Khaldun |
| 1645.0 | 1.9018638e7 | Ibn_al-Haytham | Islamic_mathematics |
| 1645.0 | 18079.0 | Ibn_al-Haytham | Leonardo_da_Vinci |
| 1645.0 | 2.7405151e7 | Ibn_al-Haytham | Muhammad_al-Rudani |
| 1645.0 | 6.7427596e7 | Ibn_al-Haytham | Qadi_Mir_Husayn_al-Maybudi |
| 1645.0 | 4.0311818e7 | Ibn_al-Haytham | Roger_Highfield |
| 1645.0 | 207174.0 | Ibn_al-Haytham | Triangulation |
| 1645.0 | 2.7579858e7 | Ibn_al-Haytham | Abu_al-Salt |
| 1645.0 | 3.5777337e7 | Ibn_al-Haytham | Cosmos:_A_Spacetime_Odyssey |
| 1645.0 | 4512160.0 | Ibn_al-Haytham | Flooding |
| 1645.0 | 1.4973076e7 | Ibn_al-Haytham | Medical_Renaissance |
| 1645.0 | 6.1415405e7 | Ibn_al-Haytham | Muhammad_Husayn_Tabataba'i |
| 1645.0 | 1.6593123e7 | Ibn_al-Haytham | Nader_El-Bizri |
| 1645.0 | 5290740.0 | Ibn_al-Haytham | Sa'ad_al-Dawla |
| 1645.0 | 982540.0 | Ibn_al-Haytham | Taqi_ad-Din_Muhammad_ibn_Ma'ruf |
| 1645.0 | 928.0 | Ibn_al-Haytham | Axiom |
| 1645.0 | 3.833419e7 | Ibn_al-Haytham | Bruges |
| 1645.0 | 11114.0 | Ibn_al-Haytham | Fiqh |
| 1645.0 | 2.9688374e7 | Ibn_al-Haytham | Galileo_Galilei |
| 1645.0 | 2618724.0 | Ibn_al-Haytham | John_L._Esposito |
| 1645.0 | 1.8426568e7 | Ibn_al-Haytham | NASA |
| 1645.0 | 5.7398059e7 | Ibn_al-Haytham | Najm_al‐Din_al‐Misri |
| 1645.0 | 5.1317367e7 | Ibn_al-Haytham | Nastulus |
| 1645.0 | 202444.0 | Ibn_al-Haytham | Ummah |
| 1645.0 | 2.8820168e7 | Ibn_al-Haytham | Abd_al-Rahman_al-Jadiri |
| 1645.0 | 1767004.0 | Ibn_al-Haytham | Abu_Mansur_Muwaffaq |
| 1645.0 | 1.9217647e7 | Ibn_al-Haytham | Abul_Qasim_ibn_Mohammed_al-Ghassani |
| 1645.0 | 1741220.0 | Ibn_al-Haytham | Bukhtishu |
| 1645.0 | 3.2142292e7 | Ibn_al-Haytham | Ibrahim_ibn_Baks |
| 1645.0 | 1782585.0 | Ibn_al-Haytham | Jabril_ibn_Bukhtishu |
| 1645.0 | 2527706.0 | Ibn_al-Haytham | Mir_Damad |
| 1645.0 | 2984836.0 | Ibn_al-Haytham | Ophthalmology_in_the_medieval_Islamic_world |
| 1645.0 | 22308.0 | Ibn_al-Haytham | Oxford |
| 1645.0 | 50585.0 | Ibn_al-Haytham | Philadelphia |
| 1645.0 | 1.9883086e7 | Ibn_al-Haytham | Philip_Sherrard |
| 1645.0 | 230250.0 | Ibn_al-Haytham | The_Ascent_of_Man |
| 1645.0 | 1964954.0 | Ibn_al-Haytham | University_of_Chicago_Press |
| 1645.0 | 262757.0 | Ibn_al-Haytham | Abd_al-Rahman_al-Sufi |
| 1645.0 | 2.8622697e7 | Ibn_al-Haytham | Ahmad_ibn_Munim_al-Abdari |
| 1645.0 | 86822.0 | Ibn_al-Haytham | Ali_ibn_Isa_al-Asturlabi |
| 1645.0 | 2.8977437e7 | Ibn_al-Haytham | Fouad_Zakariyya |
| 1645.0 | 5.3090488e7 | Ibn_al-Haytham | Haseb-i_Tabari |
| 1645.0 | 3.6545044e7 | Ibn_al-Haytham | Ibn_Jumayʿ |
| 1645.0 | 9001042.0 | Ibn_al-Haytham | Islamic_ethics |
| 1645.0 | 18957.0 | Ibn_al-Haytham | Medicine |
| 1645.0 | 5.4421139e7 | Ibn_al-Haytham | Muhammad_al-Baghdadi |
| 1645.0 | 63098.0 | Ibn_al-Haytham | Optic_chiasm |
| 1645.0 | 2.8005345e7 | Ibn_al-Haytham | Sadid_al-Din_al-Kazaruni |
| 1645.0 | 28246.0 | Ibn_al-Haytham | Sufism |
| 1645.0 | 3.2309672e7 | Ibn_al-Haytham | Abd_al-Razzaq_Lahiji |
| 1645.0 | 192230.0 | Ibn_al-Haytham | Almanac |
| 1645.0 | 2426527.0 | Ibn_al-Haytham | Ibn_al-Nafis |
| 1645.0 | 1.3433019e7 | Ibn_al-Haytham | Intromission_theory |
| 1645.0 | 1.1011952e7 | Ibn_al-Haytham | Kamal_al-Din_al-Farisi |
| 1645.0 | 94721.0 | Ibn_al-Haytham | Robert_Grosseteste |
| 1645.0 | 5290954.0 | Ibn_al-Haytham | Abu_al-Bayan_ibn_al-Mudawwar |
| 1645.0 | 2045119.0 | Ibn_al-Haytham | Abu_al-Hakam_al-Kirmani |
| 1645.0 | 2.8208073e7 | Ibn_al-Haytham | Afdal_al-Din_Kashani |
| 1645.0 | 2643686.0 | Ibn_al-Haytham | Aga_Khan_University |
| 1645.0 | 174410.0 | Ibn_al-Haytham | Armillary_sphere |
| 1645.0 | 383129.0 | Ibn_al-Haytham | Celestial_spheres |
| 1645.0 | 48167.0 | Ibn_al-Haytham | Congruence_relation |
| 1645.0 | 244588.0 | Ibn_al-Haytham | Heliocentrism |
| 1645.0 | 4.7787936e7 | Ibn_al-Haytham | Schema_for_horizontal_dials |
| 1645.0 | 1.3224789e7 | Ibn_al-Haytham | Sextant_(astronomy) |
| 1645.0 | 6.6426206e7 | Ibn_al-Haytham | American_Mathematical_Monthly |
| 1645.0 | 6012554.0 | Ibn_al-Haytham | Cosmology_in_medieval_Islam |
| 1645.0 | 3263095.0 | Ibn_al-Haytham | Ehmedê_Xanî |
| 1645.0 | 1002657.0 | Ibn_al-Haytham | Nasir_Khusraw |
| 1645.0 | 1782879.0 | Ibn_al-Haytham | Shapur_ibn_Sahl |
| 1645.0 | 6.8869871e7 | Ibn_al-Haytham | Ahi_Evren |
| 1645.0 | 172394.0 | Ibn_al-Haytham | Georg_von_Peuerbach |
| 1645.0 | 294211.0 | Ibn_al-Haytham | Globe |
| 1645.0 | 3302534.0 | Ibn_al-Haytham | List_of_Muslim_philosophers |
| 1645.0 | 1741105.0 | Ibn_al-Haytham | Muḥammad_ibn_Ibrāhīm_al-Fazārī |
| 1645.0 | 985414.0 | Ibn_al-Haytham | Nasir_al-Din_Nasir_Hunzai |
| 1645.0 | 6.3434832e7 | Ibn_al-Haytham | PMC_(identifier) |
| 1645.0 | 16433.0 | Ibn_al-Haytham | Rumi |
| 1645.0 | 1840548.0 | Ibn_al-Haytham | Zayn-e-Attar |
| 1645.0 | 2.2848684e7 | Ibn_al-Haytham | Abu_Sulayman_Sijistani |
| 1645.0 | 2.1508913e7 | Ibn_al-Haytham | Abu_ul-Ala_Shirazi |
| 1645.0 | 3.107765e7 | Ibn_al-Haytham | G._J._Toomer |
| 1645.0 | 209717.0 | Ibn_al-Haytham | Madrasa |
| 1645.0 | 3304216.0 | Ibn_al-Haytham | Mathematics_in_the_medieval_Islamic_world |
| 1645.0 | 251713.0 | Ibn_al-Haytham | Qibla |
| 1645.0 | 25532.0 | Ibn_al-Haytham | Renaissance |
| 1645.0 | 2042154.0 | Ibn_al-Haytham | Shaykh_Muhammad_ibn_Thaleb |
| 1645.0 | 3225840.0 | Ibn_al-Haytham | Sublunary_sphere |
| 1645.0 | 2.757938e7 | Ibn_al-Haytham | Ibn_al-Saffar |
| 1645.0 | 3.3642424e7 | Ibn_al-Haytham | Nasir_al-Din_al-Tusi |
| 1645.0 | 1979016.0 | Ibn_al-Haytham | Routledge_Encyclopedia_of_Philosophy |
| 1645.0 | 5.2932896e7 | Ibn_al-Haytham | Abd_al-Latif_al-Baghdadi_(medieval_writer) |
| 1645.0 | 1134.0 | Ibn_al-Haytham | Analysis |
| 1645.0 | 6.9094489e7 | Ibn_al-Haytham | Fakhr_al-Din_al-Akhlati |
| 1645.0 | 3.2144014e7 | Ibn_al-Haytham | Ibn_Hamza_al-Maghribi |
| 1645.0 | 272074.0 | Ibn_al-Haytham | Ibn_Taymiyyah |
| 1645.0 | 14533.0 | Ibn_al-Haytham | India |
| 1645.0 | 5.613996e7 | Ibn_al-Haytham | Khoja_Akhmet_Yassawi |
| 1645.0 | 24714.0 | Ibn_al-Haytham | Precession |
| 1645.0 | 23979.0 | Ibn_al-Haytham | Ptolemy |
| 1645.0 | 1102000.0 | Ibn_al-Haytham | Shen_Kuo |
| 1645.0 | 207547.0 | Ibn_al-Haytham | Thābit_ibn_Qurra |
| 1645.0 | 78209.0 | Ibn_al-Haytham | Abu_Bakr_al-Razi |
| 1645.0 | 1822259.0 | Ibn_al-Haytham | Hakim-e-Gilani |
| 1645.0 | 1.0228966e7 | Ibn_al-Haytham | Jabir_ibn_Aflah |
| 1645.0 | 3335321.0 | Ibn_al-Haytham | Shams_al-Din_al-Samarqandi |
| 1645.0 | 195520.0 | Ibn_al-Haytham | Civil_engineer |
| 1645.0 | 244107.0 | Ibn_al-Haytham | Euclid's_Elements |
| 1645.0 | 15532.0 | Ibn_al-Haytham | Integral |
| 1645.0 | 3.7477763e7 | Ibn_al-Haytham | Islamic_Golden_Age |
| 1645.0 | 7.1778387e7 | Ibn_al-Haytham | Mathematics_in_medieval_Islam |
| 1645.0 | 5.5496341e7 | Ibn_al-Haytham | Medieval_Iraq |
| 1645.0 | 2042394.0 | Ibn_al-Haytham | Miskawayh |
| 1645.0 | 1914053.0 | Ibn_al-Haytham | Mu'ayyad_al-Din_al-Urdi |
| 1645.0 | 1822322.0 | Ibn_al-Haytham | Muhammad_ibn_Mahmud_Amuli |
| 1645.0 | 8477832.0 | Ibn_al-Haytham | Sibt_al-Maridini |
| 1645.0 | 1.2224008e7 | Ibn_al-Haytham | Sufi_psychology |
| 1645.0 | 31880.0 | Ibn_al-Haytham | Universe |
| 1645.0 | 305136.0 | Ibn_al-Haytham | Visual_system |
| 1645.0 | 5.515162e7 | Ibn_al-Haytham | ISSN_(identifier) |
| 1645.0 | 1.7140872e7 | Ibn_al-Haytham | Ibn_Shuayb |
| 1645.0 | 6.3434916e7 | Ibn_al-Haytham | OCLC_(identifier) |
| 1645.0 | 3.1526932e7 | Ibn_al-Haytham | Ya'qub_ibn_Ishaq_al-Israili |
| 1645.0 | 5962454.0 | Ibn_al-Haytham | Zij-i_Sultani |
| 1645.0 | 365397.0 | Ibn_al-Haytham | Clarendon_Press |
| 1645.0 | 9247.0 | Ibn_al-Haytham | Epistemology |
| 1645.0 | 2603901.0 | Ibn_al-Haytham | Mostafa_Malekian |
| 1645.0 | 3.3731493e7 | Ibn_al-Haytham | Parallel_postulate |
| 1645.0 | 2.6499076e7 | Ibn_al-Haytham | Rufaida_Al-Aslamia |
| 1645.0 | 1.2654431e7 | Ibn_al-Haytham | Al-Birjandi |
| 1645.0 | 1.9008673e7 | Ibn_al-Haytham | Conic_section |
| 1645.0 | 14220.0 | Ibn_al-Haytham | History_of_mathematics |
| 1645.0 | 1.1410402e7 | Ibn_al-Haytham | Joseph_ben_Judah_of_Ceuta |
| 1645.0 | 1.5077184e7 | Ibn_al-Haytham | Peace_in_Islamic_philosophy |
| 1645.0 | 822045.0 | Ibn_al-Haytham | Qiyas |
| 1645.0 | 427971.0 | Ibn_al-Haytham | Specific_gravity |
| 1645.0 | 5453536.0 | Ibn_al-Haytham | Zij-i_Ilkhani |
| 1645.0 | 3663691.0 | Ibn_al-Haytham | Hering's_law_of_equal_innervation |
| 1645.0 | 4.069588e7 | Ibn_al-Haytham | Ibn_al-Adami |
| 1645.0 | 25879.0 | Ibn_al-Haytham | Roger_Bacon |
| 1645.0 | 6088.0 | Ibn_al-Haytham | Common_Era |
| 1645.0 | 2611765.0 | Ibn_al-Haytham | Henry_Corbin |
| 1645.0 | 3.9959331e7 | Ibn_al-Haytham | Ibn_al-Akfani |
| 1645.0 | 1.0409314e7 | Ibn_al-Haytham | List_of_Arabic_star_names |
| 1645.0 | 1.1468771e7 | Ibn_al-Haytham | Qāḍī_Zāda_al-Rūmī |
| 1645.0 | 3.3668326e7 | Ibn_al-Haytham | Semnan_(city) |
| 1645.0 | 4.3756445e7 | Ibn_al-Haytham | Al-Isfizari |
| 1645.0 | 2.3817094e7 | Ibn_al-Haytham | Bahmanyār |
| 1645.0 | 6886.0 | Ibn_al-Haytham | Chicago |
| 1645.0 | 6220.0 | Ibn_al-Haytham | Circle |
| 1645.0 | 3655571.0 | Ibn_al-Haytham | Eastern_Arabic_numerals |
| 1645.0 | 607777.0 | Ibn_al-Haytham | Epicycles |
| 1645.0 | 1840730.0 | Ibn_al-Haytham | Muhammad_ibn_Yusuf_al-Harawi |
| 1645.0 | 5.262552e7 | Ibn_al-Haytham | Nomanul_Haq |
| 1645.0 | 2848164.0 | Ibn_al-Haytham | Nur_ad-Din_al-Bitruji |
| 1645.0 | 983450.0 | Ibn_al-Haytham | Traditionalist_School_(perennialism) |
| 1645.0 | 1086231.0 | Ibn_al-Haytham | Al-Abbās_ibn_Said_al-Jawharī |
| 1645.0 | 607963.0 | Ibn_al-Haytham | Al-Farghani |
| 1645.0 | 982595.0 | Ibn_al-Haytham | Constantinople_observatory_of_Taqi_ad-Din |
| 1645.0 | 5.5049264e7 | Ibn_al-Haytham | ISBN_(identifier) |
| 1645.0 | 4.5124222e7 | Ibn_al-Haytham | Kitāb_al-Manāẓir |
| 1645.0 | 6548181.0 | Ibn_al-Haytham | Ma_Yize |
| 1645.0 | 6.281435e7 | Ibn_al-Haytham | Muwaqqit |
| 1645.0 | 2042316.0 | Ibn_al-Haytham | Nurbakhshi |
| 1645.0 | 23666.0 | Ibn_al-Haytham | Prime_number |
| 1645.0 | 2.3649689e7 | Ibn_al-Haytham | Shadow_square |
| 1645.0 | 1782185.0 | Ibn_al-Haytham | Zayn_al-Din_Gorgani |
| 1645.0 | 5286542.0 | Ibn_al-Haytham | Abu_Hafsa_Yazid |
| 1645.0 | 2627738.0 | Ibn_al-Haytham | History_of_optics |
| 1645.0 | 165834.0 | Ibn_al-Haytham | Ijtihad |
| 1645.0 | 658084.0 | Ibn_al-Haytham | Magnifying_glass |
| 1645.0 | 2909851.0 | Ibn_al-Haytham | Trepidation |
| 1645.0 | 8656923.0 | Ibn_al-Haytham | Ahmad_Fardid |
| 1645.0 | 4.9107555e7 | Ibn_al-Haytham | Al-Furqan_Islamic_Heritage_Foundation |
| 1645.0 | 5.2173672e7 | Ibn_al-Haytham | Al-Mubashshir_ibn_Fatik |
| 1645.0 | 5.3090036e7 | Ibn_al-Haytham | Al-Wabkanawi |
| 1645.0 | 2375470.0 | Ibn_al-Haytham | Cleomedes |
| 1645.0 | 1627160.0 | Ibn_al-Haytham | Linda_Hall_Library |
| 1645.0 | 1.7944118e7 | Ibn_al-Haytham | Physics_in_the_medieval_Islamic_world |
| 1645.0 | 23313.0 | Ibn_al-Haytham | Piri_Reis |
| 1645.0 | 2014775.0 | Ibn_al-Haytham | Qutb_al-Din_al-Shirazi |
| 1645.0 | 2.1786641e7 | Ibn_al-Haytham | UNESCO |
| 1645.0 | 426368.0 | Ibn_al-Haytham | Abu'l-Hasan_al-Uqlidisi |
| 1645.0 | 1180080.0 | Ibn_al-Haytham | Addison-Wesley |
| 1645.0 | 56176.0 | Ibn_al-Haytham | Fatimid_Caliphate |
| 1645.0 | 5460963.0 | Ibn_al-Haytham | George_Saliba |
| 1645.0 | 3.5575543e7 | Ibn_al-Haytham | Ibn_al-Durayhim |
| 1645.0 | 19445.0 | Ibn_al-Haytham | Maimonides |
| 1645.0 | 8465426.0 | Ibn_al-Haytham | Maragheh_observatory |
| 1645.0 | 4704776.0 | Ibn_al-Haytham | Motilal_Banarsidass |
| 1645.0 | 2.578541e7 | Ibn_al-Haytham | Taha_Abdurrahman |
| 1645.0 | 6.5715159e7 | Ibn_al-Haytham | Trove_(identifier) |
| 1645.0 | 6.2261939e7 | Ibn_al-Haytham | Vizier_(Abbasid_Caliphate) |
| 1645.0 | 414271.0 | Ibn_al-Haytham | Abū_Isḥāq_Ibrāhīm_al-Zarqālī |
| 1645.0 | 4385475.0 | Ibn_al-Haytham | Ancient_Greek_astronomy |
| 1645.0 | 3.6143542e7 | Ibn_al-Haytham | Ibn_al-Majdi |
| 1645.0 | 5496025.0 | Ibn_al-Haytham | Ilm_(Arabic) |
| 1645.0 | 612068.0 | Ibn_al-Haytham | Alfred_Molina |
| 1645.0 | 3.2077839e7 | Ibn_al-Haytham | Ali_ibn_Isa_al-Kahhal |
edges.write.saveAsTable("enwiki_graph_edges")
import org.graphframes.GraphFrame
val vertices = spark.sql("SELECT page_id AS id, page_title, page_len FROM enwiki_page")
val g = GraphFrame(vertices, edges)
val outDegrees = g.outDegrees
display(outDegrees)
| id | outDegree |
|---|---|
| 251.0 | 3.0 |
| 580.0 | 126.0 |
| 737.0 | 1439.0 |
| 808.0 | 909.0 |
| 858.0 | 1.0 |
| 897.0 | 635.0 |
| 1088.0 | 462.0 |
| 1143.0 | 690.0 |
| 1238.0 | 2.0 |
| 1270.0 | 91.0 |
| 1303.0 | 47.0 |
| 1322.0 | 196.0 |
| 1339.0 | 3.0 |
| 1342.0 | 1.0 |
| 1395.0 | 182.0 |
| 1460.0 | 238.0 |
| 1507.0 | 11.0 |
| 1580.0 | 32.0 |
| 1645.0 | 827.0 |
| 1650.0 | 184.0 |
| 1699.0 | 1.0 |
| 1884.0 | 179.0 |
| 1896.0 | 205.0 |
| 1903.0 | 1.0 |
| 1959.0 | 1.0 |
| 1975.0 | 350.0 |
| 1990.0 | 1206.0 |
| 2025.0 | 88.0 |
| 2122.0 | 716.0 |
| 2142.0 | 202.0 |
| 2235.0 | 65.0 |
| 2393.0 | 421.0 |
| 2443.0 | 210.0 |
| 2525.0 | 2.0 |
| 2563.0 | 276.0 |
| 2572.0 | 1.0 |
| 2580.0 | 5.0 |
| 2659.0 | 1.0 |
| 2711.0 | 1.0 |
| 2776.0 | 2.0 |
| 2821.0 | 1.0 |
| 2866.0 | 97.0 |
| 2923.0 | 311.0 |
| 2996.0 | 1.0 |
| 2999.0 | 318.0 |
| 3000.0 | 1.0 |
| 3089.0 | 87.0 |
| 3175.0 | 81.0 |
| 3220.0 | 1.0 |
| 3226.0 | 636.0 |
| 3352.0 | 769.0 |
| 3698.0 | 228.0 |
| 3794.0 | 804.0 |
| 3796.0 | 1.0 |
| 3876.0 | 342.0 |
| 3986.0 | 1596.0 |
| 3997.0 | 537.0 |
| 4078.0 | 706.0 |
| 4101.0 | 166.0 |
| 4158.0 | 96.0 |
| 4186.0 | 1.0 |
| 4190.0 | 12.0 |
| 4219.0 | 9.0 |
| 4364.0 | 366.0 |
| 4391.0 | 1307.0 |
| 4489.0 | 448.0 |
| 4519.0 | 147.0 |
| 4900.0 | 28.0 |
| 4937.0 | 23.0 |
| 5071.0 | 1.0 |
| 5074.0 | 1.0 |
| 5173.0 | 1.0 |
| 5287.0 | 1.0 |
| 5300.0 | 507.0 |
| 5308.0 | 44.0 |
| 5345.0 | 1.0 |
| 5482.0 | 201.0 |
| 5518.0 | 3.0 |
| 5803.0 | 1.0 |
| 6266.0 | 1.0 |
| 6336.0 | 128.0 |
| 6357.0 | 165.0 |
| 6361.0 | 27.0 |
| 6466.0 | 1180.0 |
| 6559.0 | 344.0 |
| 6597.0 | 129.0 |
| 6598.0 | 858.0 |
| 6620.0 | 161.0 |
| 6622.0 | 1.0 |
| 6623.0 | 576.0 |
| 6654.0 | 1338.0 |
| 6773.0 | 732.0 |
| 7066.0 | 53.0 |
| 7098.0 | 5.0 |
| 7120.0 | 320.0 |
| 7253.0 | 20.0 |
| 7387.0 | 694.0 |
| 7417.0 | 1.0 |
| 7530.0 | 27.0 |
| 7554.0 | 477.0 |
| 7644.0 | 1.0 |
| 7833.0 | 71.0 |
| 7850.0 | 83.0 |
| 7880.0 | 1.0 |
| 7993.0 | 10.0 |
| 8086.0 | 11.0 |
| 8105.0 | 3.0 |
| 8222.0 | 723.0 |
| 8389.0 | 537.0 |
| 8407.0 | 251.0 |
| 8592.0 | 577.0 |
| 8650.0 | 94.0 |
| 8743.0 | 494.0 |
| 8779.0 | 601.0 |
| 8803.0 | 1.0 |
| 8911.0 | 3.0 |
| 8924.0 | 3.0 |
| 8928.0 | 1.0 |
| 8932.0 | 14.0 |
| 9071.0 | 19.0 |
| 9182.0 | 1.0 |
| 9383.0 | 115.0 |
| 9454.0 | 176.0 |
| 9465.0 | 3.0 |
| 9946.0 | 208.0 |
| 10081.0 | 440.0 |
| 10121.0 | 4.0 |
| 10462.0 | 185.0 |
| 10468.0 | 174.0 |
| 10623.0 | 853.0 |
| 10703.0 | 144.0 |
| 10745.0 | 3.0 |
| 10768.0 | 1.0 |
| 10798.0 | 58.0 |
| 10862.0 | 826.0 |
| 11025.0 | 1027.0 |
| 11033.0 | 1928.0 |
| 11141.0 | 976.0 |
| 11146.0 | 731.0 |
| 11316.0 | 1.0 |
| 11317.0 | 9.0 |
| 11393.0 | 546.0 |
| 11458.0 | 112.0 |
| 11500.0 | 1.0 |
| 11748.0 | 746.0 |
| 11800.0 | 33.0 |
| 11858.0 | 1.0 |
| 11936.0 | 1.0 |
| 12006.0 | 1.0 |
| 12027.0 | 616.0 |
| 12366.0 | 320.0 |
| 12367.0 | 183.0 |
| 12393.0 | 627.0 |
| 12471.0 | 1849.0 |
| 12611.0 | 129.0 |
| 12626.0 | 1.0 |
| 12799.0 | 553.0 |
| 12998.0 | 119.0 |
| 13009.0 | 1105.0 |
| 13060.0 | 151.0 |
| 13188.0 | 1.0 |
| 13207.0 | 222.0 |
| 13289.0 | 1292.0 |
| 13465.0 | 337.0 |
| 13483.0 | 458.0 |
| 13601.0 | 14.0 |
| 13623.0 | 275.0 |
| 13648.0 | 11.0 |
| 13832.0 | 2.0 |
| 13910.0 | 275.0 |
| 13916.0 | 1.0 |
| 14075.0 | 1.0 |
| 14148.0 | 481.0 |
| 14315.0 | 70.0 |
| 14324.0 | 23.0 |
| 14423.0 | 441.0 |
| 14465.0 | 149.0 |
| 14514.0 | 1.0 |
| 14536.0 | 225.0 |
| 14570.0 | 272.0 |
| 14832.0 | 119.0 |
| 14837.0 | 219.0 |
| 14958.0 | 637.0 |
| 14997.0 | 41.0 |
| 15003.0 | 1.0 |
| 15004.0 | 55.0 |
| 15100.0 | 333.0 |
| 15162.0 | 1.0 |
| 15173.0 | 3.0 |
| 15207.0 | 65.0 |
| 15254.0 | 253.0 |
| 15382.0 | 5.0 |
| 15447.0 | 185.0 |
| 15538.0 | 118.0 |
| 15575.0 | 917.0 |
| 15604.0 | 590.0 |
| 15655.0 | 732.0 |
| 15727.0 | 3.0 |
| 15790.0 | 1122.0 |
| 15846.0 | 1039.0 |
| 15957.0 | 1.0 |
| 15967.0 | 659.0 |
| 16224.0 | 716.0 |
| 16283.0 | 110.0 |
| 16339.0 | 712.0 |
| 16386.0 | 40.0 |
| 16500.0 | 355.0 |
| 16503.0 | 1.0 |
| 16534.0 | 27.0 |
| 16680.0 | 28.0 |
| 16791.0 | 38.0 |
| 16861.0 | 352.0 |
| 16916.0 | 17.0 |
| 16924.0 | 1.0 |
| 17008.0 | 2.0 |
| 17044.0 | 235.0 |
| 17077.0 | 732.0 |
| 17193.0 | 469.0 |
| 17223.0 | 1.0 |
| 17437.0 | 1.0 |
| 17679.0 | 3.0 |
| 17688.0 | 166.0 |
| 17708.0 | 1.0 |
| 17712.0 | 6.0 |
| 17751.0 | 1.0 |
| 17753.0 | 457.0 |
| 17754.0 | 243.0 |
| 17775.0 | 308.0 |
| 17783.0 | 218.0 |
| 17809.0 | 604.0 |
| 17837.0 | 624.0 |
| 18024.0 | 408.0 |
| 18043.0 | 505.0 |
| 18051.0 | 242.0 |
| 18201.0 | 553.0 |
| 18221.0 | 97.0 |
| 18382.0 | 448.0 |
| 18467.0 | 67.0 |
| 18502.0 | 1.0 |
| 18539.0 | 395.0 |
| 18595.0 | 1208.0 |
| 18746.0 | 3.0 |
| 18838.0 | 1208.0 |
| 18866.0 | 539.0 |
| 18884.0 | 68.0 |
| 18902.0 | 145.0 |
| 18944.0 | 1.0 |
| 18956.0 | 305.0 |
| 19021.0 | 625.0 |
| 19023.0 | 133.0 |
| 19079.0 | 482.0 |
| 19131.0 | 266.0 |
| 19161.0 | 217.0 |
| 19165.0 | 191.0 |
| 19200.0 | 525.0 |
| 19204.0 | 167.0 |
| 19206.0 | 366.0 |
| 19325.0 | 1023.0 |
| 19351.0 | 1103.0 |
| 19499.0 | 938.0 |
| 19530.0 | 894.0 |
| 19553.0 | 658.0 |
| 19614.0 | 116.0 |
| 19669.0 | 407.0 |
| 19683.0 | 40.0 |
| 19758.0 | 846.0 |
| 19868.0 | 23.0 |
| 19873.0 | 239.0 |
| 19886.0 | 523.0 |
| 19962.0 | 80.0 |
| 20020.0 | 3.0 |
| 20029.0 | 45.0 |
| 20052.0 | 1.0 |
| 20134.0 | 884.0 |
| 20135.0 | 1.0 |
| 20170.0 | 451.0 |
| 20268.0 | 298.0 |
| 20382.0 | 1.0 |
| 20396.0 | 1314.0 |
| 20398.0 | 202.0 |
| 20425.0 | 2.0 |
| 20473.0 | 1.0 |
| 20481.0 | 438.0 |
| 20497.0 | 96.0 |
| 20506.0 | 1.0 |
| 20596.0 | 179.0 |
| 20624.0 | 18.0 |
| 20683.0 | 242.0 |
| 20735.0 | 241.0 |
| 21058.0 | 500.0 |
| 21116.0 | 2.0 |
| 21220.0 | 21.0 |
| 21296.0 | 219.0 |
| 21394.0 | 65.0 |
| 21558.0 | 19.0 |
| 21700.0 | 1.0 |
| 21761.0 | 1062.0 |
| 21938.0 | 116.0 |
| 22097.0 | 17.0 |
| 22107.0 | 1016.0 |
| 22346.0 | 182.0 |
| 22373.0 | 74.0 |
| 22412.0 | 80.0 |
| 22521.0 | 1.0 |
| 22555.0 | 1063.0 |
| 22609.0 | 51.0 |
| 22684.0 | 27.0 |
| 22951.0 | 77.0 |
| 22990.0 | 77.0 |
| 23015.0 | 354.0 |
| 23086.0 | 1.0 |
| 23136.0 | 1.0 |
| 23144.0 | 6.0 |
| 23193.0 | 183.0 |
| 23267.0 | 1297.0 |
| 23271.0 | 1.0 |
| 23336.0 | 126.0 |
| 23364.0 | 746.0 |
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| 23439.0 | 523.0 |
| 23455.0 | 4.0 |
| 23523.0 | 3.0 |
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| 23658.0 | 703.0 |
| 23706.0 | 57.0 |
| 24171.0 | 433.0 |
| 24200.0 | 1.0 |
| 24347.0 | 752.0 |
| 24354.0 | 901.0 |
| 24355.0 | 823.0 |
| 24504.0 | 2.0 |
| 24630.0 | 1389.0 |
| 24663.0 | 372.0 |
| 24664.0 | 159.0 |
| 24690.0 | 41.0 |
| 24719.0 | 1.0 |
| 24873.0 | 149.0 |
| 24985.0 | 251.0 |
| 25203.0 | 306.0 |
| 25638.0 | 402.0 |
| 25639.0 | 526.0 |
| 25686.0 | 606.0 |
| 26087.0 | 3.0 |
| 26178.0 | 1.0 |
| 26200.0 | 74.0 |
| 26544.0 | 21.0 |
| 26623.0 | 1.0 |
| 26769.0 | 2482.0 |
| 26787.0 | 1137.0 |
| 26801.0 | 1.0 |
| 27030.0 | 3.0 |
| 27118.0 | 389.0 |
| 27129.0 | 371.0 |
| 27214.0 | 300.0 |
| 27255.0 | 198.0 |
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| 27266.0 | 181.0 |
| 27466.0 | 353.0 |
| 27480.0 | 671.0 |
| 27609.0 | 174.0 |
| 27709.0 | 244.0 |
| 27760.0 | 121.0 |
| 27856.0 | 196.0 |
| 27888.0 | 97.0 |
| 27966.0 | 1.0 |
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| 28024.0 | 325.0 |
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| 29566.0 | 1.0 |
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| 29719.0 | 3.0 |
| 29744.0 | 1.0 |
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| 29894.0 | 1.0 |
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| 29997.0 | 1.0 |
| 30183.0 | 3.0 |
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| 30525.0 | 27.0 |
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| 30970.0 | 1.0 |
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| 31380.0 | 3.0 |
| 31435.0 | 295.0 |
| 31447.0 | 33.0 |
| 31528.0 | 28.0 |
| 31689.0 | 1.0 |
| 31759.0 | 367.0 |
| 31763.0 | 1.0 |
| 31834.0 | 748.0 |
| 31912.0 | 1.0 |
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| 31951.0 | 1.0 |
| 32102.0 | 1.0 |
| 32291.0 | 86.0 |
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| 32525.0 | 103.0 |
| 32622.0 | 668.0 |
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| 32871.0 | 3.0 |
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| 35947.0 | 1.0 |
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| 41175.0 | 11.0 |
| 41265.0 | 10.0 |
| 41409.0 | 47.0 |
| 41496.0 | 7.0 |
| 41575.0 | 1.0 |
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| 41751.0 | 1.0 |
| 41887.0 | 271.0 |
| 41890.0 | 385.0 |
| 41913.0 | 13.0 |
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| 42102.0 | 1.0 |
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| 44022.0 | 74.0 |
| 44141.0 | 1.0 |
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| 44595.0 | 1.0 |
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| 44740.0 | 917.0 |
| 44776.0 | 1121.0 |
| 44822.0 | 359.0 |
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| 45615.0 | 1.0 |
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| 47300.0 | 343.0 |
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| 52670.0 | 2.0 |
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| 53338.0 | 251.0 |
| 53340.0 | 623.0 |
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| 53963.0 | 3.0 |
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| 54844.0 | 3.0 |
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| 54980.0 | 899.0 |
| 54984.0 | 1.0 |
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| 55013.0 | 440.0 |
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| 55272.0 | 2.0 |
| 55279.0 | 155.0 |
| 55283.0 | 127.0 |
| 55363.0 | 72.0 |
| 55498.0 | 43.0 |
| 55539.0 | 271.0 |
| 55547.0 | 1.0 |
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| 56022.0 | 1.0 |
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| 56110.0 | 108.0 |
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| 56203.0 | 81.0 |
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import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
Article Graph Exploration
In this notebook, the graph consisting of all Wikipedia articles and their connections through links will be explored. The purpose of this notebook is to answer the following questions: * How big is the graph in terms of - Nodes, i.e. articles - Edges, i.e. links * How dense is the graph * Which articles have the highest - Degree - In/Out Degree * What can be said about the article length - mean - median - quantiles - distribution
Load Data
The first step is to locate the data we use to build the article graph, which was created in the preprocessing notebook HREF TO V NB HERE.
display(spark.sql("SHOW TABLES"))
Next we read the tables with data regarding articles and categories in order to first create the nodes and their attributes.
val dfPages = spark.sql("SELECT * FROM enwiki_page") // Read pages data
val dfCategory = spark.sql("SELECT * FROM enwiki_category") // Read categories
val dfCategoryLinks = spark.sql("SELECT * FROM enwiki_categorylinks") // Read links between articles/categories and categories
dfPages: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 5 more fields]
dfCategory: org.apache.spark.sql.DataFrame = [cat_id: int, cat_title: string ... 3 more fields]
dfCategoryLinks: org.apache.spark.sql.DataFrame = [cl_from: int, cl_to: string ... 1 more field]
We join the dataframes and drop all nodes that are redirect pages.
// Join pages with category information
val dfArticlesCat = dfPages.filter(col("page_is_redirect")===0) // remove all redirects
.join(dfCategoryLinks.filter(col("cl_type")==="page"), col("page_id")===col("cl_from"), "left")
dfArticlesCat: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 8 more fields]
// Group on article and aggregate the categories as a set per article
val dfArticlesCatGrouped = dfArticlesCat.groupBy("page_id","page_title","page_len").agg(collect_set(col("cl_to")))
dfArticlesCatGrouped: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 2 more fields]
Finally we store this as our vertex dataframe.
val dfVertex = dfArticlesCatGrouped.withColumnRenamed("page_id", "id").withColumnRenamed("collect_set(cl_to)", "categories")
dfVertex.cache()
dfVertex: org.apache.spark.sql.DataFrame = [id: int, page_title: string ... 2 more fields]
res8: dfVertex.type = [id: int, page_title: string ... 2 more fields]
The next step is to collect the edge data. Here we use a processed dataset of links which has been enriched by merging redirects into direct links (SEE V NOTEBOOK)
val dfEdgeLinks = spark.sql("SELECT * FROM enwiki_graph_edges_shortenedredirects") // Download the edges w.o. redirects
val dfEdges = dfEdgeLinks.select("src", "dst")
dfEdgeLinks: org.apache.spark.sql.DataFrame = [src: int, src_title: string ... 3 more fields]
dfEdges: org.apache.spark.sql.DataFrame = [src: int, dst: int]
Further, to avoid edges pointing towards nodes that do not exist we use two joins to filter out edges pointing towards nodes outside of our graph.
// Since graphframes does not remove edges between non existing edges automatically, we do this manually thru joins
// This is done in 2 steps where we 1st remove edges where the source does not exist
// Secondly we remove edges where the destination does not exist
val filteredEdges = dfEdgeLinks.join(dfVertex,
col("src")===dfVertex.col("id"), "inner")
.select("src", "dst")
.join(dfVertex,
col("dst")===dfVertex.col("id"), "inner").select("src","dst")
filteredEdges: org.apache.spark.sql.DataFrame = [src: int, dst: int]
val dfEdges = filteredEdges
dfEdges: org.apache.spark.sql.DataFrame = [src: int, dst: int]
Finally we can create our GraphFrame using the vertex and edge dataframes created prior.
// Create the full graph from the non-redirect articles and the the filtered edges
val g = GraphFrame(dfVertex, dfEdges).cache()
g: org.graphframes.GraphFrame = GraphFrame(v:[id: int, page_title: string ... 2 more fields], e:[src: int, dst: int])
Graph Exploration
Lets look at some key attributes of the graph to get a simple understanding of its size, density and nodes.
// In total full graph
val noNodes = g.vertices.count
val noEdges = g.edges.count
val density = noEdges / noNodes
noNodes: Long = 6569604
noEdges: Long = 607780945
density: Long = 92
// Average degree (in + out)
g.degrees.select(avg("degree")).show()
+-----------------+
| avg(degree)|
+-----------------+
|72.12373072701943|
+-----------------+
display(g.degrees)
| id | degree |
|---|---|
| 3997.0 | 3066.0 |
| 24354.0 | 4277.0 |
| 383675.0 | 827.0 |
| 526941.0 | 94.0 |
| 1044976.0 | 44.0 |
| 2580748.0 | 143.0 |
| 2777190.0 | 55.0 |
| 3335116.0 | 981.0 |
| 3906873.0 | 318.0 |
| 4171885.0 | 442.0 |
| 5527838.0 | 1.0 |
| 5773466.0 | 432.0 |
| 1.0448803e7 | 9.0 |
| 1.3900404e7 | 160.0 |
| 1.4723395e7 | 572.0 |
| 1.8791403e7 | 59.0 |
| 2.2814939e7 | 383.0 |
| 3.8738601e7 | 1.0 |
| 4.9844865e7 | 51.0 |
| 5.2701891e7 | 496.0 |
| 5.272196e7 | 995.0 |
| 5.2724058e7 | 971.0 |
| 6.9363482e7 | 3562.0 |
| 85332.0 | 754.0 |
| 2484503.0 | 593.0 |
| 1299663.0 | 938.0 |
| 1528448.0 | 871.0 |
| 3.568145e7 | 698.0 |
| 3.5702813e7 | 694.0 |
| 4.7862306e7 | 2814.0 |
| 18051.0 | 907.0 |
| 581422.0 | 152.0 |
| 2735971.0 | 575.0 |
| 6744757.0 | 1931.0 |
| 6762807.0 | 214.0 |
| 7820803.0 | 443.0 |
| 7946810.0 | 234.0 |
| 1.2248345e7 | 318.0 |
| 1.2743502e7 | 34.0 |
| 1.28778e7 | 152.0 |
| 2.3610877e7 | 202.0 |
| 3.4663596e7 | 26.0 |
| 4.0125689e7 | 169.0 |
| 4.4118511e7 | 620.0 |
| 4.6306198e7 | 112.0 |
| 5.399198e7 | 178.0 |
| 5.4531811e7 | 293.0 |
| 5.7149547e7 | 15.0 |
| 6.0188959e7 | 13.0 |
| 6.3363388e7 | 22.0 |
| 6.465077e7 | 344.0 |
| 11141.0 | 2064.0 |
| 15790.0 | 2241.0 |
| 15846.0 | 2126.0 |
| 19530.0 | 1978.0 |
| 28146.0 | 2165.0 |
| 42468.0 | 239.0 |
| 1.8828259e7 | 143.0 |
| 1.9157323e7 | 254.0 |
| 2.6328957e7 | 224.0 |
| 4.8503188e7 | 212.0 |
| 30903.0 | 1098.0 |
| 1247265.0 | 420.0 |
| 2.2396088e7 | 382.0 |
| 343134.0 | 2588.0 |
| 2080614.0 | 56.0 |
| 5.4576363e7 | 76.0 |
| 7.0582827e7 | 1.0 |
| 72758.0 | 9371.0 |
| 2188048.0 | 905.0 |
| 7744446.0 | 403.0 |
| 1.5000856e7 | 1439.0 |
| 1.5137812e7 | 1970.0 |
| 2.3329907e7 | 1242.0 |
| 5.0760135e7 | 432.0 |
| 2.8387935e7 | 521.0 |
| 1.497893e7 | 154.0 |
| 1.8814781e7 | 722.0 |
| 2.8212195e7 | 151.0 |
| 2.907822e7 | 304.0 |
| 3.2197081e7 | 455.0 |
| 4.7917953e7 | 385.0 |
| 5.5455444e7 | 288.0 |
| 5.5960908e7 | 136.0 |
| 6.6241271e7 | 296.0 |
| 7360355.0 | 669.0 |
| 1.4582319e7 | 105.0 |
| 5.8265866e7 | 16.0 |
| 13289.0 | 5210.0 |
| 13623.0 | 1532.0 |
| 105796.0 | 2895.0 |
| 611323.0 | 749.0 |
| 923896.0 | 207.0 |
| 1026468.0 | 218.0 |
| 2352987.0 | 2299.0 |
| 2569830.0 | 24.0 |
| 3072809.0 | 1586.0 |
| 4238857.0 | 476.0 |
| 8518977.0 | 168.0 |
| 1.4250638e7 | 54.0 |
| 1.7405545e7 | 51.0 |
| 1.7990997e7 | 1502.0 |
| 3.0555928e7 | 32.0 |
| 3.8759326e7 | 27.0 |
| 3.9269714e7 | 418.0 |
| 3.9817572e7 | 248.0 |
| 5.1907848e7 | 69.0 |
| 5.8427038e7 | 58.0 |
| 6.8021038e7 | 131.0 |
| 6.8072188e7 | 85.0 |
| 6.9618014e7 | 14.0 |
| 513940.0 | 25.0 |
| 1568873.0 | 1099.0 |
| 1.1421308e7 | 313.0 |
| 1.1421636e7 | 583.0 |
| 1.1485448e7 | 28.0 |
| 1.3629231e7 | 28.0 |
| 3.6847774e7 | 43.0 |
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| 184976.0 | 490.0 |
| 185513.0 | 84.0 |
| 186588.0 | 37.0 |
| 187027.0 | 302.0 |
| 188488.0 | 684.0 |
| 188644.0 | 386.0 |
| 188834.0 | 11277.0 |
| 188986.0 | 283.0 |
| 189182.0 | 389.0 |
| 189488.0 | 282.0 |
| 190227.0 | 697.0 |
| 191350.0 | 19.0 |
| 191933.0 | 610.0 |
| 192401.0 | 135.0 |
| 192545.0 | 979.0 |
| 192952.0 | 14.0 |
| 194034.0 | 2062.0 |
| 195291.0 | 124.0 |
| 195367.0 | 399.0 |
| 196013.0 | 1.0 |
| 196290.0 | 862.0 |
| 197588.0 | 333.0 |
| 198800.0 | 530.0 |
| 202253.0 | 12.0 |
| 203592.0 | 309.0 |
| 203894.0 | 174.0 |
| 204839.0 | 476.0 |
| 204974.0 | 532.0 |
| 205013.0 | 1535.0 |
| 205392.0 | 105.0 |
| 206351.0 | 734.0 |
| 206719.0 | 879.0 |
| 207103.0 | 881.0 |
| 210661.0 | 1059.0 |
| 210744.0 | 2955.0 |
| 212007.0 | 454.0 |
| 212504.0 | 379.0 |
| 213270.0 | 1181.0 |
| 213483.0 | 391.0 |
| 213516.0 | 556.0 |
| 214547.0 | 194.0 |
| 214719.0 | 78.0 |
| 214739.0 | 373.0 |
| 216619.0 | 313.0 |
| 216635.0 | 243.0 |
| 216854.0 | 316.0 |
| 216941.0 | 288.0 |
| 217119.0 | 778.0 |
| 219514.0 | 171.0 |
| 219523.0 | 190.0 |
| 219558.0 | 60.0 |
| 221858.0 | 298.0 |
| 222543.0 | 839.0 |
| 222556.0 | 581.0 |
| 225359.0 | 386.0 |
| 227161.0 | 202.0 |
| 229071.0 | 584.0 |
| 229254.0 | 461.0 |
| 230513.0 | 318.0 |
| 230596.0 | 1.0 |
| 231287.0 | 490.0 |
| 231350.0 | 304.0 |
| 232043.0 | 18.0 |
| 232643.0 | 472.0 |
| 233567.0 | 1560.0 |
| 233799.0 | 382.0 |
| 234892.0 | 322.0 |
| 234983.0 | 33.0 |
| 236532.0 | 40.0 |
| 236636.0 | 721.0 |
| 236893.0 | 78.0 |
| 237019.0 | 5152.0 |
| 237504.0 | 11506.0 |
| 238193.0 | 1289.0 |
| 239148.0 | 154.0 |
| 240376.0 | 297.0 |
| 241236.0 | 765.0 |
| 241533.0 | 148.0 |
| 242832.0 | 69.0 |
| 243022.0 | 447.0 |
| 244128.0 | 306.0 |
| 244597.0 | 133.0 |
| 245390.0 | 1189.0 |
| 246703.0 | 231.0 |
| 246728.0 | 1443.0 |
| 246944.0 | 76.0 |
| 247396.0 | 473.0 |
| 247653.0 | 297.0 |
| 250336.0 | 954.0 |
| 251316.0 | 293.0 |
| 251353.0 | 237.0 |
| 252206.0 | 238.0 |
| 253769.0 | 752.0 |
| 255040.0 | 214.0 |
| 255247.0 | 480.0 |
| 255394.0 | 73.0 |
| 256092.0 | 449.0 |
| 256425.0 | 1276.0 |
| 256830.0 | 245.0 |
| 258032.0 | 516.0 |
| 259633.0 | 190.0 |
| 259849.0 | 248.0 |
| 260195.0 | 130.0 |
| 260726.0 | 399.0 |
| 260819.0 | 163.0 |
| 261168.0 | 306.0 |
| 262597.0 | 518.0 |
| 265189.0 | 1215.0 |
| 265240.0 | 45.0 |
| 265769.0 | 236.0 |
| 266215.0 | 997.0 |
| 266617.0 | 645.0 |
| 268622.0 | 1127.0 |
| 270981.0 | 783.0 |
| 271109.0 | 1102.0 |
| 272094.0 | 141.0 |
| 273285.0 | 215601.0 |
| 273916.0 | 720.0 |
| 274468.0 | 439.0 |
| 274848.0 | 1063.0 |
| 275204.0 | 452.0 |
| 276399.0 | 69.0 |
| 276436.0 | 3662.0 |
| 277349.0 | 116.0 |
| 277404.0 | 572.0 |
| 282396.0 | 563.0 |
| 283975.0 | 2679.0 |
| 284489.0 | 253.0 |
| 284944.0 | 987.0 |
| 286323.0 | 408.0 |
| 286699.0 | 3.0 |
| 287568.0 | 823.0 |
| 290513.0 | 193.0 |
| 291112.0 | 1614.0 |
| 292080.0 | 362.0 |
| 292083.0 | 1735.0 |
| 292297.0 | 70.0 |
| 292608.0 | 1.0 |
| 292708.0 | 1694.0 |
| 293418.0 | 108.0 |
| 294136.0 | 593.0 |
| 295286.0 | 772.0 |
| 296688.0 | 1.0 |
| 297391.0 | 354.0 |
| 298705.0 | 1882.0 |
| 300474.0 | 1.0 |
| 300539.0 | 741.0 |
| 300825.0 | 1713.0 |
| 301798.0 | 691.0 |
| 302825.0 | 403.0 |
| 303632.0 | 327.0 |
| 306504.0 | 4396.0 |
| 308075.0 | 956.0 |
| 308619.0 | 366.0 |
| 308930.0 | 611.0 |
| 310155.0 | 1.0 |
| 310436.0 | 278.0 |
| 310514.0 | 254.0 |
| 310547.0 | 881.0 |
| 310950.0 | 484.0 |
| 311192.0 | 443.0 |
| 311544.0 | 105.0 |
| 312383.0 | 144.0 |
| 313148.0 | 285.0 |
| 314115.0 | 150.0 |
| 316184.0 | 353.0 |
| 317828.0 | 209.0 |
| 318168.0 | 254.0 |
| 319698.0 | 532.0 |
| 319884.0 | 1745.0 |
| 320408.0 | 1008.0 |
| 320632.0 | 146.0 |
| 320680.0 | 637.0 |
| 321560.0 | 1600.0 |
| 321932.0 | 709.0 |
| 322355.0 | 714.0 |
| 323084.0 | 566.0 |
| 323181.0 | 508.0 |
| 323599.0 | 1.0 |
| 324091.0 | 2364.0 |
| 325102.0 | 23.0 |
| 325894.0 | 548.0 |
| 326538.0 | 6398.0 |
| 328324.0 | 231.0 |
| 328529.0 | 244.0 |
| 328989.0 | 405.0 |
| 329607.0 | 137.0 |
| 330299.0 | 263.0 |
| 330799.0 | 695.0 |
| 333479.0 | 1542.0 |
| 335442.0 | 612.0 |
| 336694.0 | 2959.0 |
| 338090.0 | 567.0 |
| 338512.0 | 452.0 |
| 338757.0 | 496.0 |
| 340002.0 | 1754.0 |
| 340950.0 | 265.0 |
| 342305.0 | 2216.0 |
| 342902.0 | 572.0 |
| 343353.0 | 599.0 |
| 343570.0 | 819.0 |
| 343903.0 | 1138.0 |
| 343960.0 | 1068.0 |
| 344048.0 | 414.0 |
| 344070.0 | 135.0 |
| 344507.0 | 81.0 |
| 346809.0 | 300.0 |
| 346916.0 | 135.0 |
| 347258.0 | 222.0 |
| 347352.0 | 1887.0 |
| 347379.0 | 402.0 |
| 347775.0 | 324.0 |
| 348799.0 | 2470.0 |
| 348851.0 | 50.0 |
| 350399.0 | 1014.0 |
| 350569.0 | 340.0 |
| 350570.0 | 343.0 |
| 351267.0 | 286.0 |
| 351369.0 | 142.0 |
| 352674.0 | 316.0 |
| 352731.0 | 314.0 |
| 353742.0 | 82.0 |
| 354686.0 | 143.0 |
| 355377.0 | 346.0 |
| 355477.0 | 1706.0 |
| 356454.0 | 322.0 |
| 356543.0 | 1240.0 |
| 357220.0 | 991.0 |
| 358095.0 | 1126.0 |
| 359354.0 | 224.0 |
| 360246.0 | 331.0 |
| 360668.0 | 21.0 |
| 361204.0 | 20.0 |
| 362827.0 | 221.0 |
| 362829.0 | 453.0 |
| 366447.0 | 399.0 |
| 366610.0 | 563.0 |
| 367125.0 | 184.0 |
| 367456.0 | 125.0 |
| 370123.0 | 398.0 |
| 371545.0 | 455.0 |
| 373118.0 | 1477.0 |
| 373721.0 | 341.0 |
| 374216.0 | 554.0 |
| 375375.0 | 1767.0 |
| 375623.0 | 908.0 |
| 376168.0 | 279.0 |
| 376270.0 | 116.0 |
| 376563.0 | 131.0 |
| 376576.0 | 111.0 |
| 377210.0 | 494.0 |
| 377372.0 | 189.0 |
| 377515.0 | 558.0 |
| 378053.0 | 1193.0 |
| 378262.0 | 255.0 |
| 378310.0 | 290.0 |
| 380922.0 | 1280.0 |
| 382614.0 | 61.0 |
| 383876.0 | 529.0 |
| 384788.0 | 149.0 |
| 384959.0 | 248.0 |
| 385152.0 | 27.0 |
| 386689.0 | 306.0 |
| 386707.0 | 232.0 |
| 386888.0 | 203.0 |
| 390373.0 | 446.0 |
| 390569.0 | 884.0 |
| 392119.0 | 141.0 |
// Average in-degree
g.inDegrees.select(avg("inDegree")).show()
+-----------------+
| avg(inDegree)|
+-----------------+
|94.53287118924774|
+-----------------+
display(g.inDegrees)
| id | inDegree |
|---|---|
| 1143.0 | 3691.0 |
| 1270.0 | 517.0 |
| 1322.0 | 185.0 |
| 1650.0 | 421.0 |
| 2393.0 | 2066.0 |
| 3352.0 | 12195.0 |
| 4391.0 | 972.0 |
| 6559.0 | 36.0 |
| 7387.0 | 1964.0 |
| 8222.0 | 572.0 |
| 8407.0 | 566.0 |
| 9454.0 | 256.0 |
| 10798.0 | 49.0 |
| 10862.0 | 613.0 |
| 11025.0 | 1071.0 |
| 11393.0 | 2397.0 |
| 11800.0 | 14.0 |
| 12393.0 | 115.0 |
| 12998.0 | 94.0 |
| 13009.0 | 1586.0 |
| 13060.0 | 278.0 |
| 13207.0 | 844.0 |
| 13483.0 | 1452.0 |
| 13601.0 | 2.0 |
| 13648.0 | 1.0 |
| 13910.0 | 20.0 |
| 14837.0 | 617.0 |
| 14997.0 | 91.0 |
| 15207.0 | 296.0 |
| 15655.0 | 21944.0 |
| 16283.0 | 231.0 |
| 16534.0 | 36.0 |
| 16791.0 | 69.0 |
| 17044.0 | 518.0 |
| 17775.0 | 627.0 |
| 17783.0 | 197.0 |
| 17809.0 | 175.0 |
| 18221.0 | 16.0 |
| 18382.0 | 840.0 |
| 19868.0 | 7.0 |
| 20029.0 | 36.0 |
| 20134.0 | 5935.0 |
| 20398.0 | 203.0 |
| 21058.0 | 766.0 |
| 22609.0 | 46.0 |
| 22684.0 | 28.0 |
| 23144.0 | 5.0 |
| 25203.0 | 583.0 |
| 25638.0 | 340.0 |
| 27214.0 | 166.0 |
| 27266.0 | 164.0 |
| 29109.0 | 483.0 |
| 29177.0 | 1565.0 |
| 29630.0 | 179.0 |
| 29942.0 | 635.0 |
| 30330.0 | 623.0 |
| 30525.0 | 61.0 |
| 30617.0 | 316.0 |
| 31285.0 | 327.0 |
| 31350.0 | 1301.0 |
| 31834.0 | 342.0 |
| 32648.0 | 7.0 |
| 32680.0 | 845.0 |
| 33607.0 | 1457.0 |
| 34197.0 | 4213.0 |
| 34488.0 | 256.0 |
| 34569.0 | 1164.0 |
| 34602.0 | 2066.0 |
| 34611.0 | 1268.0 |
| 34697.0 | 263.0 |
| 34713.0 | 474.0 |
| 35044.0 | 58.0 |
| 35399.0 | 27.0 |
| 35632.0 | 36.0 |
| 35794.0 | 24.0 |
| 36192.0 | 62.0 |
| 36347.0 | 35.0 |
| 37409.0 | 2593.0 |
| 37657.0 | 4.0 |
| 38297.0 | 12.0 |
| 38607.0 | 170.0 |
| 39742.0 | 101.0 |
| 39977.0 | 74.0 |
| 41126.0 | 1.0 |
| 41496.0 | 3.0 |
| 41696.0 | 5.0 |
| 41913.0 | 146.0 |
| 43938.0 | 8.0 |
| 44205.0 | 758.0 |
| 44884.0 | 4.0 |
| 46818.0 | 697.0 |
| 48316.0 | 47.0 |
| 48838.0 | 61.0 |
| 48880.0 | 42.0 |
| 49503.0 | 344.0 |
| 49686.0 | 314.0 |
| 50847.0 | 341.0 |
| 51022.0 | 318.0 |
| 51640.0 | 124.0 |
| 52100.0 | 163.0 |
| 52987.0 | 704.0 |
| 53056.0 | 1107.0 |
| 53528.0 | 752.0 |
| 54172.0 | 404.0 |
| 54448.0 | 443.0 |
| 54551.0 | 73.0 |
| 54958.0 | 993.0 |
| 54989.0 | 584.0 |
| 55013.0 | 549.0 |
| 55498.0 | 43.0 |
| 55539.0 | 351.0 |
| 56168.0 | 1704.0 |
| 56259.0 | 942.0 |
| 56617.0 | 855.0 |
| 56943.0 | 173.0 |
| 57955.0 | 357.0 |
| 58811.0 | 218.0 |
| 59680.0 | 12.0 |
| 60001.0 | 1689.0 |
| 60068.0 | 127.0 |
| 60964.0 | 217.0 |
| 61202.0 | 18.0 |
| 61344.0 | 1923.0 |
| 62172.0 | 3.0 |
| 62740.0 | 341.0 |
| 63469.0 | 825.0 |
| 64648.0 | 1826.0 |
| 65104.0 | 181.0 |
| 65305.0 | 609.0 |
| 67231.0 | 1008.0 |
| 67618.0 | 126.0 |
| 67865.0 | 92.0 |
| 68078.0 | 335.0 |
| 68592.0 | 15.0 |
| 68678.0 | 3.0 |
| 69247.0 | 6.0 |
| 69385.0 | 129.0 |
| 71821.0 | 3983.0 |
| 72785.0 | 116.0 |
| 74097.0 | 1056.0 |
| 74411.0 | 676.0 |
| 74854.0 | 9.0 |
| 74948.0 | 2.0 |
| 75515.0 | 702.0 |
| 75877.0 | 184.0 |
| 75979.0 | 4.0 |
| 76366.0 | 573.0 |
| 77352.0 | 2290.0 |
| 77488.0 | 194.0 |
| 78023.0 | 8250.0 |
| 79130.0 | 91.0 |
| 79791.0 | 244.0 |
| 82498.0 | 126.0 |
| 82516.0 | 2.0 |
| 84275.0 | 865.0 |
| 84720.0 | 5.0 |
| 84936.0 | 991.0 |
| 85748.0 | 1928.0 |
| 86206.0 | 1.0 |
| 86406.0 | 80.0 |
| 89044.0 | 296.0 |
| 90446.0 | 436.0 |
| 90692.0 | 452.0 |
| 90891.0 | 16.0 |
| 91084.0 | 730.0 |
| 91473.0 | 390.0 |
| 91516.0 | 453.0 |
| 93112.0 | 476.0 |
| 93431.0 | 1074.0 |
| 93487.0 | 2.0 |
| 93645.0 | 327.0 |
| 94274.0 | 130.0 |
| 94284.0 | 67.0 |
| 94642.0 | 502.0 |
| 95701.0 | 314.0 |
| 95707.0 | 361.0 |
| 96382.0 | 2.0 |
| 96706.0 | 520.0 |
| 96978.0 | 1138.0 |
| 97385.0 | 269.0 |
| 97435.0 | 7.0 |
| 97682.0 | 528.0 |
| 98521.0 | 1714.0 |
| 98654.0 | 186.0 |
| 101604.0 | 345.0 |
| 102282.0 | 315.0 |
| 102775.0 | 3340.0 |
| 103751.0 | 28.0 |
| 103767.0 | 387.0 |
| 103918.0 | 2.0 |
| 104909.0 | 218.0 |
| 105086.0 | 104.0 |
| 105673.0 | 40.0 |
| 106346.0 | 308.0 |
| 107118.0 | 41.0 |
| 107401.0 | 717.0 |
| 107820.0 | 153.0 |
| 108159.0 | 123.0 |
| 108223.0 | 263.0 |
| 108249.0 | 183.0 |
| 108500.0 | 37.0 |
| 108976.0 | 415.0 |
| 109035.0 | 120.0 |
| 109450.0 | 379.0 |
| 109517.0 | 39.0 |
| 109546.0 | 89.0 |
| 109859.0 | 46.0 |
| 110168.0 | 226.0 |
| 110203.0 | 27.0 |
| 110512.0 | 112.0 |
| 110639.0 | 1031.0 |
| 110643.0 | 56.0 |
| 110842.0 | 83.0 |
| 110938.0 | 83.0 |
| 111940.0 | 52.0 |
| 112032.0 | 40.0 |
| 112265.0 | 103.0 |
| 112269.0 | 58.0 |
| 112796.0 | 55.0 |
| 113812.0 | 34.0 |
| 114128.0 | 53.0 |
| 114339.0 | 52.0 |
| 114680.0 | 427.0 |
| 114859.0 | 43.0 |
| 115150.0 | 32.0 |
| 116183.0 | 187.0 |
| 116255.0 | 89.0 |
| 116707.0 | 121.0 |
| 116786.0 | 360.0 |
| 117385.0 | 74.0 |
| 119079.0 | 71.0 |
| 119125.0 | 1178.0 |
| 119641.0 | 106.0 |
| 119777.0 | 60.0 |
| 120029.0 | 83.0 |
| 120095.0 | 532.0 |
| 120275.0 | 73.0 |
| 120293.0 | 49.0 |
| 120315.0 | 149.0 |
| 120422.0 | 45.0 |
| 120638.0 | 36.0 |
| 121434.0 | 38.0 |
| 121746.0 | 227.0 |
| 121855.0 | 86.0 |
| 122494.0 | 50.0 |
| 122936.0 | 49.0 |
| 123032.0 | 46.0 |
| 123291.0 | 72.0 |
| 124208.0 | 28.0 |
| 124432.0 | 359.0 |
| 124642.0 | 188.0 |
| 124680.0 | 35.0 |
| 125412.0 | 606.0 |
| 125540.0 | 158.0 |
| 126309.0 | 105.0 |
| 126446.0 | 62.0 |
| 126622.0 | 115.0 |
| 126982.0 | 67.0 |
| 127092.0 | 147.0 |
| 127440.0 | 44.0 |
| 127933.0 | 58.0 |
| 128243.0 | 82.0 |
| 128325.0 | 4422.0 |
| 128345.0 | 1504.0 |
| 128346.0 | 1280.0 |
| 128353.0 | 1194.0 |
| 129488.0 | 37.0 |
| 129632.0 | 65.0 |
| 129726.0 | 76.0 |
| 130013.0 | 40.0 |
| 130418.0 | 28.0 |
| 130437.0 | 27.0 |
| 131430.0 | 151.0 |
| 131479.0 | 90.0 |
| 131506.0 | 1111.0 |
| 131530.0 | 327.0 |
| 131757.0 | 152.0 |
| 132165.0 | 78.0 |
| 132973.0 | 76.0 |
| 133198.0 | 67.0 |
| 134873.0 | 24.0 |
| 135066.0 | 98.0 |
| 135137.0 | 33.0 |
| 135309.0 | 34.0 |
| 135337.0 | 26.0 |
| 135520.0 | 101.0 |
| 135704.0 | 109.0 |
| 135843.0 | 59.0 |
| 135846.0 | 44.0 |
| 135883.0 | 61.0 |
| 136030.0 | 30.0 |
| 136045.0 | 200.0 |
| 136180.0 | 304.0 |
| 136545.0 | 68.0 |
| 136943.0 | 59.0 |
| 137087.0 | 92.0 |
| 137235.0 | 69.0 |
| 137352.0 | 116.0 |
| 138437.0 | 302.0 |
| 139240.0 | 57.0 |
| 139255.0 | 47.0 |
| 139288.0 | 54.0 |
| 139459.0 | 69.0 |
| 139563.0 | 68.0 |
| 139564.0 | 72.0 |
| 140017.0 | 79.0 |
| 140165.0 | 32.0 |
| 140287.0 | 3901.0 |
| 140299.0 | 520.0 |
| 141499.0 | 6.0 |
| 141564.0 | 4.0 |
| 143196.0 | 418.0 |
| 143407.0 | 5.0 |
| 144317.0 | 87.0 |
| 145184.0 | 48.0 |
| 145206.0 | 167.0 |
| 145249.0 | 201.0 |
| 145527.0 | 834.0 |
| 145732.0 | 166.0 |
| 145892.0 | 348.0 |
| 146483.0 | 21.0 |
| 146731.0 | 89.0 |
| 149014.0 | 557.0 |
| 149063.0 | 2.0 |
| 149290.0 | 348.0 |
| 149344.0 | 1099.0 |
| 150112.0 | 3.0 |
| 150793.0 | 193.0 |
| 151299.0 | 210.0 |
| 152018.0 | 473.0 |
| 153014.0 | 170.0 |
| 153465.0 | 1262.0 |
| 153737.0 | 30.0 |
| 155300.0 | 2.0 |
| 155547.0 | 953.0 |
| 158782.0 | 75.0 |
| 158834.0 | 638.0 |
| 159433.0 | 3174.0 |
| 160370.0 | 73.0 |
| 160979.0 | 1179.0 |
| 161791.0 | 261.0 |
| 161948.0 | 5.0 |
| 162798.0 | 122.0 |
| 163118.0 | 3003.0 |
| 164143.0 | 1376.0 |
| 165684.0 | 375.0 |
| 166547.0 | 11.0 |
| 167868.0 | 22.0 |
| 168269.0 | 1150.0 |
| 168308.0 | 529.0 |
| 168375.0 | 475.0 |
| 168902.0 | 687.0 |
| 169073.0 | 134.0 |
| 169717.0 | 381.0 |
| 171070.0 | 78.0 |
| 171272.0 | 1311.0 |
| 171528.0 | 1075.0 |
| 171754.0 | 168.0 |
| 174181.0 | 670.0 |
| 174247.0 | 101.0 |
| 175750.0 | 81.0 |
| 176233.0 | 66.0 |
| 176751.0 | 275.0 |
| 178343.0 | 10.0 |
| 178462.0 | 223.0 |
| 181226.0 | 812.0 |
| 181343.0 | 113.0 |
| 182011.0 | 183.0 |
| 184215.0 | 289.0 |
| 184607.0 | 64.0 |
| 185127.0 | 509.0 |
| 185271.0 | 67.0 |
| 185578.0 | 39.0 |
| 186704.0 | 246.0 |
| 188040.0 | 30.0 |
| 191506.0 | 224.0 |
| 193410.0 | 54.0 |
| 193965.0 | 1216.0 |
| 194292.0 | 413.0 |
| 197207.0 | 38.0 |
| 197753.0 | 21.0 |
| 198568.0 | 117.0 |
| 199049.0 | 9.0 |
| 201120.0 | 350.0 |
| 201484.0 | 160.0 |
| 202687.0 | 3365.0 |
| 203949.0 | 73.0 |
| 204157.0 | 503.0 |
| 204223.0 | 25.0 |
| 205098.0 | 767.0 |
| 205427.0 | 107.0 |
| 207007.0 | 49.0 |
| 208072.0 | 461.0 |
| 208382.0 | 366.0 |
| 210133.0 | 480.0 |
| 210350.0 | 147.0 |
| 210918.0 | 1.0 |
| 211077.0 | 66.0 |
| 213595.0 | 213.0 |
| 213731.0 | 148.0 |
| 213839.0 | 10.0 |
| 214801.0 | 842.0 |
| 215073.0 | 208.0 |
| 215422.0 | 4.0 |
| 215741.0 | 301.0 |
| 215902.0 | 96.0 |
| 218425.0 | 375.0 |
| 219157.0 | 191.0 |
| 219506.0 | 119.0 |
| 220118.0 | 988.0 |
| 220409.0 | 193.0 |
| 220443.0 | 444.0 |
| 221642.0 | 1935.0 |
| 221800.0 | 4.0 |
| 222550.0 | 75.0 |
| 224110.0 | 131.0 |
| 224209.0 | 7.0 |
| 225464.0 | 113.0 |
| 225903.0 | 310.0 |
| 226065.0 | 156.0 |
| 228735.0 | 270.0 |
| 228845.0 | 1210.0 |
| 229519.0 | 78.0 |
| 229520.0 | 1399.0 |
| 230343.0 | 151.0 |
| 232942.0 | 510.0 |
| 233264.0 | 253.0 |
| 234671.0 | 610.0 |
| 234948.0 | 1.0 |
| 238417.0 | 68.0 |
| 238978.0 | 159.0 |
| 239403.0 | 8.0 |
| 239704.0 | 403.0 |
| 240436.0 | 61.0 |
| 241041.0 | 117.0 |
| 241052.0 | 199.0 |
| 241131.0 | 234.0 |
| 241513.0 | 30.0 |
| 241607.0 | 188.0 |
| 242218.0 | 41.0 |
| 242758.0 | 17.0 |
| 244225.0 | 369.0 |
| 244396.0 | 2.0 |
| 246160.0 | 297.0 |
| 246328.0 | 440.0 |
| 247187.0 | 31.0 |
| 248061.0 | 85.0 |
| 248084.0 | 13.0 |
| 250022.0 | 2233.0 |
| 250856.0 | 520.0 |
| 250937.0 | 165.0 |
| 251015.0 | 19.0 |
| 251176.0 | 338.0 |
| 251821.0 | 14.0 |
| 252487.0 | 373.0 |
| 253174.0 | 1484.0 |
| 254926.0 | 4.0 |
| 255514.0 | 293.0 |
| 255613.0 | 48.0 |
| 257219.0 | 463.0 |
| 257231.0 | 92.0 |
| 258274.0 | 63.0 |
| 260310.0 | 173.0 |
| 262290.0 | 2.0 |
| 264918.0 | 918.0 |
| 265901.0 | 628.0 |
| 266301.0 | 100.0 |
| 266920.0 | 643.0 |
| 269545.0 | 261.0 |
| 269839.0 | 175.0 |
| 271783.0 | 3.0 |
| 275092.0 | 2243.0 |
| 275908.0 | 64.0 |
| 276661.0 | 492.0 |
| 276997.0 | 446.0 |
| 277829.0 | 176.0 |
| 285090.0 | 141.0 |
| 286001.0 | 222.0 |
| 286367.0 | 5.0 |
| 288137.0 | 502.0 |
| 289821.0 | 52.0 |
| 293226.0 | 410.0 |
| 294288.0 | 91.0 |
| 294326.0 | 316.0 |
| 295046.0 | 144.0 |
| 295050.0 | 213.0 |
| 295194.0 | 33.0 |
| 296071.0 | 898.0 |
| 296472.0 | 17.0 |
| 297129.0 | 2488.0 |
| 300040.0 | 64.0 |
| 300152.0 | 2.0 |
| 300472.0 | 89.0 |
| 301839.0 | 618.0 |
| 302395.0 | 272.0 |
| 303439.0 | 132.0 |
| 304131.0 | 120.0 |
| 304253.0 | 609.0 |
| 304290.0 | 1.0 |
| 305376.0 | 252.0 |
| 306724.0 | 1031.0 |
| 307267.0 | 538.0 |
| 307801.0 | 2.0 |
| 308100.0 | 6.0 |
| 308913.0 | 4.0 |
| 309306.0 | 147.0 |
| 310108.0 | 78.0 |
| 312072.0 | 220.0 |
| 312252.0 | 198.0 |
| 312341.0 | 3.0 |
| 312989.0 | 691.0 |
| 313481.0 | 152.0 |
| 315015.0 | 199.0 |
| 315685.0 | 122.0 |
| 316095.0 | 547.0 |
| 317022.0 | 831.0 |
| 317602.0 | 10.0 |
| 319709.0 | 28.0 |
| 320300.0 | 29.0 |
| 320853.0 | 662.0 |
| 322269.0 | 886.0 |
| 322340.0 | 41.0 |
| 322603.0 | 3.0 |
| 323237.0 | 170.0 |
| 323465.0 | 3.0 |
| 325615.0 | 178.0 |
| 325956.0 | 116.0 |
| 326495.0 | 354.0 |
| 328674.0 | 121.0 |
| 329252.0 | 139.0 |
| 329436.0 | 94.0 |
| 331009.0 | 2.0 |
| 331337.0 | 271.0 |
| 332124.0 | 671.0 |
| 332348.0 | 100.0 |
| 332491.0 | 83.0 |
| 332738.0 | 111.0 |
| 332748.0 | 58.0 |
| 332841.0 | 3.0 |
| 333011.0 | 123.0 |
| 333487.0 | 86.0 |
| 333735.0 | 38.0 |
| 333965.0 | 49.0 |
| 334463.0 | 415.0 |
| 335014.0 | 117.0 |
| 336338.0 | 24.0 |
| 336911.0 | 654.0 |
| 337426.0 | 221.0 |
| 337536.0 | 1042.0 |
| 337691.0 | 549.0 |
| 338274.0 | 30.0 |
| 340039.0 | 541.0 |
| 341018.0 | 122.0 |
| 341822.0 | 38.0 |
| 344140.0 | 4686.0 |
| 346760.0 | 122.0 |
| 347092.0 | 713.0 |
| 347673.0 | 117.0 |
| 349640.0 | 49.0 |
| 349909.0 | 79.0 |
| 350614.0 | 333.0 |
| 351036.0 | 302.0 |
| 351372.0 | 244.0 |
| 352564.0 | 633.0 |
| 353025.0 | 182.0 |
| 353649.0 | 348.0 |
| 354234.0 | 176.0 |
| 355659.0 | 478.0 |
| 356014.0 | 56.0 |
| 357672.0 | 604.0 |
| 358176.0 | 2062.0 |
| 358896.0 | 93.0 |
| 360268.0 | 334.0 |
| 361053.0 | 250.0 |
| 362399.0 | 232.0 |
| 363278.0 | 206.0 |
| 363548.0 | 42.0 |
| 365238.0 | 169.0 |
| 367537.0 | 33.0 |
| 368227.0 | 534.0 |
| 371388.0 | 238.0 |
| 371942.0 | 134.0 |
| 371973.0 | 393.0 |
| 372912.0 | 182.0 |
| 373421.0 | 5.0 |
| 373595.0 | 4.0 |
| 373870.0 | 1203.0 |
| 374542.0 | 7.0 |
| 375597.0 | 135.0 |
| 375743.0 | 1295.0 |
| 377207.0 | 98.0 |
| 378179.0 | 237.0 |
| 379376.0 | 104.0 |
| 380175.0 | 238.0 |
| 380858.0 | 45.0 |
| 381099.0 | 370.0 |
| 381379.0 | 50.0 |
| 382208.0 | 6.0 |
| 382443.0 | 936.0 |
| 383017.0 | 216.0 |
| 383378.0 | 75.0 |
| 383996.0 | 220.0 |
| 384011.0 | 127.0 |
| 384368.0 | 794.0 |
| 385057.0 | 72.0 |
| 386112.0 | 124.0 |
| 387060.0 | 212.0 |
| 387618.0 | 415.0 |
| 387996.0 | 44.0 |
| 390498.0 | 112.0 |
| 390503.0 | 782.0 |
| 391167.0 | 2772.0 |
| 392103.0 | 234.0 |
| 392696.0 | 441.0 |
| 393174.0 | 527.0 |
| 393509.0 | 6.0 |
| 395889.0 | 438.0 |
| 396543.0 | 154.0 |
| 396732.0 | 745.0 |
| 398035.0 | 70.0 |
| 398558.0 | 683.0 |
| 399596.0 | 3.0 |
| 400641.0 | 1054.0 |
| 400719.0 | 335.0 |
| 400848.0 | 36.0 |
| 401119.0 | 133.0 |
| 401805.0 | 438.0 |
| 404171.0 | 1069.0 |
| 404304.0 | 308.0 |
| 405130.0 | 240.0 |
| 406071.0 | 4.0 |
| 406782.0 | 211.0 |
| 408352.0 | 499.0 |
| 409680.0 | 391.0 |
| 409932.0 | 233.0 |
| 412343.0 | 1772.0 |
| 412712.0 | 3.0 |
| 414602.0 | 66.0 |
| 415164.0 | 6.0 |
| 416328.0 | 786.0 |
| 417044.0 | 932.0 |
| 417547.0 | 530.0 |
| 417821.0 | 143.0 |
| 418465.0 | 1.0 |
| 418595.0 | 354.0 |
| 419952.0 | 171.0 |
| 420378.0 | 69.0 |
| 421656.0 | 583.0 |
| 422431.0 | 525.0 |
| 422583.0 | 2.0 |
| 422631.0 | 164.0 |
| 422886.0 | 1075.0 |
| 423393.0 | 86.0 |
| 423986.0 | 651.0 |
| 425716.0 | 310.0 |
| 425733.0 | 255.0 |
| 426178.0 | 358.0 |
| 427168.0 | 177.0 |
| 427245.0 | 757.0 |
| 428608.0 | 261.0 |
| 428742.0 | 172.0 |
| 431710.0 | 10.0 |
| 431765.0 | 142.0 |
| 432150.0 | 41.0 |
| 432943.0 | 638.0 |
| 434707.0 | 125.0 |
| 436021.0 | 52.0 |
| 436371.0 | 7.0 |
| 436553.0 | 1113.0 |
| 436650.0 | 16.0 |
| 436943.0 | 334.0 |
| 437812.0 | 229.0 |
| 439198.0 | 117.0 |
| 439619.0 | 3.0 |
| 441181.0 | 45.0 |
| 442270.0 | 189.0 |
| 443267.0 | 7.0 |
| 444422.0 | 567.0 |
| 445863.0 | 123.0 |
| 446274.0 | 395.0 |
| 447860.0 | 6.0 |
| 447922.0 | 289.0 |
| 448201.0 | 249.0 |
| 448766.0 | 242.0 |
| 448991.0 | 265.0 |
| 451717.0 | 671.0 |
| 452226.0 | 1407.0 |
| 452438.0 | 8.0 |
| 454149.0 | 32.0 |
| 457055.0 | 12201.0 |
| 457346.0 | 510.0 |
| 459502.0 | 19.0 |
| 460539.0 | 159.0 |
| 460575.0 | 8.0 |
| 461529.0 | 46.0 |
| 462946.0 | 177.0 |
| 463701.0 | 18690.0 |
| 464001.0 | 450.0 |
| 465341.0 | 670.0 |
| 465616.0 | 299.0 |
| 466611.0 | 256.0 |
| 466647.0 | 33.0 |
| 467305.0 | 171.0 |
| 467649.0 | 217.0 |
| 468677.0 | 171.0 |
| 470160.0 | 7.0 |
| 470742.0 | 361.0 |
| 471158.0 | 173.0 |
| 471573.0 | 225.0 |
| 471912.0 | 255.0 |
| 472377.0 | 91.0 |
| 473514.0 | 102.0 |
| 475029.0 | 214.0 |
| 475452.0 | 1096.0 |
| 475477.0 | 55.0 |
| 476081.0 | 3.0 |
| 477082.0 | 194.0 |
| 477170.0 | 190.0 |
| 477989.0 | 84.0 |
| 478847.0 | 131.0 |
| 478909.0 | 88.0 |
| 481856.0 | 205.0 |
| 481931.0 | 85.0 |
| 482001.0 | 90.0 |
| 483745.0 | 212.0 |
| 484685.0 | 10.0 |
| 486247.0 | 26.0 |
| 487239.0 | 2.0 |
| 487318.0 | 191.0 |
| 488171.0 | 8.0 |
| 488836.0 | 356.0 |
| 489073.0 | 2.0 |
| 489370.0 | 336.0 |
| 491474.0 | 37.0 |
| 492477.0 | 37.0 |
| 493435.0 | 644.0 |
| 493920.0 | 36.0 |
| 494156.0 | 153.0 |
| 494581.0 | 1486.0 |
| 496065.0 | 211.0 |
| 497935.0 | 65.0 |
| 498094.0 | 11.0 |
| 498397.0 | 116.0 |
| 498538.0 | 1873.0 |
| 499060.0 | 42.0 |
| 499192.0 | 5.0 |
| 501594.0 | 124.0 |
| 501802.0 | 196.0 |
| 501990.0 | 91.0 |
| 502390.0 | 6.0 |
| 502403.0 | 50.0 |
| 502742.0 | 349.0 |
| 504035.0 | 979.0 |
| 504250.0 | 348.0 |
| 504256.0 | 343.0 |
| 504273.0 | 221.0 |
| 504375.0 | 541.0 |
| 504596.0 | 43.0 |
| 505463.0 | 72.0 |
| 505743.0 | 4.0 |
| 505820.0 | 537.0 |
| 507563.0 | 552.0 |
| 507926.0 | 7.0 |
| 508737.0 | 206.0 |
| 510112.0 | 246.0 |
| 510774.0 | 84.0 |
| 511383.0 | 3.0 |
| 512977.0 | 183.0 |
| 514897.0 | 297.0 |
| 514899.0 | 1.0 |
| 515122.0 | 172.0 |
| 516324.0 | 277.0 |
| 516538.0 | 94.0 |
| 517132.0 | 70.0 |
| 517915.0 | 52.0 |
| 521089.0 | 69.0 |
| 521173.0 | 66.0 |
| 522192.0 | 152.0 |
| 523171.0 | 767.0 |
| 526013.0 | 47.0 |
| 529780.0 | 827.0 |
| 530691.0 | 1268.0 |
| 531772.0 | 261.0 |
| 532098.0 | 5.0 |
| 532353.0 | 197.0 |
| 532740.0 | 9.0 |
| 533201.0 | 115.0 |
| 533777.0 | 73.0 |
| 533851.0 | 475.0 |
| 534937.0 | 4.0 |
| 536521.0 | 597.0 |
| 537345.0 | 56.0 |
| 537694.0 | 212.0 |
| 537715.0 | 283.0 |
| 540083.0 | 1141.0 |
| 540333.0 | 796.0 |
| 540627.0 | 18.0 |
| 541270.0 | 225.0 |
| 542948.0 | 225.0 |
| 543474.0 | 122.0 |
| 544776.0 | 488.0 |
| 547452.0 | 2241.0 |
| 548154.0 | 9.0 |
| 548451.0 | 128.0 |
| 548779.0 | 132.0 |
| 549480.0 | 46.0 |
| 549513.0 | 113.0 |
| 549710.0 | 30.0 |
| 550604.0 | 109.0 |
| 552426.0 | 202.0 |
| 554584.0 | 8.0 |
| 554664.0 | 259.0 |
| 558056.0 | 90.0 |
| 558934.0 | 128.0 |
| 559316.0 | 2.0 |
| 560508.0 | 124.0 |
| 560718.0 | 113.0 |
| 560768.0 | 146.0 |
| 560879.0 | 122.0 |
| 560941.0 | 48.0 |
| 561455.0 | 86.0 |
| 561551.0 | 31.0 |
| 562299.0 | 112.0 |
| 563982.0 | 392.0 |
| 564655.0 | 233.0 |
| 564827.0 | 209.0 |
| 566199.0 | 2.0 |
| 568119.0 | 15.0 |
| 569237.0 | 82.0 |
| 569522.0 | 3.0 |
| 570792.0 | 140.0 |
| 570856.0 | 125.0 |
| 571042.0 | 17.0 |
| 571639.0 | 129.0 |
| 571953.0 | 496.0 |
| 572731.0 | 105.0 |
| 573470.0 | 244.0 |
| 574382.0 | 201.0 |
| 574499.0 | 82.0 |
| 574614.0 | 418.0 |
| 574687.0 | 59.0 |
| 575879.0 | 2.0 |
| 575948.0 | 24.0 |
| 576265.0 | 55.0 |
| 576764.0 | 5.0 |
| 577980.0 | 137.0 |
| 579730.0 | 1052.0 |
| 580819.0 | 1.0 |
| 585858.0 | 313.0 |
| 585935.0 | 7.0 |
| 588168.0 | 61.0 |
| 591134.0 | 254.0 |
| 591549.0 | 17.0 |
| 591722.0 | 588.0 |
| 592016.0 | 490.0 |
| 594242.0 | 48.0 |
| 594590.0 | 570.0 |
| 596911.0 | 673.0 |
| 597264.0 | 1794.0 |
| 600315.0 | 86.0 |
| 600579.0 | 1.0 |
| 602144.0 | 67.0 |
| 602315.0 | 95.0 |
| 602532.0 | 118.0 |
| 602684.0 | 28.0 |
| 603334.0 | 1.0 |
| 603336.0 | 141.0 |
| 603590.0 | 3.0 |
| 605113.0 | 262.0 |
| 606476.0 | 195.0 |
| 606833.0 | 33.0 |
| 608636.0 | 74.0 |
| 609523.0 | 320.0 |
| 609904.0 | 240.0 |
| 610062.0 | 2.0 |
| 611595.0 | 53.0 |
| 611913.0 | 37.0 |
| 612918.0 | 360.0 |
| 613633.0 | 123.0 |
| 614058.0 | 219.0 |
| 614275.0 | 25.0 |
| 615262.0 | 125.0 |
| 615847.0 | 124.0 |
| 616268.0 | 859.0 |
| 618314.0 | 24.0 |
| 618642.0 | 2.0 |
| 619315.0 | 302.0 |
| 619750.0 | 522.0 |
| 619984.0 | 20.0 |
| 620957.0 | 129.0 |
| 621230.0 | 11.0 |
| 621616.0 | 31.0 |
| 622187.0 | 18.0 |
| 622423.0 | 100.0 |
| 625251.0 | 56.0 |
| 626557.0 | 341.0 |
| 627759.0 | 576.0 |
| 628363.0 | 317.0 |
| 629041.0 | 10.0 |
| 629870.0 | 230.0 |
| 631299.0 | 45.0 |
| 633147.0 | 2.0 |
| 634513.0 | 77.0 |
| 635438.0 | 5.0 |
| 636774.0 | 19.0 |
| 637270.0 | 52.0 |
| 637374.0 | 533.0 |
| 638109.0 | 403.0 |
| 640389.0 | 90.0 |
| 640809.0 | 182.0 |
| 641442.0 | 26.0 |
| 642324.0 | 167.0 |
| 642696.0 | 54.0 |
| 643128.0 | 38.0 |
| 644366.0 | 9.0 |
| 644688.0 | 175.0 |
| 644779.0 | 221.0 |
| 648056.0 | 1214.0 |
| 648470.0 | 915.0 |
| 649009.0 | 121.0 |
| 650160.0 | 35.0 |
| 654219.0 | 2318.0 |
| 656074.0 | 1.0 |
| 656178.0 | 96.0 |
| 656222.0 | 29.0 |
| 656706.0 | 1255.0 |
| 659002.0 | 450.0 |
| 661077.0 | 87.0 |
| 662584.0 | 216.0 |
| 662851.0 | 177.0 |
| 664903.0 | 5.0 |
| 665239.0 | 454.0 |
| 666734.0 | 9.0 |
| 667371.0 | 525.0 |
| 669373.0 | 182.0 |
| 670701.0 | 6.0 |
| 672572.0 | 181.0 |
| 673160.0 | 308.0 |
| 673673.0 | 282.0 |
| 674751.0 | 65.0 |
| 674934.0 | 295.0 |
| 676823.0 | 15.0 |
| 679133.0 | 10.0 |
| 681813.0 | 77.0 |
| 683982.0 | 6.0 |
| 685337.0 | 6.0 |
| 685448.0 | 876.0 |
| 685552.0 | 93.0 |
| 686137.0 | 41.0 |
| 689617.0 | 430.0 |
| 690915.0 | 500.0 |
| 691878.0 | 4.0 |
| 692352.0 | 162.0 |
| 692791.0 | 24.0 |
| 693125.0 | 32.0 |
| 693977.0 | 29.0 |
| 694277.0 | 35.0 |
| 694740.0 | 798.0 |
| 696502.0 | 132.0 |
| 696606.0 | 301.0 |
| 696656.0 | 535.0 |
| 697656.0 | 295.0 |
| 699027.0 | 9.0 |
| 701239.0 | 55.0 |
| 702659.0 | 143.0 |
| 705243.0 | 289.0 |
| 705526.0 | 303.0 |
| 706407.0 | 21.0 |
| 707203.0 | 97.0 |
| 708004.0 | 45.0 |
| 708428.0 | 7.0 |
| 709058.0 | 11.0 |
| 710050.0 | 1525.0 |
| 712475.0 | 80.0 |
| 713598.0 | 25.0 |
| 713640.0 | 162.0 |
| 713760.0 | 341.0 |
| 714896.0 | 537.0 |
| 715070.0 | 165.0 |
| 715217.0 | 225.0 |
| 717926.0 | 13.0 |
| 719508.0 | 392.0 |
| 719978.0 | 58.0 |
| 720168.0 | 39.0 |
| 722201.0 | 4.0 |
| 722475.0 | 45.0 |
| 723419.0 | 7.0 |
| 724381.0 | 85.0 |
| 724820.0 | 131.0 |
| 725111.0 | 112.0 |
| 725846.0 | 5.0 |
| 726938.0 | 23.0 |
| 728517.0 | 29.0 |
| 728705.0 | 175.0 |
| 729093.0 | 12.0 |
| 730132.0 | 9.0 |
| 730822.0 | 19.0 |
| 731740.0 | 390.0 |
| 732577.0 | 25.0 |
| 733930.0 | 29.0 |
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
display(g.degrees)
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksViewe9e5c21")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksViewe9e5c21) ,min_max AS (SELECT `degree`,(SELECT MAX(`degree`) FROM q) `target_column_max`,(SELECT MIN(`degree`) FROM q) `target_column_min` FROM q) ,histogram_meta AS (SELECT `degree`,`target_column_min` `min_value`,IF(`target_column_max` = `target_column_min`,`target_column_max` + 1,`target_column_max`) `max_value`,(`target_column_max` - `target_column_min`) / 300 `step` FROM min_max) SELECT IF(ISNULL(`degree`),NULL,LEAST(WIDTH_BUCKET(`degree`,`min_value`,`max_value`,300),300)) `degree_BIN`,FIRST(`min_value` + ((IF(ISNULL(`degree`),NULL,LEAST(WIDTH_BUCKET(`degree`,`min_value`,`max_value`,300),300)) - 1) * `step`)) `degree_BIN_LOWER_BOUND`,FIRST(`step`) `degree_BIN_STEP`,COUNT(`degree`) `COUNT` FROM histogram_meta GROUP BY `degree_BIN`"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksViewe9e5c21")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
| degree_BIN | degree_BIN_LOWER_BOUND | degree_BIN_STEP | COUNT |
|---|---|---|---|
| 7.0 | 27614.519999999997 | 4602.253333333333 | 137.0 |
| 32.0 | 142670.85333333333 | 4602.253333333333 | 1.0 |
| 6.0 | 23012.266666666666 | 4602.253333333333 | 124.0 |
| 9.0 | 36819.026666666665 | 4602.253333333333 | 50.0 |
| 27.0 | 119659.58666666666 | 4602.253333333333 | 2.0 |
| 17.0 | 73637.05333333333 | 4602.253333333333 | 9.0 |
| 5.0 | 18410.013333333332 | 4602.253333333333 | 231.0 |
| 1.0 | 1.0 | 4602.253333333333 | 1.6848122e7 |
| 3.0 | 9205.506666666666 | 4602.253333333333 | 831.0 |
| 8.0 | 32216.77333333333 | 4602.253333333333 | 63.0 |
| 35.0 | 156477.6133333333 | 4602.253333333333 | 6.0 |
| 2.0 | 4603.253333333333 | 4602.253333333333 | 3664.0 |
| 4.0 | 13807.759999999998 | 4602.253333333333 | 357.0 |
| 18.0 | 78239.30666666666 | 4602.253333333333 | 8.0 |
| 21.0 | 92046.06666666667 | 4602.253333333333 | 4.0 |
| 16.0 | 69034.8 | 4602.253333333333 | 21.0 |
| 10.0 | 41421.28 | 4602.253333333333 | 35.0 |
| 12.0 | 50625.78666666667 | 4602.253333333333 | 19.0 |
| 11.0 | 46023.53333333333 | 4602.253333333333 | 23.0 |
| 15.0 | 64432.54666666666 | 4602.253333333333 | 11.0 |
| 39.0 | 174886.62666666665 | 4602.253333333333 | 1.0 |
| 96.0 | 437215.06666666665 | 4602.253333333333 | 1.0 |
| 13.0 | 55228.03999999999 | 4602.253333333333 | 20.0 |
| 14.0 | 59830.29333333333 | 4602.253333333333 | 8.0 |
| 47.0 | 211704.65333333332 | 4602.253333333333 | 2.0 |
| 19.0 | 82841.56 | 4602.253333333333 | 13.0 |
| 87.0 | 395794.7866666666 | 4602.253333333333 | 1.0 |
| 28.0 | 124261.84 | 4602.253333333333 | 3.0 |
| 120.0 | 547669.1466666666 | 4602.253333333333 | 1.0 |
| 300.0 | 1376074.7466666666 | 4602.253333333333 | 1.0 |
| 44.0 | 197897.8933333333 | 4602.253333333333 | 3.0 |
| 73.0 | 331363.24 | 4602.253333333333 | 1.0 |
| 25.0 | 110455.07999999999 | 4602.253333333333 | 5.0 |
| 20.0 | 87443.81333333332 | 4602.253333333333 | 6.0 |
| 24.0 | 105852.82666666666 | 4602.253333333333 | 4.0 |
| 31.0 | 138068.6 | 4602.253333333333 | 2.0 |
| 106.0 | 483237.6 | 4602.253333333333 | 1.0 |
| 67.0 | 303749.72 | 4602.253333333333 | 1.0 |
| 22.0 | 96648.31999999999 | 4602.253333333333 | 4.0 |
| 63.0 | 285340.70666666667 | 4602.253333333333 | 2.0 |
| 41.0 | 184091.13333333333 | 4602.253333333333 | 2.0 |
| 37.0 | 165682.12 | 4602.253333333333 | 1.0 |
| 23.0 | 101250.57333333333 | 4602.253333333333 | 4.0 |
| 26.0 | 115057.33333333333 | 4602.253333333333 | 5.0 |
| 33.0 | 147273.10666666666 | 4602.253333333333 | 4.0 |
| 43.0 | 193295.63999999998 | 4602.253333333333 | 1.0 |
| 259.0 | 1187382.3599999999 | 4602.253333333333 | 1.0 |
| 78.0 | 354374.50666666665 | 4602.253333333333 | 1.0 |
| 52.0 | 234715.91999999998 | 4602.253333333333 | 1.0 |
| 79.0 | 358976.76 | 4602.253333333333 | 1.0 |
| 54.0 | 243920.42666666667 | 4602.253333333333 | 1.0 |
| 55.0 | 248522.68 | 4602.253333333333 | 1.0 |
| 60.0 | 271533.94666666666 | 4602.253333333333 | 1.0 |
| 38.0 | 170284.37333333332 | 4602.253333333333 | 1.0 |
| 77.0 | 349772.2533333333 | 4602.253333333333 | 1.0 |
| 202.0 | 925053.9199999999 | 4602.253333333333 | 1.0 |
| 51.0 | 230113.66666666666 | 4602.253333333333 | 1.0 |
| 102.0 | 464828.58666666667 | 4602.253333333333 | 1.0 |
| 75.0 | 340567.74666666664 | 4602.253333333333 | 1.0 |
| 114.0 | 520055.62666666665 | 4602.253333333333 | 1.0 |
| 92.0 | 418806.0533333333 | 4602.253333333333 | 1.0 |
| 48.0 | 216306.90666666665 | 4602.253333333333 | 1.0 |
| 64.0 | 289942.95999999996 | 4602.253333333333 | 1.0 |
| 36.0 | 161079.86666666667 | 4602.253333333333 | 1.0 |
| 45.0 | 202500.14666666667 | 4602.253333333333 | 1.0 |
| 56.0 | 253124.93333333332 | 4602.253333333333 | 1.0 |
| 49.0 | 220909.15999999997 | 4602.253333333333 | 1.0 |
| 59.0 | 266931.6933333333 | 4602.253333333333 | 1.0 |
| 40.0 | 179488.88 | 4602.253333333333 | 1.0 |
| 98.0 | 446419.5733333333 | 4602.253333333333 | 1.0 |
| 46.0 | 207102.4 | 4602.253333333333 | 1.0 |
| 91.0 | 414203.8 | 4602.253333333333 | 1.0 |
// Average out-degree
g.outDegrees.select(avg("outDegree")).show()
+-----------------+
| avg(outDegree)|
+-----------------+
|36.06275549331961|
+-----------------+
display(g.outDegrees)
| id | outDegree |
|---|---|
| 415295.0 | 168.0 |
| 1423075.0 | 12.0 |
| 1.4588403e7 | 257.0 |
| 1.6477296e7 | 254.0 |
| 1821508.0 | 283.0 |
| 38422.0 | 826.0 |
| 885771.0 | 110.0 |
| 1219340.0 | 421.0 |
| 1645.0 | 827.0 |
| 1.0228966e7 | 323.0 |
| 1.1089309e7 | 297.0 |
| 2.2812876e7 | 255.0 |
| 4.8503188e7 | 115.0 |
| 6.829176e7 | 426.0 |
| 268622.0 | 355.0 |
| 1129601.0 | 163.0 |
| 3.1469655e7 | 108.0 |
| 3.4787877e7 | 50.0 |
| 1515726.0 | 309.0 |
| 2956898.0 | 239.0 |
| 188488.0 | 169.0 |
| 1488264.0 | 228.0 |
| 7502362.0 | 218.0 |
| 162296.0 | 547.0 |
| 526941.0 | 65.0 |
| 2.2648747e7 | 267.0 |
| 1776082.0 | 569.0 |
| 2309648.0 | 152.0 |
| 2752810.0 | 208.0 |
| 5738975.0 | 286.0 |
| 6745265.0 | 292.0 |
| 2.1099282e7 | 383.0 |
| 2.3972061e7 | 1.0 |
| 2.9899042e7 | 1109.0 |
| 4.7115212e7 | 241.0 |
| 5.2348002e7 | 85.0 |
| 5.6434139e7 | 525.0 |
| 6.8906308e7 | 179.0 |
| 67376.0 | 642.0 |
| 514006.0 | 100.0 |
| 870928.0 | 436.0 |
| 1586332.0 | 63.0 |
| 7284396.0 | 357.0 |
| 8192325.0 | 122.0 |
| 2.3753579e7 | 127.0 |
| 2.6519707e7 | 437.0 |
| 3.0139976e7 | 669.0 |
| 3.6524166e7 | 20.0 |
| 3.7678014e7 | 428.0 |
| 4.0452915e7 | 206.0 |
| 4.3359275e7 | 15.0 |
| 4.6938655e7 | 83.0 |
| 5.1052358e7 | 457.0 |
| 5.2715102e7 | 23.0 |
| 5.2730779e7 | 15.0 |
| 6.8128916e7 | 107.0 |
| 6.9051934e7 | 77.0 |
| 8592.0 | 577.0 |
| 476548.0 | 233.0 |
| 1.320224e7 | 12.0 |
| 173059.0 | 161.0 |
| 4.1173622e7 | 237.0 |
| 3.1515612e7 | 217.0 |
| 3.5455251e7 | 166.0 |
| 103902.0 | 388.0 |
| 188834.0 | 769.0 |
| 1926240.0 | 234.0 |
| 2103368.0 | 182.0 |
| 2188048.0 | 606.0 |
| 3063510.0 | 723.0 |
| 3546338.0 | 140.0 |
| 1.2689594e7 | 35.0 |
| 2.0876254e7 | 128.0 |
| 2.4642604e7 | 99.0 |
| 4.5364376e7 | 17.0 |
| 4.7267734e7 | 406.0 |
| 4.8231358e7 | 694.0 |
| 5.8331921e7 | 300.0 |
| 6.6455589e7 | 787.0 |
| 6466.0 | 1180.0 |
| 43935.0 | 214.0 |
| 1.1310691e7 | 129.0 |
| 3225985.0 | 109.0 |
| 15846.0 | 1039.0 |
| 164603.0 | 444.0 |
| 323084.0 | 351.0 |
| 6198744.0 | 138.0 |
| 2.0238168e7 | 1269.0 |
| 5.2563183e7 | 14.0 |
| 143737.0 | 277.0 |
| 171723.0 | 706.0 |
| 237019.0 | 925.0 |
| 300825.0 | 518.0 |
| 385313.0 | 640.0 |
| 413073.0 | 260.0 |
| 1022960.0 | 190.0 |
| 1055571.0 | 337.0 |
| 1335935.0 | 70.0 |
| 1907741.0 | 1129.0 |
| 2568262.0 | 135.0 |
| 2662089.0 | 129.0 |
| 2701542.0 | 80.0 |
| 2735971.0 | 292.0 |
| 2834157.0 | 113.0 |
| 3151933.0 | 152.0 |
| 3662282.0 | 131.0 |
| 3836992.0 | 88.0 |
| 4581810.0 | 140.0 |
| 5572300.0 | 245.0 |
| 5590614.0 | 105.0 |
| 5803447.0 | 123.0 |
| 9060536.0 | 171.0 |
| 1.2369322e7 | 24.0 |
| 1.7556259e7 | 132.0 |
| 2.0840639e7 | 236.0 |
| 2.5223952e7 | 387.0 |
| 2.5340857e7 | 408.0 |
| 2.6444169e7 | 272.0 |
| 2.6912066e7 | 737.0 |
| 2.7294375e7 | 203.0 |
| 2.8034822e7 | 37.0 |
| 3.075865e7 | 69.0 |
| 3.2189993e7 | 45.0 |
| 3.3478994e7 | 379.0 |
| 3.4240971e7 | 434.0 |
| 3.7467237e7 | 122.0 |
| 3.9449587e7 | 547.0 |
| 4.1941194e7 | 227.0 |
| 4.2474208e7 | 501.0 |
| 4.3973295e7 | 69.0 |
| 4.4209881e7 | 68.0 |
| 4.5076922e7 | 122.0 |
| 4.7469008e7 | 272.0 |
| 4.8675674e7 | 96.0 |
| 5.1427632e7 | 54.0 |
| 5.2505413e7 | 67.0 |
| 5.5084805e7 | 81.0 |
| 5.6296165e7 | 124.0 |
| 5.8308126e7 | 41.0 |
| 6.0603911e7 | 2135.0 |
| 6.4357378e7 | 568.0 |
| 6.4504315e7 | 526.0 |
| 6.6332401e7 | 106.0 |
| 4953963.0 | 87.0 |
| 399735.0 | 898.0 |
| 5645664.0 | 97.0 |
| 3.0874241e7 | 211.0 |
| 3.9320724e7 | 121.0 |
| 3.966069e7 | 471.0 |
| 5.9993595e7 | 320.0 |
| 6.3041167e7 | 83.0 |
| 1.3016057e7 | 43.0 |
| 6.2701997e7 | 399.0 |
| 243022.0 | 135.0 |
| 1138189.0 | 296.0 |
| 8850418.0 | 120.0 |
| 5.3095247e7 | 104.0 |
| 5.4288473e7 | 138.0 |
| 2.942e7 | 62.0 |
| 3.1433025e7 | 28.0 |
| 3.2801696e7 | 329.0 |
| 564996.0 | 409.0 |
| 5984655.0 | 118.0 |
| 6270639.0 | 67.0 |
| 8405617.0 | 84.0 |
| 3.8078804e7 | 752.0 |
| 9101110.0 | 380.0 |
| 2.8729822e7 | 152.0 |
| 3.3778906e7 | 22.0 |
| 4.0202881e7 | 126.0 |
| 4.0349364e7 | 127.0 |
| 276436.0 | 1596.0 |
| 1681220.0 | 397.0 |
| 2.6260602e7 | 293.0 |
| 2.7536113e7 | 1.0 |
| 2.0778386e7 | 490.0 |
| 24347.0 | 752.0 |
| 439650.0 | 739.0 |
| 3873186.0 | 294.0 |
| 9550267.0 | 302.0 |
| 5.5158653e7 | 534.0 |
| 1.2164716e7 | 458.0 |
| 6.5924729e7 | 113.0 |
| 11141.0 | 976.0 |
| 15790.0 | 1122.0 |
| 19530.0 | 894.0 |
| 28146.0 | 1098.0 |
| 35820.0 | 430.0 |
| 2.5678877e7 | 103.0 |
| 49308.0 | 366.0 |
| 167071.0 | 216.0 |
| 559572.0 | 29.0 |
| 958971.0 | 299.0 |
| 1368886.0 | 21.0 |
| 1.5429072e7 | 348.0 |
| 2.389211e7 | 101.0 |
| 2.9451855e7 | 18.0 |
| 3.237948e7 | 206.0 |
| 4.901959e7 | 16.0 |
| 5.0016782e7 | 15.0 |
| 156363.0 | 470.0 |
| 1574128.0 | 153.0 |
| 6405128.0 | 324.0 |
| 2.8173346e7 | 412.0 |
| 2.8523058e7 | 399.0 |
| 343134.0 | 1021.0 |
| 1.2862317e7 | 572.0 |
| 441584.0 | 304.0 |
| 1550943.0 | 310.0 |
| 2802647.0 | 83.0 |
| 5502442.0 | 276.0 |
| 5503022.0 | 278.0 |
| 5509486.0 | 274.0 |
| 8672100.0 | 1.0 |
| 1.2480106e7 | 195.0 |
| 1.2990186e7 | 47.0 |
| 2.0309786e7 | 50.0 |
| 2.1958877e7 | 754.0 |
| 2.7819429e7 | 204.0 |
| 4.0515139e7 | 60.0 |
| 4.6856482e7 | 48.0 |
| 5.5159079e7 | 13.0 |
| 18866.0 | 539.0 |
| 7.1958607e7 | 31.0 |
| 351369.0 | 74.0 |
| 5140924.0 | 31.0 |
| 68202.0 | 583.0 |
| 509779.0 | 139.0 |
| 550422.0 | 547.0 |
| 871796.0 | 397.0 |
| 2498753.0 | 41.0 |
| 3921649.0 | 223.0 |
| 3936762.0 | 123.0 |
| 4877470.0 | 115.0 |
| 5248376.0 | 65.0 |
| 5813421.0 | 165.0 |
| 6137208.0 | 123.0 |
| 6310856.0 | 111.0 |
| 6318789.0 | 46.0 |
| 6716087.0 | 92.0 |
| 6716361.0 | 82.0 |
| 7266347.0 | 598.0 |
| 7763524.0 | 83.0 |
| 7778418.0 | 38.0 |
| 7809287.0 | 44.0 |
| 7872614.0 | 105.0 |
| 7899119.0 | 71.0 |
| 7903825.0 | 63.0 |
| 7913530.0 | 100.0 |
| 7917523.0 | 44.0 |
| 7918098.0 | 46.0 |
| 8075116.0 | 56.0 |
| 8075258.0 | 56.0 |
| 8075307.0 | 55.0 |
| 9054715.0 | 159.0 |
| 1.0913301e7 | 17.0 |
| 1.3810067e7 | 228.0 |
| 1.8446368e7 | 108.0 |
| 1.9165559e7 | 127.0 |
| 1.997149e7 | 40.0 |
| 2.0014525e7 | 35.0 |
| 2.0294228e7 | 24.0 |
| 2.0341054e7 | 33.0 |
| 2.0515428e7 | 28.0 |
| 2.0758603e7 | 32.0 |
| 2.2450173e7 | 126.0 |
| 2.3519635e7 | 88.0 |
| 2.3957751e7 | 325.0 |
| 2.7846289e7 | 73.0 |
| 3.0223416e7 | 141.0 |
| 3.0268044e7 | 137.0 |
| 3.0926856e7 | 637.0 |
| 3.0965494e7 | 632.0 |
| 3.1984196e7 | 36.0 |
| 3.2281209e7 | 136.0 |
| 3.3110291e7 | 141.0 |
| 3.3256354e7 | 774.0 |
| 4.5504768e7 | 168.0 |
| 4.5695467e7 | 39.0 |
| 4.9151368e7 | 248.0 |
| 4.9749249e7 | 107.0 |
| 5.2467559e7 | 104.0 |
| 5.3085969e7 | 31.0 |
| 5.4157633e7 | 40.0 |
| 5.5781425e7 | 72.0 |
| 5.5854652e7 | 387.0 |
| 5.6530307e7 | 220.0 |
| 5.733475e7 | 307.0 |
| 5.7913592e7 | 211.0 |
| 5.8907764e7 | 305.0 |
| 6.102231e7 | 136.0 |
| 6.4589877e7 | 36.0 |
| 6.6223553e7 | 29.0 |
| 6.760169e7 | 20.0 |
| 6.8265437e7 | 192.0 |
| 6.9187423e7 | 122.0 |
| 1.4211841e7 | 1.0 |
| 761292.0 | 535.0 |
| 1013659.0 | 500.0 |
| 1704446.0 | 32.0 |
| 1866464.0 | 340.0 |
| 1907717.0 | 132.0 |
| 2154804.0 | 526.0 |
| 2568574.0 | 186.0 |
| 2743650.0 | 66.0 |
| 4851597.0 | 182.0 |
| 7613573.0 | 112.0 |
| 7935644.0 | 810.0 |
| 9408939.0 | 485.0 |
| 1.0095304e7 | 43.0 |
| 1.1979107e7 | 361.0 |
| 1.2508951e7 | 14.0 |
| 1.4486253e7 | 27.0 |
| 1.625894e7 | 89.0 |
| 1.6321994e7 | 26.0 |
| 1.9437772e7 | 105.0 |
| 1.9937765e7 | 33.0 |
| 2.0656951e7 | 156.0 |
| 2.438898e7 | 43.0 |
| 2.6108187e7 | 274.0 |
| 2.6719404e7 | 43.0 |
| 2.7984685e7 | 28.0 |
| 2.8097046e7 | 45.0 |
| 2.9474697e7 | 195.0 |
| 3.0102024e7 | 218.0 |
| 3.0746523e7 | 15.0 |
| 3.146604e7 | 441.0 |
| 3.1905754e7 | 17.0 |
| 3.3743217e7 | 27.0 |
| 3.5226968e7 | 72.0 |
| 3.6281191e7 | 132.0 |
| 3.7168977e7 | 341.0 |
| 3.7221001e7 | 92.0 |
| 3.750528e7 | 8.0 |
| 3.860385e7 | 85.0 |
| 3.9028189e7 | 4.0 |
| 3.9689e7 | 13.0 |
| 4.2341789e7 | 201.0 |
| 4.7143428e7 | 62.0 |
| 4.7370009e7 | 276.0 |
| 4.7945338e7 | 32.0 |
| 4.9072034e7 | 20.0 |
| 4.9169559e7 | 38.0 |
| 4.9642551e7 | 107.0 |
| 5.0648923e7 | 77.0 |
| 5.1467184e7 | 28.0 |
| 5.2660471e7 | 76.0 |
| 5.4460351e7 | 11.0 |
| 5.4488624e7 | 56.0 |
| 5.4579514e7 | 119.0 |
| 5.6311947e7 | 150.0 |
| 5.6348151e7 | 32.0 |
| 6.0727429e7 | 205.0 |
| 6.2079651e7 | 765.0 |
| 6.2499558e7 | 325.0 |
| 6.277709e7 | 36.0 |
| 6.2841642e7 | 36.0 |
| 6.4652328e7 | 117.0 |
| 6.4988511e7 | 19.0 |
| 6.5529946e7 | 16.0 |
| 6.6393156e7 | 54.0 |
| 6.8607891e7 | 31.0 |
| 6.9169235e7 | 169.0 |
| 8876929.0 | 720.0 |
| 1.4496904e7 | 80.0 |
| 2.3329907e7 | 943.0 |
| 2.0225783e7 | 273.0 |
| 2352987.0 | 698.0 |
| 1.4533431e7 | 392.0 |
| 1558717.0 | 251.0 |
| 2666510.0 | 636.0 |
| 7154148.0 | 797.0 |
| 1919377.0 | 191.0 |
| 3885619.0 | 27.0 |
| 2.1018883e7 | 60.0 |
| 2.5548229e7 | 16.0 |
| 352633.0 | 16.0 |
| 6831930.0 | 116.0 |
| 7001959.0 | 131.0 |
| 7065466.0 | 8.0 |
| 7352800.0 | 106.0 |
| 7900775.0 | 34.0 |
| 7901741.0 | 41.0 |
| 7917496.0 | 40.0 |
| 7921244.0 | 32.0 |
| 1.0011982e7 | 119.0 |
| 1.583696e7 | 340.0 |
| 2.1641437e7 | 333.0 |
| 2.59941e7 | 463.0 |
| 2.6361288e7 | 93.0 |
| 2.7846415e7 | 71.0 |
| 3.0826845e7 | 146.0 |
| 3.095863e7 | 616.0 |
| 3.2918059e7 | 103.0 |
| 3.3403565e7 | 589.0 |
| 4.2024949e7 | 327.0 |
| 4.5505713e7 | 168.0 |
| 291112.0 | 725.0 |
| 1096019.0 | 286.0 |
| 1403376.0 | 223.0 |
| 1897562.0 | 392.0 |
| 4655521.0 | 134.0 |
| 6605995.0 | 274.0 |
| 1.8002411e7 | 146.0 |
| 3.0175461e7 | 87.0 |
| 3.3739539e7 | 28.0 |
| 3.4940971e7 | 702.0 |
| 3.8190666e7 | 25.0 |
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| 4.2415715e7 | 19.0 |
| 4.6665667e7 | 27.0 |
| 4.7644402e7 | 330.0 |
| 4.9040329e7 | 18.0 |
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| 5.782147e7 | 90.0 |
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| 6.8724737e7 | 26.0 |
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| 7.0961308e7 | 9.0 |
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| 6.781975e7 | 1372.0 |
| 1765281.0 | 285.0 |
| 1.6096578e7 | 218.0 |
| 1.7004063e7 | 62.0 |
| 2.228941e7 | 61.0 |
| 2.4931991e7 | 59.0 |
| 6.2664637e7 | 2596.0 |
| 1617520.0 | 24.0 |
| 2.1231964e7 | 142.0 |
| 2.9714573e7 | 115.0 |
| 3.1205078e7 | 43.0 |
| 3.7763802e7 | 116.0 |
| 5.160308e7 | 17.0 |
| 6.3045081e7 | 68.0 |
| 343570.0 | 350.0 |
| 1066848.0 | 81.0 |
| 2452360.0 | 182.0 |
| 3675155.0 | 65.0 |
| 2.269203e7 | 21.0 |
| 2.2773422e7 | 393.0 |
| 2.8253567e7 | 206.0 |
| 3.1176966e7 | 693.0 |
| 3.1351322e7 | 186.0 |
| 3.1861665e7 | 88.0 |
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
display(g.inDegrees)
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksViewa10e7f7")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksViewa10e7f7) ,min_max AS (SELECT `inDegree`,(SELECT MAX(`inDegree`) FROM q) `target_column_max`,(SELECT MIN(`inDegree`) FROM q) `target_column_min` FROM q) ,histogram_meta AS (SELECT `inDegree`,`target_column_min` `min_value`,IF(`target_column_max` = `target_column_min`,`target_column_max` + 1,`target_column_max`) `max_value`,(`target_column_max` - `target_column_min`) / 300 `step` FROM min_max) SELECT IF(ISNULL(`inDegree`),NULL,LEAST(WIDTH_BUCKET(`inDegree`,`min_value`,`max_value`,300),300)) `inDegree_BIN`,FIRST(`min_value` + ((IF(ISNULL(`inDegree`),NULL,LEAST(WIDTH_BUCKET(`inDegree`,`min_value`,`max_value`,300),300)) - 1) * `step`)) `inDegree_BIN_LOWER_BOUND`,FIRST(`step`) `inDegree_BIN_STEP`,COUNT(`inDegree`) `COUNT` FROM histogram_meta GROUP BY `inDegree_BIN`"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksViewa10e7f7")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
| inDegree_BIN | inDegree_BIN_LOWER_BOUND | inDegree_BIN_STEP | COUNT |
|---|---|---|---|
| 7.0 | 27603.0 | 4600.333333333333 | 136.0 |
| 39.0 | 174813.66666666666 | 4600.333333333333 | 1.0 |
| 6.0 | 23002.666666666664 | 4600.333333333333 | 107.0 |
| 9.0 | 36803.666666666664 | 4600.333333333333 | 49.0 |
| 27.0 | 119609.66666666666 | 4600.333333333333 | 1.0 |
| 5.0 | 18402.333333333332 | 4600.333333333333 | 203.0 |
| 1.0 | 1.0 | 4600.333333333333 | 6424767.0 |
| 96.0 | 437032.6666666666 | 4600.333333333333 | 1.0 |
| 3.0 | 9201.666666666666 | 4600.333333333333 | 707.0 |
| 12.0 | 50604.666666666664 | 4600.333333333333 | 19.0 |
| 8.0 | 32203.333333333332 | 4600.333333333333 | 57.0 |
| 11.0 | 46004.33333333333 | 4600.333333333333 | 20.0 |
| 2.0 | 4601.333333333333 | 4600.333333333333 | 2683.0 |
| 4.0 | 13802.0 | 4600.333333333333 | 344.0 |
| 13.0 | 55205.0 | 4600.333333333333 | 18.0 |
| 15.0 | 64405.666666666664 | 4600.333333333333 | 12.0 |
| 20.0 | 87407.33333333333 | 4600.333333333333 | 5.0 |
| 47.0 | 211616.3333333333 | 4600.333333333333 | 2.0 |
| 26.0 | 115009.33333333333 | 4600.333333333333 | 5.0 |
| 22.0 | 96608.0 | 4600.333333333333 | 5.0 |
| 43.0 | 193215.0 | 4600.333333333333 | 3.0 |
| 63.0 | 285221.6666666666 | 4600.333333333333 | 2.0 |
| 33.0 | 147211.66666666666 | 4600.333333333333 | 4.0 |
| 10.0 | 41404.0 | 4600.333333333333 | 35.0 |
| 35.0 | 156412.3333333333 | 4600.333333333333 | 6.0 |
| 14.0 | 59805.33333333333 | 4600.333333333333 | 7.0 |
| 23.0 | 101208.33333333333 | 4600.333333333333 | 4.0 |
| 259.0 | 1186887.0 | 4600.333333333333 | 1.0 |
| 16.0 | 69006.0 | 4600.333333333333 | 20.0 |
| 19.0 | 82807.0 | 4600.333333333333 | 14.0 |
| 25.0 | 110409.0 | 4600.333333333333 | 5.0 |
| 56.0 | 253019.3333333333 | 4600.333333333333 | 1.0 |
| 49.0 | 220817.0 | 4600.333333333333 | 1.0 |
| 18.0 | 78206.66666666666 | 4600.333333333333 | 9.0 |
| 59.0 | 266820.3333333333 | 4600.333333333333 | 1.0 |
| 300.0 | 1375500.6666666665 | 4600.333333333333 | 1.0 |
| 40.0 | 179414.0 | 4600.333333333333 | 2.0 |
| 31.0 | 138011.0 | 4600.333333333333 | 2.0 |
| 79.0 | 358827.0 | 4600.333333333333 | 1.0 |
| 17.0 | 73606.33333333333 | 4600.333333333333 | 8.0 |
| 114.0 | 519838.6666666666 | 4600.333333333333 | 1.0 |
| 91.0 | 414031.0 | 4600.333333333333 | 2.0 |
| 87.0 | 395629.6666666666 | 4600.333333333333 | 1.0 |
| 28.0 | 124209.99999999999 | 4600.333333333333 | 3.0 |
| 120.0 | 547440.6666666666 | 4600.333333333333 | 1.0 |
| 44.0 | 197815.3333333333 | 4600.333333333333 | 2.0 |
| 36.0 | 161012.66666666666 | 4600.333333333333 | 1.0 |
| 54.0 | 243818.66666666666 | 4600.333333333333 | 1.0 |
| 55.0 | 248418.99999999997 | 4600.333333333333 | 1.0 |
| 73.0 | 331225.0 | 4600.333333333333 | 1.0 |
| 60.0 | 271420.6666666666 | 4600.333333333333 | 1.0 |
| 37.0 | 165613.0 | 4600.333333333333 | 2.0 |
| 46.0 | 207016.0 | 4600.333333333333 | 1.0 |
| 24.0 | 105808.66666666666 | 4600.333333333333 | 4.0 |
| 106.0 | 483035.99999999994 | 4600.333333333333 | 1.0 |
| 67.0 | 303623.0 | 4600.333333333333 | 1.0 |
| 41.0 | 184014.3333333333 | 4600.333333333333 | 1.0 |
| 77.0 | 349626.3333333333 | 4600.333333333333 | 1.0 |
| 202.0 | 924667.9999999999 | 4600.333333333333 | 1.0 |
| 51.0 | 230017.66666666666 | 4600.333333333333 | 2.0 |
| 101.0 | 460034.3333333333 | 4600.333333333333 | 1.0 |
| 75.0 | 340425.6666666666 | 4600.333333333333 | 1.0 |
| 32.0 | 142611.3333333333 | 4600.333333333333 | 1.0 |
| 21.0 | 92007.66666666666 | 4600.333333333333 | 3.0 |
| 48.0 | 216216.66666666666 | 4600.333333333333 | 1.0 |
| 64.0 | 289822.0 | 4600.333333333333 | 1.0 |
| 78.0 | 354226.6666666666 | 4600.333333333333 | 1.0 |
| 98.0 | 446233.3333333333 | 4600.333333333333 | 1.0 |
// Top in
display(g.inDegrees.join(g.vertices.withColumnRenamed("id", "v_id"), col("id")===col("v_id"), "left").orderBy(desc("inDegree")))
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
display(g.outDegrees)
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksView1bd969d")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksView1bd969d) ,min_max AS (SELECT `outDegree`,(SELECT MAX(`outDegree`) FROM q) `target_column_max`,(SELECT MIN(`outDegree`) FROM q) `target_column_min` FROM q) ,histogram_meta AS (SELECT `outDegree`,`target_column_min` `min_value`,IF(`target_column_max` = `target_column_min`,`target_column_max` + 1,`target_column_max`) `max_value`,(`target_column_max` - `target_column_min`) / 300 `step` FROM min_max) SELECT IF(ISNULL(`outDegree`),NULL,LEAST(WIDTH_BUCKET(`outDegree`,`min_value`,`max_value`,300),300)) `outDegree_BIN`,FIRST(`min_value` + ((IF(ISNULL(`outDegree`),NULL,LEAST(WIDTH_BUCKET(`outDegree`,`min_value`,`max_value`,300),300)) - 1) * `step`)) `outDegree_BIN_LOWER_BOUND`,FIRST(`step`) `outDegree_BIN_STEP`,COUNT(`outDegree`) `COUNT` FROM histogram_meta GROUP BY `outDegree_BIN`"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksView1bd969d")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
| outDegree_BIN | outDegree_BIN_LOWER_BOUND | outDegree_BIN_STEP | COUNT |
|---|---|---|---|
| 26.0 | 1037.6666666666667 | 41.46666666666667 | 2166.0 |
| 29.0 | 1162.0666666666666 | 41.46666666666667 | 1013.0 |
| 65.0 | 2654.866666666667 | 41.46666666666667 | 28.0 |
| 19.0 | 747.4000000000001 | 41.46666666666667 | 6522.0 |
| 54.0 | 2198.7333333333336 | 41.46666666666667 | 51.0 |
| 22.0 | 871.8000000000001 | 41.46666666666667 | 4277.0 |
| 7.0 | 249.8 | 41.46666666666667 | 134610.0 |
| 34.0 | 1369.4 | 41.46666666666667 | 1166.0 |
| 43.0 | 1742.6000000000001 | 41.46666666666667 | 186.0 |
| 32.0 | 1286.4666666666667 | 41.46666666666667 | 1117.0 |
| 31.0 | 1245.0 | 41.46666666666667 | 1076.0 |
| 39.0 | 1576.7333333333333 | 41.46666666666667 | 581.0 |
| 25.0 | 996.2 | 41.46666666666667 | 2143.0 |
| 6.0 | 208.33333333333334 | 41.46666666666667 | 180244.0 |
| 58.0 | 2364.6 | 41.46666666666667 | 37.0 |
| 107.0 | 4396.466666666667 | 41.46666666666667 | 2.0 |
| 9.0 | 332.73333333333335 | 41.46666666666667 | 73016.0 |
| 27.0 | 1079.1333333333334 | 41.46666666666667 | 2546.0 |
| 52.0 | 2115.8 | 41.46666666666667 | 62.0 |
| 17.0 | 664.4666666666667 | 41.46666666666667 | 11795.0 |
| 41.0 | 1659.6666666666667 | 41.46666666666667 | 259.0 |
| 28.0 | 1120.6000000000001 | 41.46666666666667 | 1308.0 |
| 33.0 | 1327.9333333333334 | 41.46666666666667 | 1615.0 |
| 88.0 | 3608.6000000000004 | 41.46666666666667 | 3.0 |
| 5.0 | 166.86666666666667 | 41.46666666666667 | 257782.0 |
| 1.0 | 1.0 | 41.46666666666667 | 1.3808876e7 |
| 10.0 | 374.20000000000005 | 41.46666666666667 | 52366.0 |
| 44.0 | 1784.0666666666668 | 41.46666666666667 | 148.0 |
| 61.0 | 2489.0 | 41.46666666666667 | 28.0 |
| 3.0 | 83.93333333333334 | 41.46666666666667 | 595738.0 |
| 37.0 | 1493.8000000000002 | 41.46666666666667 | 611.0 |
| 12.0 | 457.1333333333333 | 41.46666666666667 | 33173.0 |
| 55.0 | 2240.2000000000003 | 41.46666666666667 | 44.0 |
| 74.0 | 3028.0666666666666 | 41.46666666666667 | 10.0 |
| 8.0 | 291.26666666666665 | 41.46666666666667 | 93927.0 |
| 11.0 | 415.6666666666667 | 41.46666666666667 | 42598.0 |
| 49.0 | 1991.4 | 41.46666666666667 | 81.0 |
| 35.0 | 1410.8666666666668 | 41.46666666666667 | 800.0 |
| 2.0 | 42.46666666666667 | 41.46666666666667 | 1063177.0 |
| 4.0 | 125.4 | 41.46666666666667 | 359448.0 |
| 13.0 | 498.6 | 41.46666666666667 | 28593.0 |
| 36.0 | 1452.3333333333335 | 41.46666666666667 | 669.0 |
| 75.0 | 3069.5333333333333 | 41.46666666666667 | 12.0 |
| 18.0 | 705.9333333333334 | 41.46666666666667 | 10640.0 |
| 14.0 | 540.0666666666667 | 41.46666666666667 | 21160.0 |
| 21.0 | 830.3333333333334 | 41.46666666666667 | 5358.0 |
| 15.0 | 581.5333333333333 | 41.46666666666667 | 18867.0 |
| 38.0 | 1535.2666666666667 | 41.46666666666667 | 483.0 |
| 82.0 | 3359.8 | 41.46666666666667 | 9.0 |
| 30.0 | 1203.5333333333333 | 41.46666666666667 | 2022.0 |
| 42.0 | 1701.1333333333334 | 41.46666666666667 | 196.0 |
| 90.0 | 3691.5333333333333 | 41.46666666666667 | 1.0 |
| 23.0 | 913.2666666666667 | 41.46666666666667 | 3022.0 |
| 46.0 | 1867.0 | 41.46666666666667 | 114.0 |
| 20.0 | 788.8666666666667 | 41.46666666666667 | 8177.0 |
| 86.0 | 3525.666666666667 | 41.46666666666667 | 7.0 |
| 60.0 | 2447.5333333333333 | 41.46666666666667 | 24.0 |
| 40.0 | 1618.2 | 41.46666666666667 | 354.0 |
| 16.0 | 623.0 | 41.46666666666667 | 15615.0 |
| 53.0 | 2157.266666666667 | 41.46666666666667 | 55.0 |
| 47.0 | 1908.4666666666667 | 41.46666666666667 | 106.0 |
| 24.0 | 954.7333333333333 | 41.46666666666667 | 2567.0 |
| 50.0 | 2032.8666666666668 | 41.46666666666667 | 87.0 |
| 84.0 | 3442.7333333333336 | 41.46666666666667 | 8.0 |
| 95.0 | 3898.866666666667 | 41.46666666666667 | 3.0 |
| 71.0 | 2903.666666666667 | 41.46666666666667 | 14.0 |
| 51.0 | 2074.3333333333335 | 41.46666666666667 | 67.0 |
| 67.0 | 2737.8 | 41.46666666666667 | 19.0 |
| 48.0 | 1949.9333333333334 | 41.46666666666667 | 76.0 |
| 123.0 | 5059.933333333333 | 41.46666666666667 | 1.0 |
| 66.0 | 2696.3333333333335 | 41.46666666666667 | 11.0 |
| 78.0 | 3193.9333333333334 | 41.46666666666667 | 8.0 |
| 45.0 | 1825.5333333333333 | 41.46666666666667 | 116.0 |
| 57.0 | 2323.133333333333 | 41.46666666666667 | 35.0 |
| 62.0 | 2530.4666666666667 | 41.46666666666667 | 17.0 |
| 56.0 | 2281.666666666667 | 41.46666666666667 | 38.0 |
| 59.0 | 2406.0666666666666 | 41.46666666666667 | 30.0 |
| 300.0 | 12399.533333333335 | 41.46666666666667 | 1.0 |
| 93.0 | 3815.9333333333334 | 41.46666666666667 | 6.0 |
| 91.0 | 3733.0 | 41.46666666666667 | 6.0 |
| 113.0 | 4645.266666666666 | 41.46666666666667 | 1.0 |
| 94.0 | 3857.4 | 41.46666666666667 | 5.0 |
| 83.0 | 3401.266666666667 | 41.46666666666667 | 2.0 |
| 77.0 | 3152.4666666666667 | 41.46666666666667 | 3.0 |
| 145.0 | 5972.200000000001 | 41.46666666666667 | 1.0 |
| 108.0 | 4437.933333333333 | 41.46666666666667 | 5.0 |
| 76.0 | 3111.0 | 41.46666666666667 | 9.0 |
| 98.0 | 4023.266666666667 | 41.46666666666667 | 3.0 |
| 63.0 | 2571.9333333333334 | 41.46666666666667 | 9.0 |
| 80.0 | 3276.866666666667 | 41.46666666666667 | 4.0 |
| 69.0 | 2820.7333333333336 | 41.46666666666667 | 9.0 |
| 81.0 | 3318.3333333333335 | 41.46666666666667 | 8.0 |
| 73.0 | 2986.6000000000004 | 41.46666666666667 | 9.0 |
| 68.0 | 2779.266666666667 | 41.46666666666667 | 15.0 |
| 192.0 | 7921.133333333334 | 41.46666666666667 | 1.0 |
| 265.0 | 10948.2 | 41.46666666666667 | 1.0 |
| 70.0 | 2862.2000000000003 | 41.46666666666667 | 18.0 |
| 111.0 | 4562.333333333334 | 41.46666666666667 | 2.0 |
| 110.0 | 4520.866666666667 | 41.46666666666667 | 3.0 |
| 72.0 | 2945.1333333333337 | 41.46666666666667 | 5.0 |
| 102.0 | 4189.133333333333 | 41.46666666666667 | 3.0 |
| 64.0 | 2613.4 | 41.46666666666667 | 17.0 |
| 105.0 | 4313.533333333334 | 41.46666666666667 | 1.0 |
| 121.0 | 4977.0 | 41.46666666666667 | 1.0 |
| 119.0 | 4894.066666666667 | 41.46666666666667 | 3.0 |
| 132.0 | 5433.133333333333 | 41.46666666666667 | 2.0 |
| 171.0 | 7050.333333333334 | 41.46666666666667 | 1.0 |
| 92.0 | 3774.4666666666667 | 41.46666666666667 | 1.0 |
| 97.0 | 3981.8 | 41.46666666666667 | 2.0 |
| 99.0 | 4064.7333333333336 | 41.46666666666667 | 1.0 |
| 193.0 | 7962.6 | 41.46666666666667 | 1.0 |
| 122.0 | 5018.466666666667 | 41.46666666666667 | 2.0 |
| 100.0 | 4106.2 | 41.46666666666667 | 2.0 |
| 134.0 | 5516.066666666667 | 41.46666666666667 | 2.0 |
| 133.0 | 5474.6 | 41.46666666666667 | 2.0 |
| 168.0 | 6925.933333333333 | 41.46666666666667 | 1.0 |
| 116.0 | 4769.666666666667 | 41.46666666666667 | 4.0 |
| 87.0 | 3567.1333333333337 | 41.46666666666667 | 5.0 |
| 104.0 | 4272.066666666667 | 41.46666666666667 | 3.0 |
| 89.0 | 3650.0666666666666 | 41.46666666666667 | 3.0 |
| 106.0 | 4355.0 | 41.46666666666667 | 1.0 |
| 103.0 | 4230.6 | 41.46666666666667 | 1.0 |
| 124.0 | 5101.400000000001 | 41.46666666666667 | 1.0 |
| 101.0 | 4147.666666666667 | 41.46666666666667 | 2.0 |
| 137.0 | 5640.466666666667 | 41.46666666666667 | 1.0 |
| 135.0 | 5557.533333333334 | 41.46666666666667 | 1.0 |
| 114.0 | 4686.733333333334 | 41.46666666666667 | 1.0 |
| 125.0 | 5142.866666666667 | 41.46666666666667 | 1.0 |
| 156.0 | 6428.333333333334 | 41.46666666666667 | 1.0 |
| 85.0 | 3484.2000000000003 | 41.46666666666667 | 2.0 |
| 117.0 | 4811.133333333333 | 41.46666666666667 | 3.0 |
| 127.0 | 5225.8 | 41.46666666666667 | 1.0 |
| 109.0 | 4479.400000000001 | 41.46666666666667 | 2.0 |
| 120.0 | 4935.533333333334 | 41.46666666666667 | 3.0 |
| 148.0 | 6096.6 | 41.46666666666667 | 1.0 |
| 138.0 | 5681.933333333333 | 41.46666666666667 | 1.0 |
| 79.0 | 3235.4 | 41.46666666666667 | 2.0 |
| 163.0 | 6718.6 | 41.46666666666667 | 1.0 |
| 128.0 | 5267.266666666667 | 41.46666666666667 | 1.0 |
| 200.0 | 8252.866666666667 | 41.46666666666667 | 1.0 |
| 126.0 | 5184.333333333334 | 41.46666666666667 | 1.0 |
| 129.0 | 5308.733333333334 | 41.46666666666667 | 1.0 |
| 187.0 | 7713.8 | 41.46666666666667 | 1.0 |
// Top 10 out
display(g.outDegrees.join(g.vertices.withColumnRenamed("id", "v_id"), col("id")===col("v_id"), "left").orderBy(desc("outDegree")))
Article Length
Let's investigate how the article length is distributed among our articles.
g.vertices.stat.approxQuantile("page_len", Array(0.05, 0.25, 0.5, 0.75, 0.95), 0.001)
res14: Array[Double] = Array(580.0, 2023.0, 4081.0, 8439.0, 28182.0)
g.vertices.select(avg("page_len")).show()
+---------------+
| avg(page_len)|
+---------------+
|8293.8936494498|
+---------------+
display(g.vertices.select("page_len"))
| page_len |
|---|
| 86941.0 |
| 5568.0 |
| 120924.0 |
| 115389.0 |
| 12611.0 |
| 17355.0 |
| 5259.0 |
| 38155.0 |
| 61828.0 |
| 62649.0 |
| 38990.0 |
| 2384.0 |
| 57987.0 |
| 21803.0 |
| 27194.0 |
| 198759.0 |
| 9089.0 |
| 172360.0 |
| 1662.0 |
| 47303.0 |
| 14090.0 |
| 971.0 |
| 4632.0 |
| 169316.0 |
| 105966.0 |
| 156265.0 |
| 177759.0 |
| 54296.0 |
| 424.0 |
| 10782.0 |
| 137001.0 |
| 63095.0 |
| 40450.0 |
| 264505.0 |
| 121471.0 |
| 8092.0 |
| 22569.0 |
| 8227.0 |
| 52833.0 |
| 42277.0 |
| 62474.0 |
| 4967.0 |
| 96274.0 |
| 78185.0 |
| 57940.0 |
| 17730.0 |
| 112409.0 |
| 44000.0 |
| 53513.0 |
| 78610.0 |
| 75626.0 |
| 7026.0 |
| 24164.0 |
| 36906.0 |
| 3406.0 |
| 4767.0 |
| 16041.0 |
| 2702.0 |
| 76185.0 |
| 29857.0 |
| 117407.0 |
| 86565.0 |
| 6261.0 |
| 46002.0 |
| 4653.0 |
| 44923.0 |
| 33528.0 |
| 55134.0 |
| 31150.0 |
| 14723.0 |
| 4809.0 |
| 17732.0 |
| 22165.0 |
| 8481.0 |
| 53434.0 |
| 1407.0 |
| 100642.0 |
| 71493.0 |
| 91061.0 |
| 55922.0 |
| 15800.0 |
| 8519.0 |
| 9670.0 |
| 11078.0 |
| 117407.0 |
| 20558.0 |
| 9834.0 |
| 8856.0 |
| 34566.0 |
| 11584.0 |
| 16240.0 |
| 828.0 |
| 1689.0 |
| 2176.0 |
| 3576.0 |
| 22336.0 |
| 5414.0 |
| 8191.0 |
| 19019.0 |
| 4026.0 |
| 36712.0 |
| 40056.0 |
| 18284.0 |
| 3975.0 |
| 69627.0 |
| 14955.0 |
| 7473.0 |
| 56872.0 |
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display(g.vertices.filter(col("page_len")<200000L).select("page_len"))
| page_len |
|---|
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Quite heavy tailed distribution, it is probably a good idea to specify some cut-off value in terms of page length to avoid lookin at very short articles.
Summary
Summarizing the exploration we can conclude that the graph: * consists of over 6M nodes, i.e. articles, * consists of over 600M edges, i.e. links between pages, * is fairly dense, with and edge to vertex ratio close to 100, * has an average degree of around 72, * has a much higher average in-degree than out degree(95 vs 36), meaning that the average article has more articles pointing into it than out. * consists of a lot of short articles, and to reduce the amount of nodes we have to work with it might be a good idea to filter out some shoerter articles.
Further, looking at the distributions of the in/out-degrees we see that the distributions have long tails, indicating that there are some articles with really high degrees which increase the average.
(MAYBE NOT USE THE VIZUALS, THEY SEEM BUGGY)
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
display(g.vertices.select("page_len"))
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksViewa99e330")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksViewa99e330) ,min_max AS (SELECT `page_len`,(SELECT MAX(`page_len`) FROM q) `target_column_max`,(SELECT MIN(`page_len`) FROM q) `target_column_min` FROM q) ,histogram_meta AS (SELECT `page_len`,`target_column_min` `min_value`,IF(`target_column_max` = `target_column_min`,`target_column_max` + 1,`target_column_max`) `max_value`,(`target_column_max` - `target_column_min`) / 100 `step` FROM min_max) SELECT IF(ISNULL(`page_len`),NULL,LEAST(WIDTH_BUCKET(`page_len`,`min_value`,`max_value`,100),100)) `page_len_BIN`,FIRST(`min_value` + ((IF(ISNULL(`page_len`),NULL,LEAST(WIDTH_BUCKET(`page_len`,`min_value`,`max_value`,100),100)) - 1) * `step`)) `page_len_BIN_LOWER_BOUND`,FIRST(`step`) `page_len_BIN_STEP`,COUNT(`page_len`) `COUNT` FROM histogram_meta GROUP BY `page_len_BIN`"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksViewa99e330")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
| page_len_BIN | page_len_BIN_LOWER_BOUND | page_len_BIN_STEP | COUNT |
|---|---|---|---|
| 29.0 | 180024.88 | 6429.46 | 1050.0 |
| 26.0 | 160736.5 | 6429.46 | 980.0 |
| 65.0 | 411485.44 | 6429.46 | 22.0 |
| 19.0 | 115730.28 | 6429.46 | 2892.0 |
| 22.0 | 135018.66 | 6429.46 | 1852.0 |
| 7.0 | 38576.76 | 6429.46 | 46077.0 |
| 34.0 | 212172.18 | 6429.46 | 352.0 |
| 43.0 | 270037.32 | 6429.46 | 150.0 |
| 32.0 | 199313.26 | 6429.46 | 499.0 |
| 25.0 | 154307.04 | 6429.46 | 1163.0 |
| 6.0 | 32147.3 | 6429.46 | 68570.0 |
| 72.0 | 456491.66 | 6429.46 | 5.0 |
| 9.0 | 51435.68 | 6429.46 | 24048.0 |
| 27.0 | 167165.96 | 6429.46 | 946.0 |
| 51.0 | 321473.0 | 6429.46 | 78.0 |
| 17.0 | 102871.36 | 6429.46 | 4027.0 |
| 41.0 | 257178.4 | 6429.46 | 192.0 |
| 33.0 | 205742.72 | 6429.46 | 438.0 |
| 28.0 | 173595.42 | 6429.46 | 919.0 |
| 5.0 | 25717.84 | 6429.46 | 109202.0 |
| 1.0 | 0.0 | 6429.46 | 4367869.0 |
| 10.0 | 57865.14 | 6429.46 | 18762.0 |
| 44.0 | 276466.78 | 6429.46 | 122.0 |
| 3.0 | 12858.92 | 6429.46 | 412198.0 |
| 12.0 | 70724.06 | 6429.46 | 11388.0 |
| 8.0 | 45006.22 | 6429.46 | 33233.0 |
| 49.0 | 308614.08 | 6429.46 | 90.0 |
| 11.0 | 64294.6 | 6429.46 | 14416.0 |
| 35.0 | 218601.64 | 6429.46 | 361.0 |
| 2.0 | 6429.46 | 6429.46 | 1211372.0 |
| 4.0 | 19288.38 | 6429.46 | 194706.0 |
| 13.0 | 77153.52 | 6429.46 | 9188.0 |
| 36.0 | 225031.1 | 6429.46 | 276.0 |
| 18.0 | 109300.82 | 6429.46 | 3339.0 |
| 14.0 | 83582.98 | 6429.46 | 7223.0 |
| 21.0 | 128589.2 | 6429.46 | 2083.0 |
| 59.0 | 372908.68 | 6429.46 | 33.0 |
| 15.0 | 90012.44 | 6429.46 | 5812.0 |
| 38.0 | 237890.02 | 6429.46 | 268.0 |
| 30.0 | 186454.34 | 6429.46 | 767.0 |
| 42.0 | 263607.86 | 6429.46 | 171.0 |
| 23.0 | 141448.12 | 6429.46 | 1561.0 |
| 20.0 | 122159.74 | 6429.46 | 2373.0 |
| 60.0 | 379338.14 | 6429.46 | 36.0 |
| 40.0 | 250748.94 | 6429.46 | 221.0 |
| 16.0 | 96441.9 | 6429.46 | 4861.0 |
| 45.0 | 282896.24 | 6429.46 | 122.0 |
| 24.0 | 147877.58 | 6429.46 | 1358.0 |
| 31.0 | 192883.8 | 6429.46 | 579.0 |
| 39.0 | 244319.48 | 6429.46 | 214.0 |
| 56.0 | 353620.3 | 6429.46 | 48.0 |
| 37.0 | 231460.56 | 6429.46 | 282.0 |
| 55.0 | 347190.84 | 6429.46 | 65.0 |
| 46.0 | 289325.7 | 6429.46 | 103.0 |
| 54.0 | 340761.38 | 6429.46 | 50.0 |
| 57.0 | 360049.76 | 6429.46 | 42.0 |
| 53.0 | 334331.92 | 6429.46 | 54.0 |
| 50.0 | 315043.54 | 6429.46 | 58.0 |
| 52.0 | 327902.46 | 6429.46 | 60.0 |
| 61.0 | 385767.6 | 6429.46 | 34.0 |
| 70.0 | 443632.74 | 6429.46 | 5.0 |
| 63.0 | 398626.52 | 6429.46 | 20.0 |
| 67.0 | 424344.36 | 6429.46 | 11.0 |
| 64.0 | 405055.98 | 6429.46 | 18.0 |
| 47.0 | 295755.16 | 6429.46 | 102.0 |
| 58.0 | 366479.22000000003 | 6429.46 | 34.0 |
| 66.0 | 417914.9 | 6429.46 | 13.0 |
| 69.0 | 437203.28 | 6429.46 | 9.0 |
| 68.0 | 430773.82 | 6429.46 | 7.0 |
| 48.0 | 302184.62 | 6429.46 | 80.0 |
| 62.0 | 392197.06 | 6429.46 | 19.0 |
| 80.0 | 507927.34 | 6429.46 | 2.0 |
| 76.0 | 482209.5 | 6429.46 | 3.0 |
| 79.0 | 501497.88 | 6429.46 | 2.0 |
| 77.0 | 488638.96 | 6429.46 | 1.0 |
| 71.0 | 450062.2 | 6429.46 | 4.0 |
| 88.0 | 559363.02 | 6429.46 | 2.0 |
| 82.0 | 520786.26 | 6429.46 | 1.0 |
| 87.0 | 552933.56 | 6429.46 | 2.0 |
| 78.0 | 495068.42 | 6429.46 | 1.0 |
| 74.0 | 469350.58 | 6429.46 | 2.0 |
| 73.0 | 462921.12 | 6429.46 | 2.0 |
| 92.0 | 585080.86 | 6429.46 | 1.0 |
| 83.0 | 527215.72 | 6429.46 | 1.0 |
| 81.0 | 514356.8 | 6429.46 | 1.0 |
| 100.0 | 636516.54 | 6429.46 | 1.0 |
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
display(g.vertices.filter(col("page_len")<200000L).select("page_len"))
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksView284eaf3")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksView284eaf3) ,min_max AS (SELECT `page_len`,(SELECT MAX(`page_len`) FROM q) `target_column_max`,(SELECT MIN(`page_len`) FROM q) `target_column_min` FROM q) ,histogram_meta AS (SELECT `page_len`,`target_column_min` `min_value`,IF(`target_column_max` = `target_column_min`,`target_column_max` + 1,`target_column_max`) `max_value`,(`target_column_max` - `target_column_min`) / 100 `step` FROM min_max) SELECT IF(ISNULL(`page_len`),NULL,LEAST(WIDTH_BUCKET(`page_len`,`min_value`,`max_value`,100),100)) `page_len_BIN`,FIRST(`min_value` + ((IF(ISNULL(`page_len`),NULL,LEAST(WIDTH_BUCKET(`page_len`,`min_value`,`max_value`,100),100)) - 1) * `step`)) `page_len_BIN_LOWER_BOUND`,FIRST(`step`) `page_len_BIN_STEP`,COUNT(`page_len`) `COUNT` FROM histogram_meta GROUP BY `page_len_BIN`"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksView284eaf3")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
| page_len_BIN | page_len_BIN_LOWER_BOUND | page_len_BIN_STEP | COUNT |
|---|---|---|---|
| 29.0 | 55999.72 | 1999.99 | 6755.0 |
| 26.0 | 49999.75 | 1999.99 | 8890.0 |
| 65.0 | 127999.36 | 1999.99 | 770.0 |
| 19.0 | 35999.82 | 1999.99 | 19211.0 |
| 54.0 | 105999.47 | 1999.99 | 1250.0 |
| 22.0 | 41999.79 | 1999.99 | 13294.0 |
| 7.0 | 11999.94 | 1999.99 | 192036.0 |
| 77.0 | 151999.24 | 1999.99 | 423.0 |
| 34.0 | 65999.67 | 1999.99 | 4585.0 |
| 50.0 | 97999.51 | 1999.99 | 1560.0 |
| 94.0 | 185999.07 | 1999.99 | 318.0 |
| 57.0 | 111999.44 | 1999.99 | 972.0 |
| 43.0 | 83999.58 | 1999.99 | 2450.0 |
| 32.0 | 61999.69 | 1999.99 | 5292.0 |
| 31.0 | 59999.7 | 1999.99 | 5973.0 |
| 39.0 | 75999.62 | 1999.99 | 3178.0 |
| 98.0 | 193999.03 | 1999.99 | 181.0 |
| 25.0 | 47999.76 | 1999.99 | 10036.0 |
| 95.0 | 187999.06 | 1999.99 | 255.0 |
| 71.0 | 139999.3 | 1999.99 | 543.0 |
| 6.0 | 9999.95 | 1999.99 | 272972.0 |
| 68.0 | 133999.33 | 1999.99 | 562.0 |
| 72.0 | 141999.29 | 1999.99 | 486.0 |
| 87.0 | 171999.14 | 1999.99 | 281.0 |
| 58.0 | 113999.43000000001 | 1999.99 | 965.0 |
| 9.0 | 15999.92 | 1999.99 | 109741.0 |
| 27.0 | 51999.74 | 1999.99 | 8025.0 |
| 63.0 | 123999.38 | 1999.99 | 739.0 |
| 56.0 | 109999.45 | 1999.99 | 1119.0 |
| 51.0 | 99999.5 | 1999.99 | 1487.0 |
| 17.0 | 31999.84 | 1999.99 | 24589.0 |
| 41.0 | 79999.6 | 1999.99 | 2720.0 |
| 28.0 | 53999.73 | 1999.99 | 7320.0 |
| 33.0 | 63999.68 | 1999.99 | 4901.0 |
| 88.0 | 173999.13 | 1999.99 | 301.0 |
| 5.0 | 7999.96 | 1999.99 | 394233.0 |
| 1.0 | 0.0 | 1999.99 | 1617667.0 |
| 96.0 | 189999.05 | 1999.99 | 185.0 |
| 10.0 | 17999.91 | 1999.99 | 86644.0 |
| 85.0 | 167999.16 | 1999.99 | 296.0 |
| 48.0 | 93999.53 | 1999.99 | 1734.0 |
| 67.0 | 131999.34 | 1999.99 | 605.0 |
| 100.0 | 197999.01 | 1999.99 | 173.0 |
| 44.0 | 85999.57 | 1999.99 | 2202.0 |
| 61.0 | 119999.4 | 1999.99 | 903.0 |
| 3.0 | 3999.98 | 1999.99 | 981693.0 |
| 37.0 | 71999.64 | 1999.99 | 3624.0 |
| 83.0 | 163999.18 | 1999.99 | 289.0 |
| 12.0 | 21999.89 | 1999.99 | 56040.0 |
| 55.0 | 107999.46 | 1999.99 | 1161.0 |
| 74.0 | 145999.27 | 1999.99 | 452.0 |
| 8.0 | 13999.93 | 1999.99 | 142679.0 |
| 62.0 | 121999.39 | 1999.99 | 781.0 |
| 11.0 | 19999.9 | 1999.99 | 69460.0 |
| 49.0 | 95999.52 | 1999.99 | 1597.0 |
| 35.0 | 67999.66 | 1999.99 | 4255.0 |
| 2.0 | 1999.99 | 1999.99 | 1613295.0 |
| 66.0 | 129999.35 | 1999.99 | 649.0 |
| 76.0 | 149999.25 | 1999.99 | 417.0 |
| 4.0 | 5999.97 | 1999.99 | 609216.0 |
| 92.0 | 181999.09 | 1999.99 | 323.0 |
| 13.0 | 23999.88 | 1999.99 | 46732.0 |
| 36.0 | 69999.65 | 1999.99 | 3921.0 |
| 75.0 | 147999.26 | 1999.99 | 432.0 |
| 78.0 | 153999.23 | 1999.99 | 410.0 |
| 18.0 | 33999.83 | 1999.99 | 21783.0 |
| 69.0 | 135999.32 | 1999.99 | 591.0 |
| 14.0 | 25999.87 | 1999.99 | 39087.0 |
| 21.0 | 39999.8 | 1999.99 | 14759.0 |
| 59.0 | 115999.42 | 1999.99 | 917.0 |
| 15.0 | 27999.86 | 1999.99 | 33318.0 |
| 81.0 | 159999.2 | 1999.99 | 355.0 |
| 38.0 | 73999.63 | 1999.99 | 3413.0 |
| 97.0 | 191999.04 | 1999.99 | 202.0 |
| 73.0 | 143999.28 | 1999.99 | 505.0 |
| 30.0 | 57999.71 | 1999.99 | 6316.0 |
| 42.0 | 81999.59 | 1999.99 | 2639.0 |
| 90.0 | 177999.11000000002 | 1999.99 | 284.0 |
| 23.0 | 43999.78 | 1999.99 | 12173.0 |
| 46.0 | 89999.55 | 1999.99 | 1915.0 |
| 20.0 | 37999.81 | 1999.99 | 16727.0 |
| 70.0 | 137999.31 | 1999.99 | 602.0 |
| 99.0 | 195999.02 | 1999.99 | 156.0 |
| 60.0 | 117999.41 | 1999.99 | 887.0 |
| 40.0 | 77999.61 | 1999.99 | 3012.0 |
| 16.0 | 29999.85 | 1999.99 | 28576.0 |
| 64.0 | 125999.37 | 1999.99 | 687.0 |
| 91.0 | 179999.1 | 1999.99 | 284.0 |
| 47.0 | 91999.54 | 1999.99 | 1833.0 |
| 53.0 | 103999.48 | 1999.99 | 1277.0 |
| 45.0 | 87999.56 | 1999.99 | 2041.0 |
| 24.0 | 45999.77 | 1999.99 | 10758.0 |
| 52.0 | 101999.49 | 1999.99 | 1301.0 |
| 79.0 | 155999.22 | 1999.99 | 376.0 |
| 80.0 | 157999.21 | 1999.99 | 300.0 |
| 82.0 | 161999.19 | 1999.99 | 297.0 |
| 86.0 | 169999.15 | 1999.99 | 290.0 |
| 84.0 | 165999.17 | 1999.99 | 301.0 |
| 93.0 | 183999.08 | 1999.99 | 374.0 |
| 89.0 | 175999.12 | 1999.99 | 281.0 |
Full Graph Analysis
In this notebook we will analyze the Graph created and explored in the notebook 08_explorationArticleGraph, with the slight change that in this analysis we only consider the 25% of articles with the longest pages. We do this to reduce the size of the graph, since we found that the run times got unreasonably long whne using the full graph.
The following areas will be analyzed in this notebook: 1. Graph size and density for the reduced graph 2. Existence of leaf/source nodes in the graph 3. Connected Components 4. Shortest Paths (Maybe a game here...) 5. BFS 6. Unidirected vs bidirected edges
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
Starting by re-creating the Wiki GraphFrame.
val dfPages = spark.sql("SELECT * FROM enwiki_page") // Read pages data
val dfCategory = spark.sql("SELECT * FROM enwiki_category") // Read categories
val dfCategoryLinks = spark.sql("SELECT * FROM enwiki_categorylinks") // Read links between articles/categories and categories
dfPages: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 5 more fields]
dfCategory: org.apache.spark.sql.DataFrame = [cat_id: int, cat_title: string ... 3 more fields]
dfCategoryLinks: org.apache.spark.sql.DataFrame = [cl_from: int, cl_to: string ... 1 more field]
// Join pages with category information
val dfArticlesCat = dfPages.filter(col("page_is_redirect")===0) // remove all redirects
.join(dfCategoryLinks.filter(col("cl_type")==="page"), col("page_id")===col("cl_from"), "left")
dfArticlesCat: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 8 more fields]
// Group on article and aggregate the categories as a set per article
val dfArticlesCatGrouped = dfArticlesCat.groupBy("page_id","page_title","page_len").agg(collect_set(col("cl_to")))
dfArticlesCatGrouped: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 2 more fields]
val dfVertex = dfArticlesCatGrouped.withColumnRenamed("page_id", "id").withColumnRenamed("collect_set(cl_to)", "categories").select("id", "page_title","page_len")
dfVertex: org.apache.spark.sql.DataFrame = [id: int, page_title: string ... 1 more field]
val dfEdgeLinks = spark.sql("SELECT * FROM enwiki_graph_edges_shortenedredirects") // Download the edges w.o. redirects
dfEdgeLinks: org.apache.spark.sql.DataFrame = [src: int, src_title: string ... 3 more fields]
// Since graphframes does not remove edges between non existing edges automatically, we do this manually thru joins
// This is done in 2 steps where we 1st remove edges where the source does not exist
// Secondly we remove edges where the destination does not exist
val filteredEdges = dfEdgeLinks.join(dfVertex,
col("src")===dfVertex.col("id"), "inner")
.select("src", "dst")
.join(dfVertex,
col("dst")===dfVertex.col("id"), "inner").select("src","dst")
filteredEdges: org.apache.spark.sql.DataFrame = [src: int, dst: int]
val dfEdges = filteredEdges
dfEdges: org.apache.spark.sql.DataFrame = [src: int, dst: int]
// Create the full graph from the non-redirect articles and the the filtered edges
val gFull = GraphFrame(dfVertex, dfEdges)
gFull: org.graphframes.GraphFrame = GraphFrame(v:[id: int, page_title: string ... 1 more field], e:[src: int, dst: int])
Let us reduce the graph size based on longer articles. Recall that we thought it would be a good idea to use some quantile value as a cut-off for the page length.
// Use the approxQuantile method to quiclky get an estimate of the quantiles
dfVertex.stat.approxQuantile("page_len", Array(0.5, 0.75, 0.95), 0.005)
res7: Array[Double] = Array(4082.0, 8417.0, 28170.0)
Lets use the 0.75 quantile, then we only consider the top 25% longest articles.
// Create a reduced graph where we only condsider longer articles
val gRed = gFull.filterVertices(col("page_len")>=8417L).dropIsolatedVertices()
gRed: org.graphframes.GraphFrame = GraphFrame(v:[id: int, page_title: string ... 1 more field], e:[src: int, dst: int])
Some examples of articles below. Here Astronomer has a page length of 8573, which is fairly close to the cut-off, lets look at that article.
// Look at some examples
gRed.vertices.take(10)
res8: Array[org.apache.spark.sql.Row] = Array([580,Astronomer,8573], [633,Algae,90619], [673,Atomic_number,14031], [737,Afghanistan,310005], [799,Aquarius_(constellation),36019], [808,Alfred_Hitchcock,179231], [857,Aberdeenshire,33434], [897,Arsenic,127483], [898,Antimony,60686], [974,Ada_Lovelace,81872])
As one can see, this is still a fairly short article, so we will consider this threshold as not beeing too restrictive.
Let's look at the size of this reduced graph to see what happened as we removed the shorter articles.
// In reduced graph
val noNodes = gRed.vertices.count
val noEdges = gRed.edges.count
val density = noEdges / noNodes
noNodes: Long = 1652126
noEdges: Long = 206275015
density: Long = 124
// Let's use reduced size
val g = gRed
g: org.graphframes.GraphFrame = GraphFrame(v:[id: int, page_title: string ... 1 more field], e:[src: int, dst: int])
gFull.unpersist
dfVertex.unpersist
res8: dfVertex.type = [id: int, page_title: string ... 1 more field]
Leaf Nodes and Root Nodes
Do we have any leaf nodes, i.e. nodes without any outgoing links, or root nodes without any incoming links?
// what is the min number of incoming and outgoing edges?
val minOut = g.outDegrees.select(min("outDegree"))
val minIn = g.inDegrees.select(min("inDegree"))
println(minOut.show())
println(minIn.show())
+--------------+
|min(outDegree)|
+--------------+
| 1|
+--------------+
()
+-------------+
|min(inDegree)|
+-------------+
| 1|
+-------------+
()
minOut: org.apache.spark.sql.DataFrame = [min(outDegree): int]
minIn: org.apache.spark.sql.DataFrame = [min(inDegree): int]
Ok so we neither have any source nodes nor leaf nodes since the minimum in/out degree is 1 and not 0.
val leafNodes = g.outDegrees.filter(col("outDegree")===1).select("id")
leafNodes.distinct.count()
leafNodes: org.apache.spark.sql.DataFrame = [id: int]
res31: Long = 462
val rootNodes = g.inDegrees.filter(col("inDegree")===1).select("id")
rootNodes.distinct.count()
rootNodes: org.apache.spark.sql.DataFrame = [id: int]
res33: Long = 49074
How many nodes do we have in intersection?
rootNodes.join(leafNodes, rootNodes.col("id")===leafNodes.col("id")).count()
res35: Long = 110
def pruneLeafNodes(graph: GraphFrame, depth: Int, maxDepth: Int) : GraphFrame = {
println("Current Depth: %d".format(depth))
var leafNodes = graph.outDegrees.filter(col("outDegree")===0).select("id")
println("Current number of leaf nodes: %d".format(leafNodes.distinct.count()))
var prunedG = graph.filterVertices(!col("id").isin(leafNodes))
if (depth == maxDepth) {
return prunedG
}
else {
pruneLeafNodes(prunedG, depth + 1, maxDepth)
}
}
pruneLeafNodes: (graph: org.graphframes.GraphFrame, depth: Int, maxDepth: Int)org.graphframes.GraphFrame
def pruneRootNodes(graph: GraphFrame, depth: Int, maxDepth: Int) : GraphFrame = {
println("Current Depth: %d".format(depth))
var leafNodes = graph.inDegrees.filter(col("inDegree")===0).select("id")
println("Current number of root nodes: %d".format(leafNodes.distinct.count()))
var prunedG = graph.filterVertices(!col("id").isin(leafNodes))
if (depth == maxDepth) {
return prunedG
}
else {
pruneLeafNodes(prunedG, depth + 1, maxDepth)
}
}
pruneRootNodes: (graph: org.graphframes.GraphFrame, depth: Int, maxDepth: Int)org.graphframes.GraphFrame
Connected Components
Now let us also try out some of the built-in algorithms of GraphFrames, starting with Connected Components. Connected components searches a graph for structures of nodes connected to each other through paths. If it finds that some nodes are not able to reach eachother through some path, the nodes are placed in different components. More about this can be read at the page linked below.
// Please work for once...
val result = g.connectedComponents.setAlgorithm("graphx").run()
result.select("id", "component").orderBy("component").show()
+----+---------+
| id|component|
+----+---------+
| 316| 12|
| 738| 12|
| 789| 12|
| 825| 12|
| 856| 12|
| 897| 12|
|1164| 12|
|1267| 12|
|1307| 12|
|1346| 12|
|1437| 12|
|1453| 12|
|1514| 12|
|1545| 12|
|1570| 12|
|1623| 12|
|1629| 12|
|1698| 12|
|1761| 12|
|1762| 12|
+----+---------+
only showing top 20 rows
result: org.apache.spark.sql.DataFrame = [id: int, page_title: string ... 2 more fields]
// How many connected components do we have?
result.select("component").distinct.count
res39: Long = 2
// OK how many articles do we have in each component?
result.groupBy("component").count().show()
+---------+-------+
|component| count|
+---------+-------+
| 12|1652125|
| 11516425| 1|
+---------+-------+
Ok, so all articles except 1 ends up in the same component. What article is that?
result.withColumnRenamed("id", "idComp").join(gRed.vertices, col("idComp")===col("id")).filter(col("component")===11516425L).show()
+--------+------------+--------+---------+--------+------------+--------+
| idComp| page_title|page_len|component| id| page_title|page_len|
+--------+------------+--------+---------+--------+------------+--------+
|11516425|Mohammadabad| 18652| 11516425|11516425|Mohammadabad| 18652|
+--------+------------+--------+---------+--------+------------+--------+
This page only links to short pages, which will not be included in the graph.
One can also look at Strongly Connected Components, where directionality also matters when determining wether a set of nodes can reach another set. However this was not done in this project.
Shortest Paths
We performed some analysis on the shortest paths from all nodes to some landmark nodes. In this analysis we look at some quantile values of the distance from all nodes to one, to get a sense of the diameter of the graph. To get a more accurate measure of this diameter, one should probably sample a large sample of nodes, and the take an average over the maximum distance for all of the landmark nodes, however when testing this with only 10 landmark nodes we ran in to problems with the runtime. Further, we came up with a small game based on the shortest distances, which we perhaps will have time to play during the presentation.
Lets just pick one arbitrary article on wikipedia, say Lord Voldemort.
g.vertices.filter(col("page_title").rlike("Voldemort")).show()
+--------+--------------------+--------+
| id| page_title|page_len|
+--------+--------------------+--------+
|12294600|Voldemort_Can't_S...| 11987|
| 45106| Lord_Voldemort| 69962|
|54184819|Voldemort:_Origin...| 12304|
+--------+--------------------+--------+
Running shortest paths on this landmark, we are able to retrieve the distances from every node to our landmark.
spark.catalog.clearCache() // clear the cache, seems like this helped when the cluster was under a lot of stress
val articleId = 45106L
val results = g.shortestPaths.landmarks(Seq(articleId)).run()
results.select("id", "distances").sample(0.000001).show()
+----+------------+
| id| distances|
+----+------------+
|9425|{45106 -> 3}|
+----+------------+
articleId: Long = 45106
results: org.apache.spark.sql.DataFrame = [id: int, page_title: string ... 2 more fields]
// Look ath the schema
results.printSchema
root
|-- id: integer (nullable = true)
|-- page_title: string (nullable = true)
|-- page_len: integer (nullable = true)
|-- distances: map (nullable = true)
| |-- key: long
| |-- value: integer (valueContainsNull = false)
// Write the results to a parquet file so we don't have to redo this all the time when the cluster dies
results.write.parquet("WikipediaData/shortestPathsLV.parquet")
val dfPaths = spark.read.parquet("/WikipediaData/shortestPathsLV.parquet")
dfPaths: org.apache.spark.sql.DataFrame = [id: int, page_title: string ... 2 more fields]
// Check the schema and compare with above to make sure everything worked
dfPaths.printSchema
root
|-- id: integer (nullable = true)
|-- page_title: string (nullable = true)
|-- page_len: integer (nullable = true)
|-- distances: map (nullable = true)
| |-- key: long
| |-- value: integer (valueContainsNull = true)
// Get the distances only
val dfD = dfPaths.select("distances").withColumn("dist", col("distances").getItem(articleId))
dfD: org.apache.spark.sql.DataFrame = [distances: map<bigint,int>, dist: int]
Now, what is the longest distance from this node to any node in the graph? This should give us an idea of how "deep" or "shallow" the graph is. If the longest distance is not that big, it means that our graph is really shallow.
dfD.select(max("dist")).show()
+---------+
|max(dist)|
+---------+
| 5|
+---------+
This seems pretty shallow, only 5 clicks from Lord Voldemort and you could reach any article on Wikipedia.
// Let's also look at some quantiles of the distance for good measure.
dfD.stat.approxQuantile("dist", Array(0.5, 0.75, 0.95), 0.001)
res40: Array[Double] = Array(3.0, 4.0, 4.0)
The idea was to do this in a more rigorous fashion, where we randomly sample say 100 articles and for each of said aritcles repeat the procedure above. However, since the cluster seemed to be under a lot of stress, and this was a fairly resource intense analysis, we deicded to not pursue this further.
GAME TIME We have created a game notebook where we have hidden the name of the true landmark, the name of the notebook is 13_gameNotebook. Clone the notebook, run the code cells with hidden code (don't view the code cell, that is cheating ;) ). Based on the returned distance, try to figure out what the true landmark is. Good luck...
Below is an example with the same landmark as above.
// This function retrieves the distance to the landmark node for a guess given by the user.
def computeDistanceToTarget(guess: String, distanceDf: org.apache.spark.sql.DataFrame) : Unit = {
val guessRow = distanceDf.filter(col("page_title")===guess)
if (guessRow.isEmpty) {
return println("Guess not in articles...Try again")
}
val distance = guessRow.select("distances.%s".format(45106L)).collect()(0)(0)
println("Distance from %s to target is %s".format(guess, distance))
}
computeDistanceToTarget: (guess: String, distanceDf: org.apache.spark.sql.DataFrame)Unit
// This should be 1...
computeDistanceToTarget("Harry_Potter", dfPaths)
Distance from Harry_Potter to target is 1
computeDistanceToTarget("Bay_City,_Texas", dfPaths)
Distance from Bay_City,_Texas to target is 3
computeDistanceToTarget("Jesus", dfPaths)
Distance from Jesus to target is 3
BFS
Finally, the last GraphFrames algorithm we tried out in the analysis of the article graph was BFS(Breadth-first search). BFS finds the shortest path(s) from one vertex (or a set of vertices) to another vertex (or a set of vertices).
displayHTML(frameIt("https://en.wikipedia.org/wiki/Breadth-first_search", 500))
// TRY OUT CHANGEING MAXPATH PARAM TO SEE IF IT SPEEDS UP
val paths = g.bfs.fromExpr("page_title = 'Lord_Voldemort'").toExpr("page_title = 'Thanksgiving_after_Communion'").run()
paths.show()
+--------------------+-----------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| from| e0| v1| e1| v2| e2| to|
+--------------------+-----------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|{45106, Lord_Vold...| {45106, 2731583}|{2731583, Adolf_H...| {2731583, 56371}|{56371, Mass_(lit...| {56371, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 17867}|{17867, London, 3...| {17867, 18974659}|{18974659, Englis...|{18974659, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 87334}|{87334, Prophecy,...| {87334, 730118}|{730118, Prayer_t...| {730118, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 738}|{738, Albania, 27...| {738, 5596099}|{5596099, Catholi...| {5596099, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 17867}|{17867, London, 3...| {17867, 5955}|{5955, Church_of_...| {5955, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 1645518}|{1645518, Massach...| {1645518, 5955}|{5955, Church_of_...| {5955, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...|{45106, 11927837}|{11927837, Religi...| {11927837, 5955}|{5955, Church_of_...| {5955, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...|{45106, 21244171}|{21244171, Gentry...| {21244171, 21541}|{21541, Nicene_Cr...| {21541, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...|{45106, 19119938}|{19119938, Holly,...| {19119938, 5223729}|{5223729, Blood_o...| {5223729, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...|{45106, 60495569}|{60495569, Harry_...|{60495569, 19280748}|{19280748, Episco...|{19280748, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 1645518}|{1645518, Massach...| {1645518, 19280748}|{19280748, Episco...|{19280748, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 3414021}|{3414021, George_...| {3414021, 19280748}|{19280748, Episco...|{19280748, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 45936}|{45936, Spirit_po...| {45936, 9767}|{9767, Eucharist,...| {9767, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 87334}|{87334, Prophecy,...| {87334, 46398}|{46398, Rosary, 6...| {46398, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 29798}|{29798, The_Lord_...| {29798, 65427}|{65427, Plainsong...| {65427, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 87334}|{87334, Prophecy,...| {87334, 30869117}|{30869117, Latin_...|{30869117, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 738}|{738, Albania, 27...| {738, 30869117}|{30869117, Latin_...|{30869117, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...|{45106, 13425800}|{13425800, War_on...|{13425800, 30869117}|{30869117, Latin_...|{30869117, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 52847}|{52847, Children'...| {52847, 18494}|{18494, Lord's_Pr...| {18494, 12999830}|{12999830, Thanks...|
|{45106, Lord_Vold...| {45106, 738}|{738, Albania, 27...| {738, 347105}|{347105, Mother_T...| {347105, 12999830}|{12999830, Thanks...|
+--------------------+-----------------+--------------------+--------------------+--------------------+--------------------+--------------------+
only showing top 20 rows
paths: org.apache.spark.sql.DataFrame = [from: struct<id: int, page_title: string ... 1 more field>, e0: struct<src: int, dst: int> ... 5 more fields]
displayHTML(frameIt("https://en.wikipedia.org/wiki/Lord_Voldemort", 500))
This was quite slow as you can see from the runtime, we can speed it up by specifying the maxPathLength which governs how many many edges the algorithm is alowed to travel before it should have reached its destination.
// THIS JUST KEPT CRASHING....
// val paths = g.bfs.fromExpr("page_title = 'Lord_Voldemort'").toExpr("page_title = 'Thanksgiving_after_Communion'").maxPathLength(5).run()
// paths.show()
Hopefully this was faster.
Finding bidirected edges
How common are bi-directed edges? We check this using motifs.
val motifGraph = g.find("(a) - [e1] -> (b) ; (b) - [e2] -> (a)") // Find pairs of nodes pointing to each other
val totalCount = motifGraph.count()/2L // Divide by 2 since every row will be a duplicate
motifGraph: org.apache.spark.sql.DataFrame = [a: struct<id: int, page_title: string ... 1 more field>, e1: struct<src: int, dst: int> ... 2 more fields]
totalCount: Long = 50566122
Interesting, about 25% of edges...
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
Motif Search and Case study: Wallenberg Family
In this notebook we are studying how well connected our funders are according to wikipedia, we use this to study the processing of graphframes on subgraphs
Creating the GraphFrame
The graphframe is created following the same procedure as that of the full graph.
val dfPages = spark.sql("SELECT * FROM enwiki_page")
val dfCategory = spark.sql("SELECT * FROM enwiki_category")
val dfCategoryLinks = spark.sql("SELECT * FROM enwiki_categorylinks")
dfPages: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 5 more fields]
dfCategory: org.apache.spark.sql.DataFrame = [cat_id: int, cat_title: string ... 3 more fields]
dfCategoryLinks: org.apache.spark.sql.DataFrame = [cl_from: int, cl_to: string ... 1 more field]
val dfArticlesCat = dfPages.filter(col("page_is_redirect")===0)
.join(dfCategoryLinks.filter(col("cl_type")==="page"), col("page_id")===col("cl_from"), "left")
dfArticlesCat: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 8 more fields]
val dfArticlesCatGrouped = dfArticlesCat.groupBy("page_id","page_title").agg(collect_set(col("cl_to")))
dfArticlesCatGrouped: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 1 more field]
val dfVertex = dfArticlesCatGrouped.withColumnRenamed("page_id", "id").withColumnRenamed("collect_set(cl_to)", "categories")
dfVertex: org.apache.spark.sql.DataFrame = [id: int, page_title: string ... 1 more field]
val dfEdgeLinks = spark.sql("SELECT * FROM enwiki_graph_edges_shortenedredirects")
val dfEdges = dfEdgeLinks.select("src", "dst")
dfEdgeLinks: org.apache.spark.sql.DataFrame = [src: int, src_title: string ... 3 more fields]
dfEdges: org.apache.spark.sql.DataFrame = [src: int, dst: int]
val g = GraphFrame(dfVertex, dfEdges)
g.cache()
g: org.graphframes.GraphFrame = GraphFrame(v:[id: int, page_title: string ... 1 more field], e:[src: int, dst: int])
res6: g.type = GraphFrame(v:[id: int, page_title: string ... 1 more field], e:[src: int, dst: int])
Motif Finding
This method looks for substructures along the GraphFrame object which we can then filter on. We use this to study the behaviour both on the full dataset and on a smaller dataset containing all articles with swedish-language text (9176 articles)
val motif = g.find("(a) - [e] -> (b)")
val subgraph = motif.filter(array_contains(col("a.categories"), "Articles_containing_Swedish-language_text")).filter(array_contains(col("b.categories"), "Articles_containing_Swedish-language_text"))
subgraph.cache()
motif: org.apache.spark.sql.DataFrame = [a: struct<id: int, page_title: string ... 1 more field>, e: struct<src: int, dst: int> ... 1 more field]
subgraph: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [a: struct<id: int, page_title: string ... 1 more field>, e: struct<src: int, dst: int> ... 1 more field]
res7: subgraph.type = [a: struct<id: int, page_title: string ... 1 more field>, e: struct<src: int, dst: int> ... 1 more field]
We use a filter first looking at all edges that have their destination at the wallenberg family page.
val exampleMotif = motif.filter(motif("e.dst")===1193699)
exampleMotif: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [a: struct<id: int, page_title: string ... 1 more field>, e: struct<src: int, dst: int> ... 1 more field]
display(exampleMotif)
val WallenbergTo = motif.filter(motif("e.src")===1193699)
display(WallenbergTo)
We observe here that the second query was significantly faster than the first; the reason is that is due to Spark's lazy evaluation. As such, the first time that the motif is called to display the processing of all the millions of edges are processed and evaluated, whereas in the second case it simply used the graph from memory and filtered the edges. We repeat the first query to see the actual speed of filtering for the motif.
display(exampleMotif)
As we can see, even without explicitly putting the motif in cache, this second attempt was executed one order of magnitude faster than the the first.
val subgraphWallenbergFrom = subgraph.filter(motif("e.dst")===1193699)
display(subgraphWallenbergFrom)
val subgraphWallenbergTo = subgraph.filter(motif("e.src")===1193699)
display(subgraphWallenbergTo)
Note: During testing cmd 21 was as fast as 2s [no image of this execution exist], implying that the actual runtime of these queries are very dependent on cluster usage at the time.
//val exampleMotifDistanceTwo = g.find("(a) - [e] -> (b); (b) - [e2] -> (c); !(a) - [] ->(c)")
//val twoFromWallenberg = exampleMotifDistanceTwo.filter(exampleMotifDistanceTwo("e.src")===1193699)
//twoFromWallenberg.count()
While we wanted to show a measure of the numerical explosion for any node when going from a neighbour to just the vertices 2 links away, there was never an instance of the query that terminated. This query went for as long as 25 hours without processing all edges.
Conclusion
I DONT KNOW WHAT TO SAY HERE REALLY, HELP Some closing thoughts after working with the Wikipedia data for quite some time is that the Graph was a lot harder to process than what was initially expected. The size and density made it difficult to investigate all of the fields we initially planned. For instance, something we thought would be interesting before we started was to run label propagation for community detection on the graph, and then compare the found communities to the categories. However, we were sadly not able to get label propagation working on even the reduced graph.
Future Work
Some suggestions regarding future work are:
- Comparing the English Wikipedia we have analyzed now a different language, such as Swedish and look for
- General differences in the analysis we performed on the English Wikipedia
- Missing articles
- Missing links
- Running pagerank on both and comparing the top scorers
- Performing a more rigorous analysis of the depth of the graph, not just looking at one node but a larger sample
- Using a larger cluster in order to:
- Analyze the full graph and not just the longer articles considered in this project
- Running label propagation and compare communities to categories
- Using GraphX instead of GraphFrames to see whether we can achieve faster run-times
- More?
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
Game Setup
This notebook just creates the dataframe required to run the game in XX_gameNotebook. Below cell creates a GraphFrame the same way as in notebook 09_fullGraphAnalysis.
// Run this to get the graphframe
val dfPages = spark.sql("SELECT * FROM enwiki_page")
val dfCategory = spark.sql("SELECT * FROM enwiki_category")
val dfCategoryLinks = spark.sql("SELECT * FROM enwiki_categorylinks")
val dfArticlesCat = dfPages.filter(col("page_is_redirect")===0)
.join(dfCategoryLinks.filter(col("cl_type")==="page"), col("page_id")===col("cl_from"), "left")
val dfArticlesCatGrouped = dfArticlesCat.groupBy("page_id","page_title","page_len").agg(collect_set(col("cl_to")))
val dfVertex = dfArticlesCatGrouped.withColumnRenamed("page_id", "id").withColumnRenamed("collect_set(cl_to)", "categories").select("id", "page_title","page_len")
dfVertex
val dfEdgeLinks = spark.sql("SELECT * FROM enwiki_graph_edges_shortenedredirects")
val filteredEdges = dfEdgeLinks.join(dfVertex,
col("src")===dfVertex.col("id"), "inner")
.select("src", "dst")
.join(dfVertex,
col("dst")===dfVertex.col("id"), "inner").select("src","dst")
val dfEdges = filteredEdges
val gFull = GraphFrame(dfVertex, dfEdges)
val gRed = gFull.filterVertices(col("page_len")>=8417L).dropIsolatedVertices()
val g = gRed
dfPages: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 5 more fields]
dfCategory: org.apache.spark.sql.DataFrame = [cat_id: int, cat_title: string ... 3 more fields]
dfCategoryLinks: org.apache.spark.sql.DataFrame = [cl_from: int, cl_to: string ... 1 more field]
dfArticlesCat: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 8 more fields]
dfArticlesCatGrouped: org.apache.spark.sql.DataFrame = [page_id: int, page_title: string ... 2 more fields]
dfVertex: org.apache.spark.sql.DataFrame = [id: int, page_title: string ... 1 more field]
dfEdgeLinks: org.apache.spark.sql.DataFrame = [src: int, src_title: string ... 3 more fields]
filteredEdges: org.apache.spark.sql.DataFrame = [src: int, dst: int]
dfEdges: org.apache.spark.sql.DataFrame = [src: int, dst: int]
gFull: org.graphframes.GraphFrame = GraphFrame(v:[id: int, page_title: string ... 1 more field], e:[src: int, dst: int])
gRed: org.graphframes.GraphFrame = GraphFrame(v:[id: int, page_title: string ... 1 more field], e:[src: int, dst: int])
g: org.graphframes.GraphFrame = GraphFrame(v:[id: int, page_title: string ... 1 more field], e:[src: int, dst: int])
Now the cell below is hidden, please don't view it since the correct answer for the game is there. What happens in the cell is that shortest paths is permored on the graph, with the target node as or landmark.
Finally we just write the dataframe with the shortest paths to a parquet file in dbfs which we can just download when we play the game.
results.write.parquet("WikipediaData/shortestPathsGameSetup.parquet")
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
GAME TIME!
Try to get as close as possible to the target page, or even figure out which it is!
Instructions
- Clone this notebook
- Run the cells below to download the data and define the function
- Provide the function with you guesses and try to come as close(or far) to the target as possible!
NOTE:
The guesses need to exactly match the page name according to Wikipedia. The easiest way to find this name for an article is to just google the article, and then paste the last part of the url as a guess. For instance, if your guess is "List of Saint Seiya Episode.G characters", you would use the guess List_of_Saint_Seiya_Episode.G_characters since the url to the articel on Wikipedia is https://en.wikipedia.org/wiki/List_of_Saint_Seiya_Episode.G_characters.
val results = spark.read.parquet("/WikipediaData/shortestPathsGameSetup.parquet")
results: org.apache.spark.sql.DataFrame = [id: int, page_title: string ... 2 more fields]
Don't look at the dataframe results or the cell below, since that would give you the answer and sort of ruin the experience ;)
def computeDistanceToTarget(guess: String, distanceDf: org.apache.spark.sql.DataFrame) : Unit = {
val guessRow = distanceDf.filter(col("page_title")===guess)
if (guessRow.isEmpty) {
return println("Guess not in articles...Try again")
}
val distance = guessRow.select("distances.%s".format(articleId)).collect()(0)(0)
println("Distance from %s to target is %s".format(guess, distance))
}
computeDistanceToTarget: (guess: String, distanceDf: org.apache.spark.sql.DataFrame)Unit
// use the function
computeDistanceToTarget("List_of_Saint_Seiya_Episode.G_characters", results)
Distance from List_of_Saint_Seiya_Episode.G_characters to target is 3
computeDistanceToTarget("Planthopper", results)
Distance from Planthopper to target is 3
computeDistanceToTarget("Styringomyia", results)
Distance from Styringomyia to target is 3
Graph of Wikipedia
Done:
-
Data Ingestion
-
Data pre-processing
-
Preliminary analysis
-
Preliminary exploration
TODO:
-
Exploration notebook
- Pages vs Redirect pages
- etc..
-
Merge redirect pages
-
Look at structure of Categories (dendrogram maybe)
-
Make sure that everything, (including most of presentation notebook is done by 2 Dec)
-
Meeting 5th to prep the presentation notebook together
-
Make presentation Notebook(Save as HTML as well)
-
Maybe we need to write a report?(Ask Raaz)
Ingestion
DONE (PATH)
Prepro
- Merge redirects (DONE)
- Reduce graph size by (If needed)(VILHELM)
- only considering articles with less than X incoming links
- using connected components (IMPOSSIBLE, Spark crashes when trying to run StronglyConnectedComponents on the whole graph)
Exploration
- Categories (HENRIK)
- Size
- Hierarchy (Categories and their sub-categories)
- Keep track of number of articles per category and maybe where they point(inside/outside cat)
- Links
- Articles(DONE)(ALBIN)(
08_explorationArticleGraph)- In/out degrees (Top/Bottom/Avg)
- Number of articles
- Number of edges
- Density
- Page lenght
- Other
- Add more as we go
Analysis
- Full Graph (ALBIN)(
0X_fullGraphAnalysis)(NOTE: USE SMALLER GRAPH)- Connected Components (Strongly)(ALBIN)(NOTE: I WILL NEED TO RESTRICT FULL GRAPH A LITTLE)(DONE: JUST 1)
- Category belonging to separate
- Leaf pruning (ALBIN)(This does not seem to exist, strange? Also, there exists no root nodes, i.e. nodes with no incoming edges.)(NOTE: I THINK WE CAN DROP THIS, IF WE FILTER THE GRAPH IT BECOMES DENSER)
- Look at 0 out degree nodes, remove and repeat X times
- Bidirected vs uni-directed edges (ALBIN)
- How common is bidirected (DONE)
- How common is up-pointing links vs down-pointing (w.r.t. category hierarchy)
- If unidirected, how long is connection in other direction (does it exist?)
- Maybe BFS, depends on time it takes(ALBIN)(DONE)
- Shortest Paths (ALBIN)(DONE: ALSO AN IDEA FOR PRESENTATION)
- Connected Components (Strongly)(ALBIN)(NOTE: I WILL NEED TO RESTRICT FULL GRAPH A LITTLE)(DONE: JUST 1)
- Category based (SIMON)
- WASP/Wallenberg Graph/Some category we find interesting
- Where is the category in the hierarcy trees (NOT DONE, TREES NOT FINISHED)
- Interesting incoming edges
- Interesting outgoing edges
- Speed comparison motif vs just filters vs shortestpath (Motif for distance >1 never finishes)
- All GraphFrames algorithms and find things like:
- Pagerank
- Components
- Communities
- Motifs (finding missing edges using non-complete triangels)
- Bfs
- WASP/Wallenberg Graph/Some category we find interesting
- Ideas: (VILHELM)
- Community detection based on motif finding (Idea)
TODOS:
- Make a new intro notebook instead of this one
- Wrap up what is left to be done
- Clean up notebooks and make them look nice
- Make notebook with our conclusions
Presentation
Planning what do include in the presentation
Intro
- ADD HERE
Data Ingestion
Include things from notebooks: * ADD HERE
Data Preprocessing
Include things from notebooks: * ADD HERE
Exploration
Include things from notebooks: * ADD HERE
Analysis
Include things from notebooks: * ADD HERE
Conclusions
- ADD HERE
Visual Question Answering using Transformers
Project members:
- firstName lastName, Institution
- firstName lastName, Institution
Task Description
Visual Question Answering (VQA) is the task of understanding a given image and answering questions in natural language based on the image. This is a challenging task as it requires reasoning about two different data modalities (text and image) in conjunction. An additional challenge is to generate an answer to the question in natural language. Such a system enables multimodal interaction with humans and one useful application is in assistive technologies for visually challenged individuals. A general framework to solve this task involves the following steps:
- Image feature extraction
- Question feature extraction
- Relating and combining image and question features
- Answer generation
However, for this project, we simplify this problem by only considering yes/no type questions. This removes the need to train an answer generation model and the VQA task can be simply posed as a binary classification problem as follows:
- Image feature extraction
- Question feature extraction
- Classifier to predict yes/no answer
The classifier performs the task of relating the image and the question to predict the most appropriate answer. A mathematical formulation of the task is:
Given an image \(x\), a question \(q\) and answer \(a \in {0, 1}\), the task is to learn a model to predict the correct answer choice \(a = f(x, q ; \theta)\), with model parameters \(\theta\).
Dataset
For this task, we use the VQA (Visual Question Answering) v1.0 dataset (https://visualqa.org/) [1]. This dataset was first introduced at the VQA Challenge at CVPR 2016 and it is used as a standard benchmark dataset for the VQA task. This dataset uses selected images from the COCO dataset [6] and each image can have multiple related questions. We pick the subset of the dataset that contains yes/no type questions. Then, we obtain a dataset that consists of 63317 training images and 30612 validation images. In total, there are 95302 questions in the training set and 45478 questions in the validation set. Below, we visualize a few examples from the training dataset.
/dbfs/ml/VQA
#Uncomment and run these commands to download and unzip the dataset
#!wget -nc https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/Questions_Train_mscoco.zip
#!wget -nc https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/Annotations_Train_mscoco.zip
#!wget -nc http://images.cocodataset.org/zips/train2014.zip
#!wget -nc https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/Questions_Val_mscoco.zip
#!wget -nc https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/Annotations_Val_mscoco.zip
#!wget -nc http://images.cocodataset.org/zips/val2014.zip
#!unzip -qq '*.zip'
#!ls train2014 | wc -l
#!ls val2014 | wc -l
from collections import namedtuple
import json
import matplotlib.pyplot as plt
import os
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
VQAVisualizationExample = namedtuple('VQAVisualizationExample', [
'question_txt',
'answer_txt',
'img'
])
class VQADataset(Dataset):
def __init__(self, data_dir="/dbfs/ml/VQA/", data_split="train"):
self.data_split = data_split
self.data_dir = data_dir
# Get ready the text
if self.data_split=="train":
self.questions = json.load(open(os.path.join(self.data_dir, 'MultipleChoice_mscoco_train2014_questions.json')))['questions']
self.answers = json.load(open(os.path.join(self.data_dir, 'mscoco_train2014_annotations.json')))['annotations']
else:
self.questions = json.load(open(os.path.join(self.data_dir, 'MultipleChoice_mscoco_val2014_questions.json')))['questions']
self.answers = json.load(open(os.path.join(self.data_dir, 'mscoco_val2014_annotations.json')))['annotations']
self.yesno_indices = [i for i, a in enumerate(self.answers) if a["answer_type"] == "yes/no"]
self.questions = [self.questions[i] for i in self.yesno_indices]
self.answers = [self.answers[i] for i in self.yesno_indices]
def __len__(self):
return len(self.questions)
def __getitem__(self, idx):
question = self.questions[idx]
answer = self.answers[idx]
assert question['question_id'] == answer['question_id']
question_txt = question['question']
answer_txt = answer['multiple_choice_answer']
img_id = question['image_id']
img = Image.open(os.path.join(self.data_dir, f'{self.data_split}2014/COCO_{self.data_split}2014_{img_id:012}.jpg'))
return VQAVisualizationExample(question_txt, answer_txt, img)
vqa_ds = VQADataset(data_split="train")
# Visualize few examples from the dataset
n_examples = 10
example_ids = [0, 8, 100, 200, 300, 400, 500, 600, 800, 1000]
fig, ax = plt.subplots(n_examples//2, 2, figsize=(30, 40))
for k in range(n_examples):
j = 0 if k%2==0 else 1
i = k // 2
example = vqa_ds[example_ids[k]]
question_txt = example.question_txt
correct_answer = example.answer_txt
ax[i][j].imshow(example.img)
_ = ax[i][j].set_xticks([])
_ = ax[i][j].set_yticks([])
_ = ax[i][j].set_title(f"Question: {question_txt}, \nCorrect answer: {correct_answer}", fontsize=14)
Solution Idea
We follow the following steps to solve this task:
- Image feature extraction
- Question feature extraction
- Classifier to pick the correct answer
Given an image \(x\), a question \(q\) and answer \(a \in {0, 1}\), the VQA model can be formulated as a classifier \(p = h(g_v(x), g_l(q))\), where \(p\) is the probability of the answer being "yes", \(p = P(a=1|x, q)\). Here, \(g_l(\cdot)\) is the text feature extractor and \(g_v\) is the visual feature extractor. The answer \(a\) is predicted as \(a = \mathcal{1}_{p>0.5}\).
Inspired by the recent advances in the usage of Transformers in both vision and language representation learning, we use Transformer architectures to extract image and text features. Particularly, self-supervised pretraining on large unlabeled datasets have been shown to transfer well to new tasks. Sometimes, these self-supervised representations even surpass fully supervised training on the specific task, especially when limited labeled data is available. Hence, we choose to use publicly available self-supervised and pre-trained Transformer models for the feature extractors. For the image feature extractor, we use the Small Vision Transformer (ViT-Small/16) [5] pre-trained using DINO self-supervised learning method [2]. For the text feature extractor that is used to extract features for the questions, we use the ALBERT model [3], which is a computationally efficient version of BERT [4].
The Transformer feature extractors output a set of feature vectors for each pair of question and image. The feature extractors themselves are kept frozen and are not trained. We add a small trainable interaction module and allows the image and text features to interact and extract a combined set of features that is useful for answering the question. These features are processed using a 2-layer MLP to get the final classification prediction. The flowchart of our method is shown in the figure below.
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
_ = ax.imshow(Image.open("/dbfs/ml/VQA/vqa.png"))
_ = ax.set_xticks([])
_ = ax.set_yticks([])
_ = ax.axis("off")
Results
We implemented the training of our model using the distributed data parallel method with Pytorch and Horovod. This can leverage multiple GPUs to perform scalable training of deep learning models. On the yes/no VQA task, we achieve an accuracy of x % on the validation dataset. Considering that we use a simple setup with few trainable parameters, the achieved performance looks reasonable. Deeper and more complicated interaction between the image and text features can be beneficial to improve the results.
References
[1] Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017). Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6904-6913).
[2] Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9650-9660).
[3] Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019, September). ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In International Conference on Learning Representations.
[4] Kenton, J. D. M. W. C., & Toutanova, L. K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT (pp. 4171-4186).
[5] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020, September). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations.
[6] Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014, September). Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.
/dbfs/ml/VQA
!wget -nc https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/Questions_Train_mscoco.zip
!wget -nc https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/Annotations_Train_mscoco.zip
!wget -nc http://images.cocodataset.org/zips/train2014.zip
!unzip -qqn '*.zip'
!ls train2014 | wc -l
/dbfs/ml/VQA
!wget -nc https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/Questions_Val_mscoco.zip
!wget -nc https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/Annotations_Val_mscoco.zip
!wget -nc http://images.cocodataset.org/zips/val2014.zip
!unzip -qqn '*Val*.zip'
!unzip -qqn 'val*.zip'
!ls val2014 | wc -l
from collections import namedtuple
from functools import partial
import json
from tqdm.notebook import tqdm
import numpy as np
import os
from pathlib import Path
import torch
import transformers
from torch.utils.data import Dataset, DataLoader
from transformers import AlbertTokenizer, AlbertModel
from transformers import ViTFeatureExtractor, ViTModel
from torch.nn import TransformerEncoder, TransformerEncoderLayer
from PIL import Image
import horovod.torch as hvd
from sparkdl import HorovodRunner
# VQATrainingExample = namedtuple('VQATrainingExample', [
# 'txt',
# 'encoded_txt',
# 'label',
# 'encoded_img'
# ])
def collator_f(batch, txt_tokenizer):
txt = [ex['txt'] for ex in batch]
encoded_txt = txt_tokenizer(txt, padding=True, return_tensors="pt", return_attention_mask=True)
label = torch.FloatTensor([ex['label'] for ex in batch])
encoded_img = {'pixel_values': torch.stack([ex['encoded_img']['pixel_values'][0] for ex in batch])}
return {'txt': txt, 'encoded_txt': encoded_txt, 'label': label, 'encoded_img': encoded_img}
class VQADatset(Dataset):
def __init__(self, txt_tokenizer, data_split="train"):
# Loading the tokenizer and image feature extractor
self.txt_tokenizer = txt_tokenizer
self.img_feat_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vits16')
self.data_split = data_split
# Get ready the text
if self.data_split=="train":
self.questions = json.load(open('/dbfs/ml/VQA/MultipleChoice_mscoco_train2014_questions.json'))['questions']
self.answers = json.load(open('/dbfs/ml/VQA/mscoco_train2014_annotations.json'))['annotations']
else:
self.questions = json.load(open('/dbfs/ml/VQA/MultipleChoice_mscoco_val2014_questions.json'))['questions']
self.answers = json.load(open('/dbfs/ml/VQA/mscoco_val2014_annotations.json'))['annotations']
self.yesno_indices = [i for i, a in enumerate(self.answers) if a["answer_type"] == "yes/no"]
self.questions = [self.questions[i] for i in self.yesno_indices]
self.answers = [self.answers[i] for i in self.yesno_indices]
def __len__(self):
return len(self.questions)
def __getitem__(self, idx):
question = self.questions[idx]
answer = self.answers[idx]
assert question['question_id'] == answer['question_id']
txt = question['question']
assert len(question['multiple_choices']) == 18
label = 1 if answer['multiple_choice_answer'] == "yes" else 0
img_id = question['image_id']
img = Image.open(f'/dbfs/ml/VQA/{self.data_split}2014/COCO_{self.data_split}2014_{img_id:012}.jpg')
try:
encoded_img = self.img_feat_extractor(images=img, return_tensors="pt")
except:
encoded_img = self.img_feat_extractor(images=img.convert('RGB'), return_tensors="pt")
return {'txt': txt, 'encoded_txt': '', 'label': label, 'encoded_img': encoded_img}
class VQAModel(torch.nn.Module):
def __init__(self, d_model: int=384, nhead: int=6, d_hid: int=384, nlayers: int=1, n_class: int=1):
super(VQAModel, self).__init__()
# Loading the encoders
self.albert = AlbertModel.from_pretrained("albert-base-v2")
self.vit = ViTModel.from_pretrained('facebook/dino-vits16', add_pooling_layer=False)
# Freeze them
self.vit.eval()
self.albert.eval()
for param in self.albert.parameters():
param.requires_grad = False
for param in self.vit.parameters():
param.requires_grad = False
# A linear layer to map Albert to ViT size
self.linear_map = torch.nn.Sequential(torch.nn.Linear(768, d_hid), torch.nn.GELU())
self.linear_map_img = torch.nn.Sequential(torch.nn.Linear(d_hid, d_hid), torch.nn.GELU())
# The multimodal transformer block
encoder_layer = TransformerEncoderLayer(d_model, nhead, d_hid)
self.transformer_encoder = TransformerEncoder(encoder_layer, nlayers)
# A linear layer for classification
self.linear_cls = torch.nn.Sequential(torch.nn.Linear(4*d_hid, d_hid//4), torch.nn.GELU(), torch.nn.Linear(d_hid//4, n_class), torch.nn.GELU())
def set_eval(self):
self.linear_map.eval()
self.linear_map_img.eval()
self.transformer_encoder.eval()
self.linear_cls.eval()
def set_train(self):
self.linear_map.train()
self.linear_map_img.train()
self.transformer_encoder.train()
self.linear_cls.train()
def forward(self, encoded_txt, encoded_img):
txt_out = self.albert(**encoded_txt).last_hidden_state
txt_out = self.linear_map(txt_out)
img_out = self.vit(**encoded_img).last_hidden_state
img_out = self.linear_map_img(img_out)
txt_img = torch.cat((txt_out, img_out), dim=-2)
txt_img = self.transformer_encoder(txt_img)
attention_mask = encoded_txt.attention_mask[:, 1:].unsqueeze(-1)
txt_img_features = torch.cat([txt_img[:,0], txt_img[:,txt_out.shape[1]],
torch.sum(txt_img[:, 1:txt_out.shape[1]] * attention_mask, dim=-2) / torch.sum(attention_mask, dim=-2),
torch.mean(txt_img[:, txt_out.shape[1]:], dim=-2)], dim=-1)
pred = self.linear_cls(txt_img_features)
return pred
Training
We train the model with a batch size of 256 and learning rate of 1e-5 (Aadam) for 24 epochs.
It roughly takes 1 hour to train the model for one epoch.
Parallelization
We use Horovod to distribute the training on multiple GPUs. Using Horovod we can train on single-GPU, multiple-GPUs, or even multiple hosts without any further code changes.
Using Horovod requires only minimal code changes. Including:
-
Scaling the batch size:
lr=1e-5 * hvd.size() -
Wrap the optimizer in
hvd.DistributedOptimizer. The distributed optimizer delegates gradient computation to the original optimizer, averages gradients, and then applies those averaged gradients. -
Modify the code to save checkpoints only on worker 0 to prevent other workers from corrupting them. (
hvd.rank() != 0) -
Partition dataset among workers using DistributedSampler:
train_sampler = torch.utils.data.distributed.DistributedSampler
def train_one_epoch(model, optimizer, criterion, data_loader, epoch, device):
losses = []
pred_labels = []
true_labels = []
for i, batch in enumerate(data_loader):
if i % 25 == 0:
print(f'Train: {int(i*100/len(data_loader))}%')
encoded_txt = batch['encoded_txt'].to(device)
encoded_img = {'pixel_values': batch['encoded_img']['pixel_values'].to(device)}
optimizer.zero_grad()
pred = model(encoded_txt, encoded_img)
pred_labels.append((pred.reshape(-1).detach().cpu() > 0.0).long())
true_labels.append(batch['label'])
loss = criterion(pred.reshape(-1), batch['label'].to(device))
loss.backward()
optimizer.step()
losses.append(loss.item())
accuracy = np.mean(torch.cat(pred_labels).numpy() == torch.cat(true_labels).numpy())
return np.mean(losses), accuracy
def validate(model, data_loader, device):
pred_labels = []
true_labels = []
with torch.no_grad():
for i, batch in enumerate(data_loader):
if i % 25 == 0:
print(f'Val: {int(i*100/len(data_loader))}%')
encoded_txt = batch['encoded_txt'].to(device)
encoded_img = {'pixel_values': batch['encoded_img']['pixel_values'].to(device)}
pred = model(encoded_txt, encoded_img)
pred_labels.append((pred.reshape(-1).detach().cpu() > 0.0).long())
true_labels.append(batch['label'])
accuracy = np.mean(torch.cat(pred_labels).numpy() == torch.cat(true_labels).numpy())
return accuracy
def train(use_horovod=True):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
if use_horovod:
hvd.init()
if torch.cuda.is_available():
torch.cuda.set_device(hvd.local_rank())
txt_tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
train_vqa_ds = VQADatset(txt_tokenizer, data_split="train")
val_vqa_ds = VQADatset(txt_tokenizer, data_split="val")
collator = partial(collator_f, txt_tokenizer=txt_tokenizer)
output_dir = "/dbfs/ml/VQA/outputs/"
resume_from_checkpoint = os.path.join(output_dir, "checkpoint_19.pth")
model = VQAModel()
n_epochs = 50
start_epoch = 0
if os.path.exists(resume_from_checkpoint):
state_dict = torch.load(resume_from_checkpoint)
model.load_state_dict(state_dict["state_dict"])
start_epoch = state_dict["epoch"]
print(f"Model checkpoint state loaded from {resume_from_checkpoint}")
if use_horovod:
from torch.utils.data.distributed import DistributedSampler
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5 * hvd.size())
if os.path.exists(resume_from_checkpoint):
optimizer.load_state_dict(state_dict["optimizer"])
for p in optimizer.param_groups[0]["params"]:
p.to(device)
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
train_sampler = DistributedSampler(train_vqa_ds, num_replicas=hvd.size(), rank=hvd.rank())
train_vqa_dl = DataLoader(train_vqa_ds, batch_size=256, shuffle=False, collate_fn=collator, num_workers=4, sampler=train_sampler)
val_sampler = DistributedSampler(val_vqa_ds, num_replicas=hvd.size(), rank=hvd.rank())
val_vqa_dl = DataLoader(val_vqa_ds, batch_size=256, shuffle=False, collate_fn=collator, num_workers=4, sampler=val_sampler)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
if os.path.exists(resume_from_checkpoint):
optimizer.load_state_dict(state_dict["optimizer"])
for p in optimizer.param_groups[0]["params"]:
p.to(device)
train_vqa_dl = DataLoader(train_vqa_ds, batch_size=1024, shuffle=True, collate_fn=collator, num_workers=8)
val_vqa_dl = DataLoader(val_vqa_ds, batch_size=1024, shuffle=False, collate_fn=collator, num_workers=8)
model.to(device)
criterion = torch.nn.BCEWithLogitsLoss()
for epoch in range(start_epoch, n_epochs):
print(f'Epoch: {epoch+1}')
model.set_train()
epoch_loss, epoch_accuracy = train_one_epoch(model, optimizer, criterion, train_vqa_dl, epoch, device)
model.set_eval()
train_accuracy = epoch_accuracy
val_accuracy = validate(model, val_vqa_dl, device)
log_stats = {"train_loss": epoch_loss, "train_accuracy": train_accuracy, "val_accuracy": val_accuracy}
if (use_horovod and hvd.rank() == 0) or not use_horovod:
save_dict = {
"epoch": epoch + 1,
"state_dict": model.state_dict(),
"optimizer": optimizer.state_dict(),
"train_accuracy": train_accuracy,
"val_accuracy": val_accuracy,
"loss": epoch_loss
}
torch.save(save_dict, os.path.join(output_dir, f"checkpoint_{epoch}.pth"))
print(f"Epoch={epoch}, Loss={epoch_loss}, Train Accuracy={train_accuracy}, Val Accuracy={val_accuracy}")
def main(use_horovod=True, np=1):
if use_horovod:
hr = HorovodRunner(np=np, driver_log_verbosity='all')
hr.run(train)
else:
train(use_horovod=False)
main(use_horovod=True)
#%cd /dbfs/ml/VQA
!ls /dbfs/ml/VQA/outputs
import torch
import os
!ls /dbfs/ml/VQA/outputs
checkpoints_dir = "/dbfs/ml/VQA/outputs/"
rdd = sc.parallelize(list(range(50)))
def get_stats_from_checkpoint(checkpoint_path):
state_dict = torch.load(checkpoint_path, map_location="cpu")
return state_dict['epoch'], state_dict['train_accuracy'], state_dict['val_accuracy']
train_stats = rdd.filter(lambda x: os.path.exists(os.path.join(checkpoints_dir, f"checkpoint_{x}.pth")))
train_stats = train_stats.map(lambda x: get_stats_from_checkpoint(x))
train_stats = train_stats.aggregateByKey({"epoch": [], "train_accuracy": [], "val_accuracy": []},
lambda x, y: {"epoch": [] + [y["epoch"],],
"train_accuracy": [] + [y["train_accuracy"],],
"val_accuracy": [] + [y["val_accuracy"],]},
lambda x, y: {"epoch": x["epoch"] + y["epoch"],
"train_accuracy": x["train_accuracy"] + y["train_accuracy"],
"val_accuracy": x["val_accuracy"] + y["val_accuracy"]})
train_stats = train_stats.collect()
training_checkpoint_path = "/dbfs/ml/VQA/outputs/checkpoint_19.pth"
state_dict = torch.load(training_checkpoint_path, map_location="cpu")
state_dict['train_accuracy'], state_dict['val_accuracy']
ScaDaMaLe Course site and book
The following is from databricks blog with minor adaptations with help from Tilo Wiklund.
Distributed deep learning training using PyTorch with HorovodRunner for MNIST
This notebook demonstrates how to train a model for the MNIST dataset using PyTorch. It first shows how to train the model on a single node, and then shows how to adapt the code using HorovodRunner for distributed training.
Requirements
- This notebook runs on CPU or GPU clusters.
- To run the notebook, create a cluster with
- Two workers
Cluster Specs on databricks
Run on tiny-debug-cluster-(no)gpu or another cluster with the following runtime specifications with CPU/non-GPU and GPU clusters, respectively:
- Runs on non-GPU cluster with 3 (or more) nodes on 7.4 ML runtime (nodes are 1+2 x m4.xlarge)
- Runs on GPU cluster with 3 (or more) nodes on 7.4 ML GPU runtime (nodes are 1+2 x g4dn.xlarge)
You do not need to "install" anything else in databricks as everything needed is pre-installed in the runtime environment on the right nodes.
Set up checkpoint location
The next cell creates a directory for saved checkpoint models. Databricks recommends saving training data under dbfs:/ml, which maps to file:/dbfs/ml on driver and worker nodes.
PYTORCH_DIR = '/dbfs/ml/horovod_pytorch'
Prepare single-node code
First you need to have working single-node PyTorch code. This is modified from Horovod's PyTorch MNIST Example.
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
# Specify training parameters
batch_size = 100
num_epochs = 5
momentum = 0.5
log_interval = 100
def train_one_epoch(model, device, data_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(data_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(data_loader) * len(data),
100. * batch_idx / len(data_loader), loss.item()))
from time import time
import os
# LOG_DIR = os.path.join(PYTORCH_DIR, str(time()), 'MNISTDemo')
LOG_DIR = os.path.join(PYTORCH_DIR, 'MNISTDemo')
os.makedirs(LOG_DIR)
def save_checkpoint(model, optimizer, epoch):
filepath = LOG_DIR + '/checkpoint-{epoch}.pth.tar'.format(epoch=epoch)
state = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(state, filepath)
import torch.optim as optim
from torchvision import datasets, transforms
def train(learning_rate):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_dataset = datasets.MNIST(
'data',
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]))
data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
for epoch in range(1, num_epochs + 1):
train_one_epoch(model, device, data_loader, optimizer, epoch)
save_checkpoint(model, optimizer, epoch)
Run the train function you just created to train a model on the driver node.
# Runs in 49.65 seconds on 3 node GPU cluster
# Runs in 118.2 seconds on 3 node non-GPU cluster
train(learning_rate = 0.001)
Migrate to HorovodRunner
HorovodRunner takes a Python method that contains deep learning training code with Horovod hooks. HorovodRunner pickles the method on the driver and distributes it to Spark workers. A Horovod MPI job is embedded as a Spark job using barrier execution mode.
import horovod.torch as hvd
from sparkdl import HorovodRunner
def train_hvd(learning_rate):
# Initialize Horovod
hvd.init()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type == 'cuda':
# Pin GPU to local rank
torch.cuda.set_device(hvd.local_rank())
train_dataset = datasets.MNIST(
# Use different root directory for each worker to avoid conflicts
root='data-%d'% hvd.rank(),
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
)
from torch.utils.data.distributed import DistributedSampler
# Configure the sampler so that each worker gets a distinct sample of the input dataset
train_sampler = DistributedSampler(train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
# Use train_sampler to load a different sample of data on each worker
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler)
model = Net().to(device)
# The effective batch size in synchronous distributed training is scaled by the number of workers
# Increase learning_rate to compensate for the increased batch size
optimizer = optim.SGD(model.parameters(), lr=learning_rate * hvd.size(), momentum=momentum)
# Wrap the local optimizer with hvd.DistributedOptimizer so that Horovod handles the distributed optimization
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
# Broadcast initial parameters so all workers start with the same parameters
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
for epoch in range(1, num_epochs + 1):
train_one_epoch(model, device, train_loader, optimizer, epoch)
# Save checkpoints only on worker 0 to prevent conflicts between workers
if hvd.rank() == 0:
save_checkpoint(model, optimizer, epoch)
Now that you have defined a training function with Horovod, you can use HorovodRunner to distribute the work of training the model.
The HorovodRunner parameter np sets the number of processes. This example uses a cluster with two workers, each with a single GPU, so set np=2. (If you use np=-1, HorovodRunner trains using a single process on the driver node.)
# Runs in 51.63 seconds on 3 node GPU cluster
# Runs in 96.6 seconds on 3 node non-GPU cluster
hr = HorovodRunner(np=-1)
hr.run(train_hvd, learning_rate = 0.001)
!ls /dbfs/ml/
Under the hood, HorovodRunner takes a Python method that contains deep learning training code with Horovod hooks. HorovodRunner pickles the method on the driver and distributes it to Spark workers. A Horovod MPI job is embedded as a Spark job using the barrier execution mode. The first executor collects the IP addresses of all task executors using BarrierTaskContext and triggers a Horovod job using mpirun. Each Python MPI process loads the pickled user program, deserializes it, and runs it.
For more information, see HorovodRunner API documentation.
Scalable Analysis of a Massive Knowledge Graph
Project members:
- Filip Cornell, KTH
- Yifei Jin, KTH
- Joel Oskarsson, LiU
- Tianyi Zho, KTH
Introduction
The aim of this project is to demonstrate how scalable data mining can be performed for massive knowledge graphs. We utilize multiple techniques to extract patterns from knowledge graphs, but focus in particular on the use of motif mining. The dataset used is the WikiKG90Mv2 knowledge graph, which contains entities and relations from the Wikidata knowledge base.
Dataset
Wikidata is an online knowledge base that can be openly edited by anyone. Most people interact with Wikidata mainly through the parts of it used in Wikipedia, but Wikidata also extends past information found in Wikipedia pages.
The ogbl-wikikg2 is a knowledge graph built from relations found in Wikidata. It contains 2,500,604 nodes, corresponding to different entities in the knowledge base, and 601,062,811 directed edges, corresponding to relations between the entitites. There are in total 535 unique relations and each edge in the graph corresponds to one of these relations. If we re-use the example found in the dataset description, there is an edge (relation) of type citizen of from the node (entity) Geoffrey Hinton to the node (entity) Canada. Another example of a small knowledge graph is given below.
By Jayarathina - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=37135596
We work with the "training" subgraph. In the raw data the entities and relations are only identified with a unique id number, but the dataset also comes with textual names and short descriptions of all entitites and relations.
We will make use of the GraphFrames package in order to efficiently work with the graph. GraphFrames allows operation that work directly on the network representation and on Dataframes containing all nodes and edges. We will utilize this dual-representation throughout the project.
Motif mining
Motifs are (typically small) reocurring subgraphs in some larger graph. The occurence of such patterns is often interesting to study in order to study the larger graph. An incredibly simple motif would be that of just two nodes connected by an edge, (A) --> (B). If we count the number of times this motif occurs in a graph we will surely end up with just the number of edges in the graph (not very interesting). If we instead think of a motif corresponding to directed cycles of some length \(n\) ((A) --> (B) --> ... --> (A)), finding the occurences of these motifs in the graph is far more interesting. As will be shown throughout our notebooks, many tasks can be tackled by methods that use motif mining at its core. This type of data mining can give valueable insights about the knowledge graph and its entities.
The problem of finding motifs has been well studied in the scientific litterature (see for example and). GraphFrames comes with its own efficient implementation of motif finding through the find method. This method takes as input a string describing the motif pattern to be found. For example graph.find("(a)-[r]->(b)") looks for all edges and returns a dataframe with the different values of a, b and r found.
This notebook
This first notebook describes how we load the data from a server into the databricks distributed file storage. The full dataset is stored as a zip-file so we then have to extract it and locate the files of interest.
// Start by importing some useful packages
import org.apache.spark.ml._
import org.apache.spark.ml.feature._
import org.apache.spark.sql.DataFrame
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
import org.apache.spark.ml._
import org.apache.spark.ml.feature._
import org.apache.spark.sql.DataFrame
import org.graphframes.GraphFrame
import org.apache.spark.sql.functions._
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
// Copy dataset from the server where it is stored
FileUtils.copyURLToFile(new URL("http://snap.stanford.edu/ogb/data/linkproppred/wikikg-v2.zip"), new File("/tmp/wikikg-v2.zip"))
ls /tmp/
unzip /tmp/wikikg-v2.zip
cp -r file:///databricks/driver/wikikg-v2 dbfs:///wikikg-v2
res1: Boolean = true
Now we should have the data on disk. Let's load it into a spark dataframe and display some of it to make sure it looks as we expect.
val df = spark.read.option("sep", ",").csv("dbfs:///wikikg-v2/original/train_2015.csv.gz")
df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
df.take(3)
res1: Array[org.apache.spark.sql.Row] = Array([Q8016027,P166,Q12177413], [Q6986100,P106,Q82955], [Q231256,P31,Q5])
display(df)
| _c0 | _c1 | _c2 |
|---|---|---|
| Q8016027 | P166 | Q12177413 |
| Q6986100 | P106 | Q82955 |
| Q231256 | P31 | Q5 |
| Q14086213 | P27 | Q29 |
| Q4134811 | P31 | Q16521 |
| Q1396316 | P27 | Q30 |
| Q15378563 | P106 | Q1281618 |
| Q16205843 | P31 | Q5 |
| Q16661298 | P21 | Q6581097 |
| Q4662361 | P21 | Q6581097 |
| Q966926 | P161 | Q1520157 |
| Q3067464 | P1412 | Q397 |
| Q6309309 | P106 | Q2526255 |
| Q3447470 | P47 | Q3451352 |
| Q7736291 | P57 | Q1526143 |
| Q19317886 | P180 | Q20460 |
| Q977232 | P735 | Q19819760 |
| Q515552 | P1344 | Q840654 |
| Q5939586 | P735 | Q2190619 |
| Q16854393 | P31 | Q5 |
| Q7730688 | P264 | Q202585 |
| Q548803 | P21 | Q6581097 |
| Q2964983 | P31 | Q5 |
| Q825080 | P21 | Q6581097 |
| Q5337564 | P21 | Q6581097 |
| Q508214 | P21 | Q6581097 |
| Q7880918 | P106 | Q82955 |
| Q630446 | P463 | Q674715 |
| Q9652 | P17 | Q30 |
| Q2076002 | P20 | Q370131 |
| Q3166748 | P39 | Q3044918 |
| Q4466355 | P171 | Q4479361 |
| Q16007300 | P27 | Q183 |
| Q10284786 | P21 | Q6581097 |
| Q774732 | P495 | Q30 |
| Q5497015 | P31 | Q5 |
| Q11716562 | P735 | Q1679656 |
| Q3417253 | P735 | Q18186323 |
| Q1688718 | P641 | Q32112 |
| Q2306219 | P31 | Q34442 |
| Q10647157 | P105 | Q34740 |
| Q7316144 | P264 | Q6693968 |
| Q17610406 | P31 | Q4167836 |
| Q1218313 | P161 | Q727086 |
| Q6513697 | P69 | Q7895785 |
| Q6418548 | P50 | Q931105 |
| Q1059119 | P155 | Q744320 |
| Q7788716 | P31 | Q5 |
| Q780951 | P144 | Q655296 |
| Q5232826 | P108 | Q49210 |
| Q3158937 | P27 | Q142 |
| Q835566 | P463 | Q684415 |
| Q4755089 | P735 | Q18177321 |
| Q2285451 | P21 | Q6581097 |
| Q8073207 | P155 | Q5979066 |
| Q3991336 | P272 | Q179200 |
| Q1351354 | P735 | Q15277251 |
| Q15132021 | P166 | Q178473 |
| Q2637771 | P735 | Q18105736 |
| Q16137 | P150 | Q101628 |
| Q19009324 | P31 | Q79007 |
| Q172696 | P17 | Q159 |
| Q5180906 | P735 | Q2671794 |
| Q3178861 | P735 | Q2563703 |
| Q504421 | P27 | Q145 |
| Q4131195 | P166 | Q185493 |
| Q943194 | P54 | Q81888 |
| Q3623735 | P161 | Q3972409 |
| Q9303561 | P21 | Q6581072 |
| Q3920445 | P54 | Q5324397 |
| Q558817 | P27 | Q183 |
| Q17345057 | P669 | Q19639657 |
| Q5932255 | P69 | Q1247373 |
| Q6370576 | P21 | Q6581097 |
| Q3520331 | P161 | Q2959515 |
| Q3704450 | P161 | Q2323644 |
| Q508335 | P106 | Q482980 |
| Q5294185 | P607 | Q362 |
| Q4722203 | P31 | Q5 |
| Q1511958 | P27 | Q183 |
| Q4316095 | P31 | Q5 |
| Q3418881 | P54 | Q192890 |
| Q865986 | P31 | Q5 |
| Q1794279 | P31 | Q5 |
| Q3502702 | P161 | Q2818919 |
| Q3521026 | P31 | Q24862 |
| Q3918700 | P19 | Q2280 |
| Q9389678 | P735 | Q16282870 |
| Q3260285 | P19 | Q1479 |
| Q5701959 | P106 | Q12912932 |
| Q5920916 | P69 | Q49088 |
| Q1587600 | P735 | Q1587364 |
| Q3876042 | P27 | Q145 |
| Q17113349 | P102 | Q190219 |
| Q2234084 | P105 | Q34740 |
| Q7407726 | P27 | Q664 |
| Q698967 | P166 | Q16787486 |
| Q14913235 | P682 | Q14818120 |
| Q3174252 | P106 | Q11063 |
| Q1648793 | P106 | Q4964182 |
| Q17610634 | P276 | Q937679 |
| Q1874649 | P39 | Q18887908 |
| Q2049982 | P106 | Q33999 |
| Q6828159 | P69 | Q458393 |
| Q737544 | P31 | Q3863 |
| Q6406769 | P19 | Q1741 |
| Q101976 | P106 | Q177220 |
| Q4908234 | P21 | Q6581097 |
| Q4795427 | P21 | Q6581097 |
| Q119477 | P106 | Q4964182 |
| Q2756109 | P27 | Q142 |
| Q2000255 | P123 | Q861799 |
| Q4783363 | P123 | Q122741 |
| Q1291457 | P19 | Q47611 |
| Q6197083 | P19 | Q2559651 |
| Q1055332 | P136 | Q2975633 |
| Q5649107 | P19 | Q54349 |
| Q2166440 | P19 | Q1874 |
| Q18638756 | P735 | Q923 |
| Q3986885 | P495 | Q30 |
| Q17291504 | P669 | Q2681969 |
| Q1486 | P190 | Q174 |
| Q1374461 | P54 | Q729560 |
| Q6250566 | P19 | Q18125 |
| Q560613 | P106 | Q49757 |
| Q2174633 | P47 | Q3452571 |
| Q5544717 | P106 | Q81096 |
| Q5658939 | P106 | Q937857 |
| Q6457069 | P61 | Q983620 |
| Q11084438 | P17 | Q148 |
| Q15971283 | P27 | Q222 |
| Q17395516 | P276 | Q2694686 |
| Q11364927 | P734 | Q16877751 |
| Q18121831 | P31 | Q4167836 |
| Q2922552 | P31 | Q24862 |
| Q2390657 | P31 | Q5 |
| Q11977111 | P735 | Q6979750 |
| Q725452 | P54 | Q1056948 |
| Q3084069 | P106 | Q639669 |
| Q328074 | P106 | Q33999 |
| Q17341508 | P186 | Q287 |
| Q19248873 | P156 | Q19248875 |
| Q6205678 | P106 | Q860918 |
| Q275637 | P27 | Q794 |
| Q3159906 | P735 | Q941049 |
| Q6247971 | P106 | Q42973 |
| Q207265 | P17 | Q213 |
| Q1468927 | P27 | Q29 |
| Q4422738 | P31 | Q5 |
| Q4661693 | P123 | Q671855 |
| Q3039230 | P31 | Q783794 |
| Q6646324 | P155 | Q5007829 |
| Q7052677 | P106 | Q6625963 |
| Q1449121 | P21 | Q6581097 |
| Q1609104 | P166 | Q17412908 |
| Q1305581 | P106 | Q2306091 |
| Q124102 | P19 | Q78 |
| Q16983652 | P21 | Q6581097 |
| Q1079353 | P19 | Q240071 |
| Q15071428 | P136 | Q1344 |
| Q17594823 | P131 | Q1101 |
| Q9353218 | P31 | Q5 |
| Q16552876 | P31 | Q15416 |
| Q4739661 | P21 | Q6581072 |
| Q6488439 | P161 | Q4901125 |
| Q1199120 | P150 | Q10864925 |
| Q1582596 | P19 | Q554051 |
| Q15993231 | P106 | Q40348 |
| Q11622672 | P31 | Q5 |
| Q3526704 | P106 | Q82955 |
| Q163898 | P58 | Q1356749 |
| Q3275776 | P136 | Q179700 |
| Q525301 | P27 | Q36 |
| Q216750 | P20 | Q100 |
| Q16988469 | P106 | Q330679 |
| Q15431276 | P166 | Q10905276 |
| Q7457298 | P344 | Q3057187 |
| Q76125 | P19 | Q1794 |
| Q6648 | P31 | Q577 |
| Q1438771 | P19 | Q268219 |
| Q17397396 | P669 | Q18956977 |
| Q944922 | P21 | Q6581097 |
| Q78475 | P20 | Q1741 |
| Q8263992 | P106 | Q43845 |
| Q231904 | P31 | Q484170 |
| Q1797274 | P31 | Q1149652 |
| Q16221351 | P21 | Q6581097 |
| Q648663 | P196 | Q2179 |
| Q4919349 | P175 | Q469901 |
| Q7041772 | P57 | Q2582445 |
| Q731938 | P31 | Q783794 |
| Q741624 | P31 | Q5 |
| Q91413 | P47 | Q82704 |
| Q2965386 | P106 | Q13415036 |
| Q1200098 | P39 | Q29182 |
| Q19255461 | P131 | Q1025079 |
| Q5407804 | P735 | Q545971 |
| Q1599704 | P21 | Q6581097 |
| Q9221193 | P31 | Q4167836 |
| Q733722 | P162 | Q925604 |
| Q7620527 | P108 | Q174710 |
| Q6459314 | P137 | Q1092839 |
| Q17446036 | P669 | Q18951829 |
| Q827351 | P106 | Q82955 |
| Q1603600 | P27 | Q40 |
| Q19358358 | P607 | Q362 |
| Q1630416 | P156 | Q3468482 |
| Q2645470 | P735 | Q3480335 |
| Q14519610 | P20 | Q84125 |
| Q4513020 | P21 | Q6581097 |
| Q12165429 | P735 | Q830350 |
| Q686132 | P197 | Q657879 |
| Q3852094 | P106 | Q2004963 |
| Q2888251 | P195 | Q3329787 |
| Q4017905 | P27 | Q38 |
| Q363400 | P106 | Q28389 |
| Q5672439 | P27 | Q145 |
| Q6221909 | P108 | Q210175 |
| Q1530053 | P161 | Q1243049 |
| Q969302 | P735 | Q16799105 |
| Q17811548 | P735 | Q2260734 |
| Q438272 | P161 | Q3315571 |
| Q17319464 | P669 | Q19293809 |
| Q1904546 | P106 | Q11774891 |
| Q1361315 | P106 | Q937857 |
| Q69060 | P31 | Q748149 |
| Q11461955 | P102 | Q232595 |
| Q17373950 | P1435 | Q916333 |
| Q128434 | P735 | Q18534268 |
| Q1357974 | P735 | Q18028491 |
| Q99270 | P102 | Q7320 |
| Q3099087 | P20 | Q90 |
| Q5078636 | P106 | Q193391 |
| Q1066528 | P1412 | Q150 |
| Q1683377 | P106 | Q2306091 |
| Q265641 | P21 | Q6581072 |
| Q3142763 | P31 | Q5 |
| Q17120059 | P31 | Q5 |
| Q2504707 | P735 | Q18515951 |
| Q15193 | P166 | Q15831432 |
| Q682102 | P65 | Q191684 |
| Q291906 | P735 | Q391321 |
| Q3523004 | P161 | Q3122043 |
| Q7575854 | P161 | Q7920794 |
| Q742370 | P20 | Q259497 |
| Q2326570 | P54 | Q1974241 |
| Q15427472 | P161 | Q1521718 |
| Q16106519 | P21 | Q6581097 |
| Q566094 | P1412 | Q188 |
| Q16264118 | P19 | Q80011 |
| Q11728505 | P21 | Q6581097 |
| Q3544837 | P31 | Q5 |
| Q17464572 | P1435 | Q916333 |
| Q121203 | P17 | Q843 |
| Q2597650 | P86 | Q494596 |
| Q2384009 | P19 | Q146530 |
| Q3262309 | P27 | Q142 |
| Q4746747 | P106 | Q19595175 |
| Q478841 | P47 | Q477575 |
| Q3807782 | P735 | Q18609696 |
| Q7306161 | P155 | Q4597077 |
| Q788536 | P105 | Q35409 |
| Q1343410 | P150 | Q11060628 |
| Q1224020 | P106 | Q1234713 |
| Q7323977 | P21 | Q6581097 |
| Q18666175 | P360 | Q5 |
| Q11328839 | P264 | Q1062659 |
| Q150939 | P17 | Q20 |
| Q325621 | P57 | Q960868 |
| Q4023328 | P734 | Q17041572 |
| Q5233147 | P31 | Q5 |
| Q7759503 | P155 | Q7318993 |
| Q1758653 | P19 | Q2865 |
| Q701977 | P272 | Q122865 |
| Q95394 | P27 | Q183 |
| Q9648 | P85 | Q40807 |
| Q14537090 | P31 | Q13406463 |
| Q3814258 | P106 | Q674426 |
| Q15992967 | P735 | Q15921732 |
| Q2263892 | P61 | Q3849346 |
| Q17542119 | P27 | Q142 |
| Q16025645 | P39 | Q382617 |
| Q823819 | P21 | Q6581097 |
| Q7860058 | P106 | Q33999 |
| Q82644 | P47 | Q103244 |
| Q6170242 | P39 | Q18524027 |
| Q359568 | P463 | Q83172 |
| Q1262649 | P463 | Q320642 |
| Q5399381 | P735 | Q1474085 |
| Q14133684 | P17 | Q148 |
| Q6525755 | P27 | Q16 |
| Q1894107 | P27 | Q183 |
| Q1629775 | P27 | Q183 |
| Q1398238 | P735 | Q220546 |
| Q13426472 | P131 | Q13415446 |
| Q19247919 | P156 | Q19247920 |
| Q3903206 | P735 | Q3903143 |
| Q3588560 | P735 | Q13426635 |
| Q15446966 | P106 | Q36834 |
| Q6097775 | P106 | Q937857 |
| Q5254991 | P106 | Q33999 |
| Q818132 | P20 | Q139427 |
| Q1626597 | P287 | Q15112244 |
| Q2613276 | P155 | Q6420067 |
| Q2351637 | P19 | Q28441 |
| Q116480 | P31 | Q506240 |
| Q6027756 | P31 | Q783794 |
| Q11533372 | P734 | Q4342082 |
| Q499613 | P97 | Q521109 |
| Q1426 | P1344 | Q8558 |
| Q5730360 | P31 | Q5 |
| Q4800264 | P108 | Q129421 |
| Q249665 | P31 | Q484170 |
| Q1724331 | P150 | Q860644 |
| Q1197991 | P150 | Q14342442 |
| Q3766136 | P106 | Q42973 |
| Q9388019 | P166 | Q15715250 |
| Q1037672 | P166 | Q705153 |
| Q5563183 | P106 | Q82955 |
| Q1111239 | P21 | Q6581097 |
| Q616023 | P19 | Q1486 |
| Q17291276 | P17 | Q55 |
| Q7348773 | P106 | Q82955 |
| Q18882921 | P816 | Q18882866 |
| Q2572455 | P19 | Q2833 |
| Q3613084 | P155 | Q3824028 |
| Q149126 | P155 | Q6423276 |
| Q596209 | P27 | Q142 |
| Q279418 | P21 | Q6581072 |
| Q3709228 | P131 | Q494192 |
| Q4307556 | P19 | Q997029 |
| Q4656085 | P840 | Q84 |
| Q1584631 | P39 | Q17521638 |
| Q372013 | P21 | Q6581097 |
| Q889831 | P31 | Q5 |
| Q1745654 | P21 | Q6581097 |
| Q109783 | P106 | Q81096 |
| Q1605886 | P6 | Q14033950 |
| Q403878 | P735 | Q1173883 |
| Q5163513 | P735 | Q679755 |
| Q7340283 | P21 | Q6581097 |
| Q18177861 | P170 | Q148475 |
| Q1478712 | P31 | Q34442 |
| Q19404255 | P138 | Q131219 |
| Q375068 | P40 | Q1337618 |
| Q1181198 | P676 | Q1744 |
| Q1884989 | P106 | Q82955 |
| Q3081458 | P27 | Q142 |
| Q132901 | P61 | Q11821 |
| Q1391931 | P937 | Q2795 |
| Q4059527 | P31 | Q5 |
| Q1359405 | P19 | Q37320 |
| Q5370568 | P155 | Q7760164 |
| Q208359 | P21 | Q6581097 |
| Q19912648 | P31 | Q3305213 |
| Q906411 | P31 | Q123705 |
| Q13423282 | P31 | Q3464665 |
| Q3804432 | P106 | Q937857 |
| Q7237271 | P106 | Q2526255 |
| Q17604628 | P131 | Q9822 |
| Q4364374 | P272 | Q179200 |
| Q3960601 | P69 | Q168756 |
| Q819749 | P31 | Q5 |
| Q17319044 | P31 | Q5 |
| Q10930013 | P131 | Q1196382 |
| Q15989863 | P27 | Q664 |
| Q720009 | P27 | Q30 |
| Q3048716 | P166 | Q12201378 |
| Q10786394 | P105 | Q34740 |
| Q1527178 | P31 | Q5 |
| Q120041 | P27 | Q183 |
| Q14075875 | P31 | Q5 |
| Q2423299 | P106 | Q189290 |
| Q10320251 | P21 | Q6581072 |
| Q155079 | P31 | Q5 |
| Q1603068 | P364 | Q1860 |
| Q1731462 | P19 | Q671781 |
| Q2150454 | P400 | Q188642 |
| Q1351098 | P19 | Q53896 |
| Q3012302 | P641 | Q542 |
| Q664009 | P527 | Q12887 |
| Q3046920 | P908 | Q14915512 |
| Q15069394 | P31 | Q5 |
| Q17301153 | P17 | Q55 |
| Q13571305 | P1383 | Q2716879 |
| Q17499932 | P161 | Q1399112 |
| Q506414 | P20 | Q495 |
| Q99288 | P21 | Q6581097 |
| Q18769445 | P527 | Q17461811 |
| Q16232245 | P31 | Q5 |
| Q4668973 | P39 | Q18691526 |
| Q988388 | P31 | Q3957 |
| Q2276063 | P264 | Q1430474 |
| Q5661768 | P31 | Q5 |
| Q7472016 | P31 | Q3863 |
| Q1237655 | P39 | Q29182 |
| Q318017 | P9 | Q3850456 |
| Q1397830 | P150 | Q22210 |
| Q2647423 | P106 | Q1930187 |
| Q1462855 | P102 | Q455038 |
| Q2327467 | P19 | Q6596 |
| Q3090678 | P22 | Q1890878 |
| Q3133282 | P131 | Q494011 |
| Q2321113 | P197 | Q2059086 |
| Q5006455 | P69 | Q13371 |
| Q1110084 | P31 | Q226730 |
| Q6218956 | P106 | Q2066131 |
| Q1849651 | P138 | Q2786563 |
| Q443067 | P27 | Q16 |
| Q17173279 | P19 | Q3616 |
| Q7009812 | P155 | Q7534753 |
| Q946349 | P31 | Q5 |
| Q2373513 | P39 | Q19360355 |
| Q6132938 | P735 | Q677191 |
| Q543710 | P27 | Q183 |
| Q27504 | P31 | Q5 |
| Q3010810 | P276 | Q2796641 |
| Q467927 | P734 | Q15080511 |
| Q14655016 | P106 | Q33999 |
| Q4001087 | P272 | Q3614072 |
| Q782490 | P155 | Q637139 |
| Q15982647 | P463 | Q618537 |
| Q5497133 | P106 | Q1930187 |
| Q28740 | P509 | Q188874 |
| Q7755958 | P449 | Q216108 |
| Q1203831 | P161 | Q1364909 |
| Q4813173 | P31 | Q14762205 |
| Q3484349 | P21 | Q6581097 |
| Q3492843 | P286 | Q7299749 |
| Q2924930 | P54 | Q298267 |
| Q736695 | P161 | Q3380016 |
| Q891363 | P20 | Q255802 |
| Q952045 | P106 | Q1281618 |
| Q2562617 | P106 | Q211346 |
| Q18334968 | P735 | Q1985538 |
| Q3294004 | P106 | Q1930187 |
| Q1326980 | P19 | Q2807 |
| Q2293972 | P915 | Q65 |
| Q4514816 | P31 | Q5 |
| Q5944753 | P27 | Q29 |
| Q7638177 | P264 | Q917561 |
| Q670245 | P31 | Q484170 |
| Q108666 | P740 | Q84 |
| Q6787306 | P27 | Q258 |
| Q1675399 | P156 | Q1634438 |
| Q1863550 | P735 | Q1795088 |
| Q71790 | P31 | Q5 |
| Q7621505 | P175 | Q259429 |
| Q17306266 | P641 | Q5372 |
| Q6110588 | P735 | Q18028597 |
| Q456467 | P161 | Q470260 |
| Q2856948 | P495 | Q142 |
| Q106730 | P20 | Q496056 |
| Q260344 | P21 | Q6581072 |
| Q392696 | P162 | Q59259 |
| Q6172174 | P31 | Q5 |
| Q826906 | P27 | Q183 |
| Q3080822 | P106 | Q36180 |
| Q4329399 | P161 | Q106743 |
| Q2052882 | P734 | Q1688722 |
| Q1165070 | P31 | Q484170 |
| Q5267608 | P20 | Q40738 |
| Q364635 | P27 | Q183 |
| Q2562063 | P106 | Q3387717 |
| Q17597620 | P1435 | Q916333 |
| Q3588330 | P27 | Q142 |
| Q16210084 | P735 | Q4925477 |
| Q18844876 | P279 | Q725 |
| Q5298546 | P69 | Q1399299 |
| Q15710824 | P467 | Q2458227 |
| Q1200481 | P31 | Q1289426 |
| Q2982896 | P31 | Q483453 |
| Q980257 | P161 | Q719529 |
| Q98605 | P31 | Q5 |
| Q10436799 | P735 | Q18402099 |
| Q172381 | P735 | Q4927589 |
| Q1412278 | P84 | Q12174873 |
| Q12012136 | P735 | Q8079337 |
| Q1068361 | P31 | Q43183 |
| Q3099503 | P735 | Q19793321 |
| Q616668 | P131 | Q1725768 |
| Q12890820 | P31 | Q5 |
| Q2897825 | P31 | Q5 |
| Q1561551 | P19 | Q4120832 |
| Q7794480 | P31 | Q5 |
| Q15436770 | P27 | Q183 |
| Q16979881 | P27 | Q145 |
| Q251931 | P500 | Q42880 |
| Q2003245 | P47 | Q1001647 |
| Q5389676 | P21 | Q6581097 |
| Q17610179 | P156 | Q17610330 |
| Q167540 | P735 | Q14371254 |
| Q716134 | P31 | Q5 |
| Q92937 | P31 | Q5 |
| Q708788 | P27 | Q183 |
| Q1072227 | P21 | Q6581097 |
| Q1453347 | P31 | Q5 |
| Q363049 | P106 | Q2526255 |
| Q1788523 | P27 | Q183 |
| Q1097677 | P106 | Q2066131 |
| Q1618378 | P31 | Q5 |
| Q4772624 | P21 | Q6581097 |
| Q796898 | P279 | Q1022125 |
| Q7597634 | P735 | Q17862013 |
| Q13945591 | P31 | Q12808966 |
| Q17491796 | P31 | Q3305213 |
| Q6137778 | P735 | Q677191 |
| Q1365502 | P19 | Q3777 |
| Q14240356 | P27 | Q183 |
| Q2371597 | P131 | Q235297 |
| Q362790 | P21 | Q6581097 |
| Q1581704 | P734 | Q8157228 |
| Q3767701 | P21 | Q6581097 |
| Q1756968 | P131 | Q302778 |
| Q4643245 | P179 | Q2744 |
| Q1468795 | P31 | Q5 |
| Q372888 | P1066 | Q658109 |
| Q2159505 | P735 | Q13564349 |
| Q3142278 | P437 | Q633454 |
| Q2078606 | P54 | Q170703 |
| Q15456735 | P27 | Q183 |
| Q4773154 | P31 | Q5 |
| Q3610189 | P21 | Q6581097 |
| Q18619597 | P186 | Q4259259 |
| Q1432671 | P31 | Q11424 |
| Q6184062 | P119 | Q252312 |
| Q1637939 | P161 | Q230424 |
| Q15073944 | P971 | Q516405 |
| Q1901634 | P108 | Q154804 |
| Q3588525 | P119 | Q1092107 |
| Q365909 | P108 | Q186285 |
| Q1613120 | P108 | Q152171 |
| Q3809209 | P108 | Q49112 |
| Q7034144 | P31 | Q16521 |
| Q152480 | P27 | Q28 |
| Q11779798 | P27 | Q36 |
| Q1909669 | P102 | Q49768 |
| Q5553032 | P31 | Q5 |
| Q4143281 | P131 | Q7677 |
| Q3959252 | P31 | Q5 |
| Q5928071 | P31 | Q5 |
| Q10513187 | P21 | Q6581097 |
| Q16225870 | P735 | Q18245781 |
| Q18352608 | P108 | Q622664 |
| Q1315907 | P495 | Q30 |
| Q2993591 | P20 | Q157246 |
| Q10401493 | P31 | Q16521 |
| Q1612701 | P735 | Q1158570 |
| Q267106 | P1344 | Q8415 |
| Q5543762 | P69 | Q7055270 |
| Q2790698 | P31 | Q641226 |
| Q6251982 | P21 | Q6581097 |
| Q526300 | P54 | Q19593 |
| Q18402350 | P106 | Q486748 |
| Q5361279 | P27 | Q30 |
| Q1023602 | P17 | Q142 |
| Q12351891 | P1412 | Q143 |
| Q729416 | P31 | Q482994 |
| Q69792 | P19 | Q1726 |
| Q335059 | P937 | Q646980 |
| Q378893 | P27 | Q739 |
| Q10354246 | P31 | Q5 |
| Q2130002 | P47 | Q1300392 |
| Q3026655 | P106 | Q14089670 |
| Q19359691 | P361 | Q19220658 |
| Q1650174 | P27 | Q30 |
| Q321122 | P69 | Q258464 |
| Q6519747 | P69 | Q219563 |
| Q6790850 | P31 | Q5 |
| Q191156 | P105 | Q334460 |
| Q3243531 | P21 | Q6581072 |
| Q19655965 | P138 | Q150747 |
| Q1554558 | P27 | Q145 |
| Q586589 | P162 | Q6758665 |
| Q879932 | P735 | Q364753 |
| Q1512168 | P161 | Q234581 |
| Q2285321 | P21 | Q6581072 |
| Q1717030 | P39 | Q17344251 |
| Q433728 | P69 | Q258464 |
| Q2355147 | P31 | Q34442 |
| Q2875177 | P9 | Q13427665 |
| Q14129509 | P27 | Q29 |
| Q1126256 | P150 | Q2113624 |
| Q361550 | P27 | Q183 |
| Q3633077 | P161 | Q6807665 |
| Q14077770 | P364 | Q1860 |
| Q17099809 | P21 | Q6581097 |
| Q2813614 | P166 | Q2547676 |
| Q18758936 | P360 | Q5 |
| Q19242427 | P155 | Q19242425 |
| Q879921 | P27 | Q30 |
| Q45250 | P166 | Q16336085 |
| Q556844 | P21 | Q6581072 |
| Q11445861 | P39 | Q17506823 |
| Q19468639 | P17 | Q55 |
| Q1724511 | P150 | Q200041 |
| Q1318697 | P105 | Q34740 |
| Q4864032 | P54 | Q849315 |
| Q16266468 | P155 | Q16746473 |
| Q3278502 | P17 | Q142 |
| Q333808 | P20 | Q320378 |
| Q745505 | P19 | Q326879 |
| Q10857434 | P39 | Q19803234 |
| Q93023 | P106 | Q36180 |
| Q1176842 | P178 | Q1889419 |
| Q2945030 | P123 | Q1371744 |
| Q254243 | P31 | Q484170 |
| Q3260689 | P166 | Q11593374 |
| Q5986988 | P54 | Q1128631 |
| Q18774690 | P17 | Q55 |
| Q787672 | P106 | Q639669 |
| Q156300 | P509 | Q12152 |
| Q1746124 | P735 | Q4927128 |
| Q1536603 | P361 | Q2239885 |
| Q4123384 | P27 | Q159 |
| Q14945515 | P31 | Q5 |
| Q4919827 | P69 | Q486156 |
| Q2900832 | P641 | Q542 |
| Q123846 | P27 | Q183 |
| Q5082576 | P69 | Q1149089 |
| Q2652245 | P21 | Q6581097 |
| Q265050 | P17 | Q17 |
| Q3092343 | P19 | Q90 |
| Q6534502 | P682 | Q14878786 |
| Q1620131 | P19 | Q497200 |
| Q2154721 | P31 | Q5 |
| Q8057631 | P361 | Q1658029 |
| Q18147747 | P527 | Q325648 |
| Q6883161 | P159 | Q35765 |
| Q599510 | P279 | Q862597 |
| Q3247743 | P161 | Q2929411 |
| Q3361 | P150 | Q836607 |
| Q4766504 | P735 | Q558067 |
| Q2915712 | P31 | Q188509 |
| Q3791066 | P161 | Q1179412 |
| Q5820724 | P21 | Q6581097 |
| Q11379 | P279 | Q35120 |
| Q764578 | P22 | Q573424 |
| Q1587049 | P108 | Q32120 |
| Q1432423 | P27 | Q183 |
| Q1544298 | P108 | Q122453 |
| Q3387066 | P21 | Q6581097 |
| Q12771789 | P21 | Q6581097 |
| Q110146 | P1344 | Q8558 |
| Q12176843 | P735 | Q617272 |
| Q7441460 | P27 | Q30 |
| Q13815768 | P106 | Q855091 |
| Q7287753 | P106 | Q16145150 |
| Q3545150 | P19 | Q11434728 |
| Q1298397 | P527 | Q19273492 |
| Q114172 | P106 | Q36180 |
| Q7980788 | P175 | Q7729259 |
| Q3767590 | P106 | Q201788 |
| Q3349229 | P21 | Q6581072 |
| Q530033 | P106 | Q937857 |
| Q17334994 | P1204 | Q1693 |
| Q49351 | P119 | Q1574424 |
| Q3204923 | P161 | Q3085009 |
| Q11512722 | P156 | Q11449940 |
| Q584535 | P54 | Q912247 |
| Q395755 | P19 | Q2044 |
| Q275960 | P86 | Q2121109 |
| Q7350769 | P21 | Q6581097 |
| Q1222145 | P166 | Q10905334 |
| Q3090265 | P1343 | Q17166797 |
| Q7612227 | P19 | Q128114 |
| Q3521378 | P735 | Q19803513 |
| Q309459 | P31 | Q11424 |
| Q2486115 | P17 | Q30 |
| Q1356727 | P19 | Q1489 |
| Q6186526 | P735 | Q2227398 |
| Q3002796 | P31 | Q5 |
| Q3823896 | P58 | Q1343394 |
| Q4792831 | P27 | Q16 |
| Q4306855 | P19 | Q898 |
| Q418099 | P735 | Q4700926 |
| Q5088940 | P264 | Q183387 |
| Q109290 | P108 | Q681250 |
| Q3452529 | P17 | Q142 |
| Q1989101 | P69 | Q215539 |
| Q1227732 | P31 | Q5 |
| Q4483063 | P735 | Q15731576 |
| Q5483119 | P1344 | Q8544 |
| Q1516684 | P31 | Q3918 |
| Q1852242 | P21 | Q6581097 |
| Q138591 | P21 | Q6581097 |
| Q4087615 | P27 | Q30 |
| Q6709245 | P27 | Q30 |
| Q14910640 | P31 | Q16521 |
| Q325973 | P20 | Q220 |
| Q4275847 | P21 | Q6581097 |
| Q21376 | P131 | Q2150573 |
| Q723491 | P175 | Q286596 |
| Q799817 | P39 | Q29182 |
| Q5081265 | P21 | Q6581097 |
| Q3309191 | P27 | Q142 |
| Q1961283 | P27 | Q142 |
| Q199644 | P21 | Q6581097 |
| Q763242 | P19 | Q625091 |
| Q4666319 | P21 | Q6581097 |
| Q232789 | P54 | Q2739 |
| Q5112591 | P31 | Q659103 |
| Q660030 | P17 | Q142 |
| Q6461262 | P196 | Q2179 |
| Q591809 | P39 | Q29182 |
| Q5776365 | P156 | Q5776449 |
| Q3169038 | P171 | Q140435 |
| Q4138699 | P27 | Q41 |
| Q1520732 | P161 | Q93957 |
| Q3083977 | P1412 | Q397 |
| Q2040986 | P105 | Q7432 |
| Q18710362 | P171 | Q6544822 |
| Q3127840 | P735 | Q668885 |
| Q6242151 | P21 | Q6581097 |
| Q12022481 | P19 | Q270704 |
| Q41079 | P150 | Q571219 |
| Q1578559 | P21 | Q6581097 |
| Q6124922 | P27 | Q30 |
| Q11884465 | P21 | Q6581097 |
| Q17495989 | P186 | Q4259259 |
| Q1505686 | P102 | Q49763 |
| Q123057 | P735 | Q19264720 |
| Q3236684 | P69 | Q3047595 |
| Q5518466 | P31 | Q482994 |
| Q111258 | P21 | Q6581097 |
| Q7340315 | P21 | Q6581097 |
| Q14634026 | P162 | Q1522276 |
| Q3158480 | P27 | Q142 |
| Q1578490 | P106 | Q635734 |
| Q3384862 | P937 | Q90 |
| Q1212630 | P161 | Q2004024 |
| Q15040646 | P176 | Q463261 |
| Q16097054 | P21 | Q6581097 |
| Q5448544 | P54 | Q371136 |
| Q7082998 | P106 | Q82955 |
| Q1862750 | P735 | Q2658970 |
| Q5708659 | P264 | Q557632 |
| Q1912743 | P21 | Q6581097 |
| Q76624 | P21 | Q6581097 |
| Q12286383 | P161 | Q12279836 |
| Q3839964 | P27 | Q38 |
| Q14512358 | P31 | Q16521 |
| Q807487 | P106 | Q13365117 |
| Q2865080 | P31 | Q5 |
| Q627861 | P156 | Q712744 |
| Q3105726 | P735 | Q1675463 |
| Q1440286 | P31 | Q5 |
| Q2410737 | P197 | Q2229953 |
| Q17490971 | P186 | Q296955 |
| Q392783 | P161 | Q235278 |
| Q904686 | P21 | Q6581072 |
| Q1579823 | P31 | Q5 |
| Q351426 | P31 | Q5 |
| Q1727278 | P131 | Q701072 |
| Q641445 | P17 | Q35 |
| Q517824 | P21 | Q6581097 |
| Q2061133 | P106 | Q42603 |
| Q3068 | P150 | Q653380 |
| Q7688610 | P400 | Q23882 |
| Q4864522 | P54 | Q205033 |
| Q9001319 | P509 | Q29496 |
| Q5152881 | P155 | Q7755601 |
| Q1668661 | P31 | Q5 |
| Q2436828 | P17 | Q30 |
| Q3776054 | P161 | Q289020 |
| Q1705061 | P21 | Q6581097 |
| Q5726326 | P19 | Q861627 |
| Q7135261 | P27 | Q668 |
| Q18211541 | P735 | Q2102316 |
| Q19243435 | P31 | Q21199 |
| Q232323 | P53 | Q852111 |
| Q3059538 | P106 | Q42973 |
| Q4164772 | P166 | Q403569 |
| Q5806128 | P106 | Q483501 |
| Q148356 | P65 | Q191684 |
| Q2425611 | P735 | Q18115390 |
| Q4811406 | P27 | Q20 |
| Q5873935 | P31 | Q16521 |
| Q3833180 | P54 | Q1538348 |
| Q718029 | P27 | Q55 |
| Q12353687 | P735 | Q4925623 |
| Q17439083 | P17 | Q55 |
| Q1045289 | P27 | Q38 |
| Q4728548 | P175 | Q254748 |
| Q3726042 | P735 | Q16908530 |
| Q586650 | P106 | Q482980 |
| Q6223036 | P27 | Q408 |
| Q1054560 | P21 | Q6581097 |
| Q1634253 | P735 | Q839387 |
| Q7793136 | P735 | Q18002322 |
| Q5550671 | P607 | Q362 |
| Q1704544 | P20 | Q1715 |
| Q3629978 | P19 | Q174234 |
| Q439776 | P27 | Q884 |
| Q5240782 | P106 | Q12299841 |
| Q1780852 | P19 | Q485253 |
| Q2639899 | P944 | Q13011 |
| Q3706766 | P27 | Q38 |
| Q978042 | P21 | Q6581097 |
| Q55007 | P131 | Q16120 |
| Q495287 | P54 | Q2565016 |
| Q1716692 | P21 | Q6581097 |
| Q3166201 | P21 | Q6581097 |
| Q10856433 | P21 | Q6581097 |
| Q722653 | P54 | Q194116 |
| Q1387025 | P27 | Q30 |
| Q561504 | P106 | Q81096 |
| Q950911 | P106 | Q170790 |
| Q164527 | P17 | Q213 |
| Q143644 | P21 | Q6581097 |
| Q1438437 | P479 | Q178805 |
| Q1022 | P150 | Q727750 |
| Q389355 | P421 | Q6655 |
| Q2093520 | P509 | Q8454 |
| Q5045254 | P131 | Q694 |
| Q3100008 | P413 | Q2270380 |
| Q6272552 | P21 | Q6581097 |
| Q597975 | P27 | Q30 |
| Q2062480 | P21 | Q6581072 |
| Q6943701 | P161 | Q705477 |
| Q1601980 | P21 | Q6581072 |
| Q2097256 | P106 | Q13382576 |
| Q5504856 | P19 | Q124539 |
| Q1085538 | P196 | Q2179 |
| Q1101218 | P106 | Q1028181 |
| Q6846492 | P735 | Q361309 |
| Q696695 | P735 | Q750186 |
| Q2039114 | P20 | Q1741 |
| Q3185034 | P106 | Q783906 |
| Q6239051 | P166 | Q2427600 |
| Q728989 | P178 | Q739711 |
| Q5386205 | P54 | Q1148233 |
| Q24276 | P131 | Q228 |
| Q3441558 | P21 | Q6581097 |
| Q3390565 | P47 | Q3450641 |
| Q4175945 | P156 | Q4175753 |
| Q15427472 | P161 | Q90760 |
| Q7697274 | P31 | Q16521 |
| Q5314616 | P241 | Q1752901 |
| Q11630108 | P175 | Q266852 |
| Q2060744 | P735 | Q2117521 |
| Q19249212 | P155 | Q19249211 |
| Q631546 | P190 | Q566156 |
| Q581128 | P21 | Q6581097 |
| Q573817 | P166 | Q315026 |
| Q6286364 | P735 | Q471788 |
| Q1900652 | P31 | Q5 |
| Q259961 | P27 | Q145 |
| Q1352925 | P21 | Q6581097 |
| Q2481005 | P31 | Q5 |
| Q11550152 | P31 | Q5 |
| Q2307428 | P136 | Q860626 |
| Q1215771 | P106 | Q15059856 |
| Q28003 | P138 | Q981207 |
| Q531718 | P25 | Q269815 |
| Q15432782 | P31 | Q5 |
| Q4009559 | P180 | Q35500 |
| Q11338028 | P264 | Q8194234 |
| Q12795307 | P31 | Q5 |
| Q489111 | P19 | Q2807 |
| Q956947 | P641 | Q328716 |
| Q3340483 | P735 | Q7029481 |
| Q8364922 | P17 | Q20 |
| Q3177608 | P21 | Q6581097 |
| Q13635614 | P19 | Q7880 |
| Q1638132 | P21 | Q6581097 |
| Q8017253 | P21 | Q6581097 |
| Q1147949 | P136 | Q1057172 |
| Q1442905 | P31 | Q5 |
| Q4441393 | P27 | Q212 |
| Q3167699 | P27 | Q142 |
| Q88914 | P20 | Q1741 |
| Q1825280 | P106 | Q1028181 |
| Q3267066 | P106 | Q11774891 |
| Q4953897 | P21 | Q6581097 |
| Q1448741 | P31 | Q5 |
| Q3377600 | P19 | Q3549 |
| Q361297 | P106 | Q2462658 |
| Q1019463 | P166 | Q2727598 |
| Q2486041 | P16 | Q1852230 |
| Q7078743 | P31 | Q134556 |
| Q3838578 | P27 | Q38 |
| Q6907590 | P57 | Q311219 |
| Q7569036 | P364 | Q1860 |
| Q3084582 | P106 | Q250867 |
| Q336912 | P106 | Q42603 |
| Q1174833 | P106 | Q16267607 |
| Q77452 | P131 | Q1165 |
| Q4054640 | P20 | Q9248 |
| Q4061138 | P19 | Q2801 |
| Q5537133 | P31 | Q5 |
| Q5978761 | P175 | Q2248393 |
| Q1726422 | P27 | Q39 |
| Q16217373 | P27 | Q30 |
| Q212642 | P17 | Q142 |
| Q918881 | P17 | Q16 |
| Q17595057 | P31 | Q18762207 |
| Q5372051 | P735 | Q18121477 |
| Q7148414 | P31 | Q571 |
| Q5575620 | P735 | Q18404297 |
| Q5233173 | P106 | Q40348 |
| Q44902 | P27 | Q142 |
| Q6638165 | P156 | Q6641129 |
| Q756861 | P21 | Q6581072 |
| Q5795789 | P106 | Q82955 |
| Q17616366 | P31 | Q41176 |
| Q2285273 | P735 | Q634916 |
| Q2374013 | P31 | Q16970 |
| Q14598336 | P166 | Q17231624 |
| Q3557606 | P40 | Q561201 |
| Q15971609 | P36 | Q19566 |
| Q1458664 | P735 | Q14038597 |
| Q66378 | P17 | Q39 |
| Q5666275 | P31 | Q5 |
| Q5944327 | P21 | Q6581097 |
| Q7562956 | P735 | Q18201529 |
| Q1399879 | P106 | Q11774891 |
| Q2505482 | P127 | Q568743 |
| Q15434159 | P102 | Q49763 |
| Q62125 | P17 | Q183 |
| Q14086244 | P106 | Q82955 |
| Q6792807 | P21 | Q6581097 |
| Q3430946 | P106 | Q17486376 |
| Q1595237 | P31 | Q5 |
| Q362146 | P735 | Q1795260 |
| Q731499 | P741 | Q3039938 |
| Q5362464 | P39 | Q18654736 |
| Q11153932 | P27 | Q399 |
| Q1601853 | P108 | Q1051840 |
| Q1527054 | P735 | Q2190619 |
| Q16018600 | P607 | Q362 |
| Q590842 | P735 | Q1343668 |
| Q1659531 | P161 | Q445044 |
| Q2927024 | P31 | Q5 |
| Q1560877 | P131 | Q597 |
| Q2930282 | P161 | Q2389393 |
| Q5149087 | P156 | Q6875448 |
| Q15065337 | P27 | Q34266 |
| Q7044683 | P264 | Q2338889 |
| Q1063846 | P31 | Q5 |
| Q5909022 | P106 | Q36834 |
| Q371748 | P131 | Q53711 |
| Q4863959 | P31 | Q5 |
| Q845844 | P190 | Q67249 |
| Q11896452 | P735 | Q3817554 |
| Q936816 | P553 | Q918 |
| Q4944147 | P175 | Q2557820 |
| Q6512983 | P54 | Q48925 |
| Q1697265 | P27 | Q183 |
| Q170978 | P31 | Q8148 |
| Q6557797 | P102 | Q216082 |
| Q1753406 | P175 | Q2808 |
| Q3613930 | P106 | Q611644 |
| Q6762241 | P735 | Q18760860 |
| Q1219363 | P161 | Q358990 |
| Q2910096 | P27 | Q801 |
| Q15352 | P150 | Q15937 |
| Q4832644 | P1344 | Q8567 |
| Q4965165 | P106 | Q3282637 |
| Q7448399 | P21 | Q6581097 |
| Q2015910 | P31 | Q1201493 |
| Q5284862 | P21 | Q6581097 |
| Q5377936 | P123 | Q2744153 |
| Q5076344 | P21 | Q6581097 |
| Q908693 | P69 | Q49112 |
| Q5230765 | P27 | Q408 |
| Q3172593 | P106 | Q14972848 |
| Q2304393 | P136 | Q1443316 |
| Q6833784 | P21 | Q6581097 |
| Q3741059 | P21 | Q6581097 |
| Q19912132 | P195 | Q160236 |
| Q10719196 | P138 | Q1355965 |
| Q15635799 | P186 | Q40089 |
| Q1727931 | P21 | Q6581097 |
| Q2083880 | P136 | Q270948 |
| Q6312220 | P166 | Q12201526 |
| Q718825 | P54 | Q796179 |
| Q1687119 | P31 | Q5 |
| Q4758280 | P69 | Q499510 |
| Q1152657 | P162 | Q259593 |
| Q1170303 | P27 | Q30 |
| Q1468017 | P27 | Q183 |
| Q15447020 | P20 | Q1709 |
| Q2337200 | P27 | Q183 |
| Q350799 | P27 | Q25 |
| Q868577 | P21 | Q6581072 |
| Q3360690 | P31 | Q11424 |
| Q3903015 | P106 | Q82955 |
| Q7917352 | P102 | Q2399535 |
| Q5087189 | P106 | Q488205 |
| Q1005361 | P495 | Q17 |
| Q5106495 | P21 | Q6581097 |
| Q1460419 | P27 | Q36 |
| Q7828851 | P479 | Q273140 |
| Q13452 | P6 | Q14076448 |
| Q3807354 | P106 | Q937857 |
| Q3450538 | P47 | Q3451069 |
| Q7472058 | P61 | Q735603 |
| Q15069001 | P166 | Q185493 |
| Q5509758 | P175 | Q546573 |
Looks good! The main part of the dataset is now in databricks.
df.columns = Array("head", "rel","tail")
val list = List("src", "rel", "dst")
import spark.implicits._
val edgesDF = df.toDF(list:_*)
list: List[String] = List(src, rel, dst)
import spark.implicits._
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
import org.graphframes._
import org.graphframes._
// Copied from https://stackoverflow.com/questions/57513292/how-to-make-graphframe-from-edge-dataframe-only
val verticesDf = edgesDF.select("src").union(edgesDF.select("dst")).distinct().withColumnRenamed("src", "id")
val graph=GraphFrame(verticesDf,edgesDF)
verticesDf: org.apache.spark.sql.DataFrame = [id: string]
graph: org.graphframes.GraphFrame = GraphFrame(v:[id: string], e:[src: string, dst: string ... 1 more field])
val nEdges = graph.edges.count()
display(graph.edges)
| src | rel | dst |
|---|---|---|
| Q8016027 | P166 | Q12177413 |
| Q6986100 | P106 | Q82955 |
| Q231256 | P31 | Q5 |
| Q14086213 | P27 | Q29 |
| Q4134811 | P31 | Q16521 |
| Q1396316 | P27 | Q30 |
| Q15378563 | P106 | Q1281618 |
| Q16205843 | P31 | Q5 |
| Q16661298 | P21 | Q6581097 |
| Q4662361 | P21 | Q6581097 |
| Q966926 | P161 | Q1520157 |
| Q3067464 | P1412 | Q397 |
| Q6309309 | P106 | Q2526255 |
| Q3447470 | P47 | Q3451352 |
| Q7736291 | P57 | Q1526143 |
| Q19317886 | P180 | Q20460 |
| Q977232 | P735 | Q19819760 |
| Q515552 | P1344 | Q840654 |
| Q5939586 | P735 | Q2190619 |
| Q16854393 | P31 | Q5 |
| Q7730688 | P264 | Q202585 |
| Q548803 | P21 | Q6581097 |
| Q2964983 | P31 | Q5 |
| Q825080 | P21 | Q6581097 |
| Q5337564 | P21 | Q6581097 |
| Q508214 | P21 | Q6581097 |
| Q7880918 | P106 | Q82955 |
| Q630446 | P463 | Q674715 |
| Q9652 | P17 | Q30 |
| Q2076002 | P20 | Q370131 |
| Q3166748 | P39 | Q3044918 |
| Q4466355 | P171 | Q4479361 |
| Q16007300 | P27 | Q183 |
| Q10284786 | P21 | Q6581097 |
| Q774732 | P495 | Q30 |
| Q5497015 | P31 | Q5 |
| Q11716562 | P735 | Q1679656 |
| Q3417253 | P735 | Q18186323 |
| Q1688718 | P641 | Q32112 |
| Q2306219 | P31 | Q34442 |
| Q10647157 | P105 | Q34740 |
| Q7316144 | P264 | Q6693968 |
| Q17610406 | P31 | Q4167836 |
| Q1218313 | P161 | Q727086 |
| Q6513697 | P69 | Q7895785 |
| Q6418548 | P50 | Q931105 |
| Q1059119 | P155 | Q744320 |
| Q7788716 | P31 | Q5 |
| Q780951 | P144 | Q655296 |
| Q5232826 | P108 | Q49210 |
| Q3158937 | P27 | Q142 |
| Q835566 | P463 | Q684415 |
| Q4755089 | P735 | Q18177321 |
| Q2285451 | P21 | Q6581097 |
| Q8073207 | P155 | Q5979066 |
| Q3991336 | P272 | Q179200 |
| Q1351354 | P735 | Q15277251 |
| Q15132021 | P166 | Q178473 |
| Q2637771 | P735 | Q18105736 |
| Q16137 | P150 | Q101628 |
| Q19009324 | P31 | Q79007 |
| Q172696 | P17 | Q159 |
| Q5180906 | P735 | Q2671794 |
| Q3178861 | P735 | Q2563703 |
| Q504421 | P27 | Q145 |
| Q4131195 | P166 | Q185493 |
| Q943194 | P54 | Q81888 |
| Q3623735 | P161 | Q3972409 |
| Q9303561 | P21 | Q6581072 |
| Q3920445 | P54 | Q5324397 |
| Q558817 | P27 | Q183 |
| Q17345057 | P669 | Q19639657 |
| Q5932255 | P69 | Q1247373 |
| Q6370576 | P21 | Q6581097 |
| Q3520331 | P161 | Q2959515 |
| Q3704450 | P161 | Q2323644 |
| Q508335 | P106 | Q482980 |
| Q5294185 | P607 | Q362 |
| Q4722203 | P31 | Q5 |
| Q1511958 | P27 | Q183 |
| Q4316095 | P31 | Q5 |
| Q3418881 | P54 | Q192890 |
| Q865986 | P31 | Q5 |
| Q1794279 | P31 | Q5 |
| Q3502702 | P161 | Q2818919 |
| Q3521026 | P31 | Q24862 |
| Q3918700 | P19 | Q2280 |
| Q9389678 | P735 | Q16282870 |
| Q3260285 | P19 | Q1479 |
| Q5701959 | P106 | Q12912932 |
| Q5920916 | P69 | Q49088 |
| Q1587600 | P735 | Q1587364 |
| Q3876042 | P27 | Q145 |
| Q17113349 | P102 | Q190219 |
| Q2234084 | P105 | Q34740 |
| Q7407726 | P27 | Q664 |
| Q698967 | P166 | Q16787486 |
| Q14913235 | P682 | Q14818120 |
| Q3174252 | P106 | Q11063 |
| Q1648793 | P106 | Q4964182 |
| Q17610634 | P276 | Q937679 |
| Q1874649 | P39 | Q18887908 |
| Q2049982 | P106 | Q33999 |
| Q6828159 | P69 | Q458393 |
| Q737544 | P31 | Q3863 |
| Q6406769 | P19 | Q1741 |
| Q101976 | P106 | Q177220 |
| Q4908234 | P21 | Q6581097 |
| Q4795427 | P21 | Q6581097 |
| Q119477 | P106 | Q4964182 |
| Q2756109 | P27 | Q142 |
| Q2000255 | P123 | Q861799 |
| Q4783363 | P123 | Q122741 |
| Q1291457 | P19 | Q47611 |
| Q6197083 | P19 | Q2559651 |
| Q1055332 | P136 | Q2975633 |
| Q5649107 | P19 | Q54349 |
| Q2166440 | P19 | Q1874 |
| Q18638756 | P735 | Q923 |
| Q3986885 | P495 | Q30 |
| Q17291504 | P669 | Q2681969 |
| Q1486 | P190 | Q174 |
| Q1374461 | P54 | Q729560 |
| Q6250566 | P19 | Q18125 |
| Q560613 | P106 | Q49757 |
| Q2174633 | P47 | Q3452571 |
| Q5544717 | P106 | Q81096 |
| Q5658939 | P106 | Q937857 |
| Q6457069 | P61 | Q983620 |
| Q11084438 | P17 | Q148 |
| Q15971283 | P27 | Q222 |
| Q17395516 | P276 | Q2694686 |
| Q11364927 | P734 | Q16877751 |
| Q18121831 | P31 | Q4167836 |
| Q2922552 | P31 | Q24862 |
| Q2390657 | P31 | Q5 |
| Q11977111 | P735 | Q6979750 |
| Q725452 | P54 | Q1056948 |
| Q3084069 | P106 | Q639669 |
| Q328074 | P106 | Q33999 |
| Q17341508 | P186 | Q287 |
| Q19248873 | P156 | Q19248875 |
| Q6205678 | P106 | Q860918 |
| Q275637 | P27 | Q794 |
| Q3159906 | P735 | Q941049 |
| Q6247971 | P106 | Q42973 |
| Q207265 | P17 | Q213 |
| Q1468927 | P27 | Q29 |
| Q4422738 | P31 | Q5 |
| Q4661693 | P123 | Q671855 |
| Q3039230 | P31 | Q783794 |
| Q6646324 | P155 | Q5007829 |
| Q7052677 | P106 | Q6625963 |
| Q1449121 | P21 | Q6581097 |
| Q1609104 | P166 | Q17412908 |
| Q1305581 | P106 | Q2306091 |
| Q124102 | P19 | Q78 |
| Q16983652 | P21 | Q6581097 |
| Q1079353 | P19 | Q240071 |
| Q15071428 | P136 | Q1344 |
| Q17594823 | P131 | Q1101 |
| Q9353218 | P31 | Q5 |
| Q16552876 | P31 | Q15416 |
| Q4739661 | P21 | Q6581072 |
| Q6488439 | P161 | Q4901125 |
| Q1199120 | P150 | Q10864925 |
| Q1582596 | P19 | Q554051 |
| Q15993231 | P106 | Q40348 |
| Q11622672 | P31 | Q5 |
| Q3526704 | P106 | Q82955 |
| Q163898 | P58 | Q1356749 |
| Q3275776 | P136 | Q179700 |
| Q525301 | P27 | Q36 |
| Q216750 | P20 | Q100 |
| Q16988469 | P106 | Q330679 |
| Q15431276 | P166 | Q10905276 |
| Q7457298 | P344 | Q3057187 |
| Q76125 | P19 | Q1794 |
| Q6648 | P31 | Q577 |
| Q1438771 | P19 | Q268219 |
| Q17397396 | P669 | Q18956977 |
| Q944922 | P21 | Q6581097 |
| Q78475 | P20 | Q1741 |
| Q8263992 | P106 | Q43845 |
| Q231904 | P31 | Q484170 |
| Q1797274 | P31 | Q1149652 |
| Q16221351 | P21 | Q6581097 |
| Q648663 | P196 | Q2179 |
| Q4919349 | P175 | Q469901 |
| Q7041772 | P57 | Q2582445 |
| Q731938 | P31 | Q783794 |
| Q741624 | P31 | Q5 |
| Q91413 | P47 | Q82704 |
| Q2965386 | P106 | Q13415036 |
| Q1200098 | P39 | Q29182 |
| Q19255461 | P131 | Q1025079 |
| Q5407804 | P735 | Q545971 |
| Q1599704 | P21 | Q6581097 |
| Q9221193 | P31 | Q4167836 |
| Q733722 | P162 | Q925604 |
| Q7620527 | P108 | Q174710 |
| Q6459314 | P137 | Q1092839 |
| Q17446036 | P669 | Q18951829 |
| Q827351 | P106 | Q82955 |
| Q1603600 | P27 | Q40 |
| Q19358358 | P607 | Q362 |
| Q1630416 | P156 | Q3468482 |
| Q2645470 | P735 | Q3480335 |
| Q14519610 | P20 | Q84125 |
| Q4513020 | P21 | Q6581097 |
| Q12165429 | P735 | Q830350 |
| Q686132 | P197 | Q657879 |
| Q3852094 | P106 | Q2004963 |
| Q2888251 | P195 | Q3329787 |
| Q4017905 | P27 | Q38 |
| Q363400 | P106 | Q28389 |
| Q5672439 | P27 | Q145 |
| Q6221909 | P108 | Q210175 |
| Q1530053 | P161 | Q1243049 |
| Q969302 | P735 | Q16799105 |
| Q17811548 | P735 | Q2260734 |
| Q438272 | P161 | Q3315571 |
| Q17319464 | P669 | Q19293809 |
| Q1904546 | P106 | Q11774891 |
| Q1361315 | P106 | Q937857 |
| Q69060 | P31 | Q748149 |
| Q11461955 | P102 | Q232595 |
| Q17373950 | P1435 | Q916333 |
| Q128434 | P735 | Q18534268 |
| Q1357974 | P735 | Q18028491 |
| Q99270 | P102 | Q7320 |
| Q3099087 | P20 | Q90 |
| Q5078636 | P106 | Q193391 |
| Q1066528 | P1412 | Q150 |
| Q1683377 | P106 | Q2306091 |
| Q265641 | P21 | Q6581072 |
| Q3142763 | P31 | Q5 |
| Q17120059 | P31 | Q5 |
| Q2504707 | P735 | Q18515951 |
| Q15193 | P166 | Q15831432 |
| Q682102 | P65 | Q191684 |
| Q291906 | P735 | Q391321 |
| Q3523004 | P161 | Q3122043 |
| Q7575854 | P161 | Q7920794 |
| Q742370 | P20 | Q259497 |
| Q2326570 | P54 | Q1974241 |
| Q15427472 | P161 | Q1521718 |
| Q16106519 | P21 | Q6581097 |
| Q566094 | P1412 | Q188 |
| Q16264118 | P19 | Q80011 |
| Q11728505 | P21 | Q6581097 |
| Q3544837 | P31 | Q5 |
| Q17464572 | P1435 | Q916333 |
| Q121203 | P17 | Q843 |
| Q2597650 | P86 | Q494596 |
| Q2384009 | P19 | Q146530 |
| Q3262309 | P27 | Q142 |
| Q4746747 | P106 | Q19595175 |
| Q478841 | P47 | Q477575 |
| Q3807782 | P735 | Q18609696 |
| Q7306161 | P155 | Q4597077 |
| Q788536 | P105 | Q35409 |
| Q1343410 | P150 | Q11060628 |
| Q1224020 | P106 | Q1234713 |
| Q7323977 | P21 | Q6581097 |
| Q18666175 | P360 | Q5 |
| Q11328839 | P264 | Q1062659 |
| Q150939 | P17 | Q20 |
| Q325621 | P57 | Q960868 |
| Q4023328 | P734 | Q17041572 |
| Q5233147 | P31 | Q5 |
| Q7759503 | P155 | Q7318993 |
| Q1758653 | P19 | Q2865 |
| Q701977 | P272 | Q122865 |
| Q95394 | P27 | Q183 |
| Q9648 | P85 | Q40807 |
| Q14537090 | P31 | Q13406463 |
| Q3814258 | P106 | Q674426 |
| Q15992967 | P735 | Q15921732 |
| Q2263892 | P61 | Q3849346 |
| Q17542119 | P27 | Q142 |
| Q16025645 | P39 | Q382617 |
| Q823819 | P21 | Q6581097 |
| Q7860058 | P106 | Q33999 |
| Q82644 | P47 | Q103244 |
| Q6170242 | P39 | Q18524027 |
| Q359568 | P463 | Q83172 |
| Q1262649 | P463 | Q320642 |
| Q5399381 | P735 | Q1474085 |
| Q14133684 | P17 | Q148 |
| Q6525755 | P27 | Q16 |
| Q1894107 | P27 | Q183 |
| Q1629775 | P27 | Q183 |
| Q1398238 | P735 | Q220546 |
| Q13426472 | P131 | Q13415446 |
| Q19247919 | P156 | Q19247920 |
| Q3903206 | P735 | Q3903143 |
| Q3588560 | P735 | Q13426635 |
| Q15446966 | P106 | Q36834 |
| Q6097775 | P106 | Q937857 |
| Q5254991 | P106 | Q33999 |
| Q818132 | P20 | Q139427 |
| Q1626597 | P287 | Q15112244 |
| Q2613276 | P155 | Q6420067 |
| Q2351637 | P19 | Q28441 |
| Q116480 | P31 | Q506240 |
| Q6027756 | P31 | Q783794 |
| Q11533372 | P734 | Q4342082 |
| Q499613 | P97 | Q521109 |
| Q1426 | P1344 | Q8558 |
| Q5730360 | P31 | Q5 |
| Q4800264 | P108 | Q129421 |
| Q249665 | P31 | Q484170 |
| Q1724331 | P150 | Q860644 |
| Q1197991 | P150 | Q14342442 |
| Q3766136 | P106 | Q42973 |
| Q9388019 | P166 | Q15715250 |
| Q1037672 | P166 | Q705153 |
| Q5563183 | P106 | Q82955 |
| Q1111239 | P21 | Q6581097 |
| Q616023 | P19 | Q1486 |
| Q17291276 | P17 | Q55 |
| Q7348773 | P106 | Q82955 |
| Q18882921 | P816 | Q18882866 |
| Q2572455 | P19 | Q2833 |
| Q3613084 | P155 | Q3824028 |
| Q149126 | P155 | Q6423276 |
| Q596209 | P27 | Q142 |
| Q279418 | P21 | Q6581072 |
| Q3709228 | P131 | Q494192 |
| Q4307556 | P19 | Q997029 |
| Q4656085 | P840 | Q84 |
| Q1584631 | P39 | Q17521638 |
| Q372013 | P21 | Q6581097 |
| Q889831 | P31 | Q5 |
| Q1745654 | P21 | Q6581097 |
| Q109783 | P106 | Q81096 |
| Q1605886 | P6 | Q14033950 |
| Q403878 | P735 | Q1173883 |
| Q5163513 | P735 | Q679755 |
| Q7340283 | P21 | Q6581097 |
| Q18177861 | P170 | Q148475 |
| Q1478712 | P31 | Q34442 |
| Q19404255 | P138 | Q131219 |
| Q375068 | P40 | Q1337618 |
| Q1181198 | P676 | Q1744 |
| Q1884989 | P106 | Q82955 |
| Q3081458 | P27 | Q142 |
| Q132901 | P61 | Q11821 |
| Q1391931 | P937 | Q2795 |
| Q4059527 | P31 | Q5 |
| Q1359405 | P19 | Q37320 |
| Q5370568 | P155 | Q7760164 |
| Q208359 | P21 | Q6581097 |
| Q19912648 | P31 | Q3305213 |
| Q906411 | P31 | Q123705 |
| Q13423282 | P31 | Q3464665 |
| Q3804432 | P106 | Q937857 |
| Q7237271 | P106 | Q2526255 |
| Q17604628 | P131 | Q9822 |
| Q4364374 | P272 | Q179200 |
| Q3960601 | P69 | Q168756 |
| Q819749 | P31 | Q5 |
| Q17319044 | P31 | Q5 |
| Q10930013 | P131 | Q1196382 |
| Q15989863 | P27 | Q664 |
| Q720009 | P27 | Q30 |
| Q3048716 | P166 | Q12201378 |
| Q10786394 | P105 | Q34740 |
| Q1527178 | P31 | Q5 |
| Q120041 | P27 | Q183 |
| Q14075875 | P31 | Q5 |
| Q2423299 | P106 | Q189290 |
| Q10320251 | P21 | Q6581072 |
| Q155079 | P31 | Q5 |
| Q1603068 | P364 | Q1860 |
| Q1731462 | P19 | Q671781 |
| Q2150454 | P400 | Q188642 |
| Q1351098 | P19 | Q53896 |
| Q3012302 | P641 | Q542 |
| Q664009 | P527 | Q12887 |
| Q3046920 | P908 | Q14915512 |
| Q15069394 | P31 | Q5 |
| Q17301153 | P17 | Q55 |
| Q13571305 | P1383 | Q2716879 |
| Q17499932 | P161 | Q1399112 |
| Q506414 | P20 | Q495 |
| Q99288 | P21 | Q6581097 |
| Q18769445 | P527 | Q17461811 |
| Q16232245 | P31 | Q5 |
| Q4668973 | P39 | Q18691526 |
| Q988388 | P31 | Q3957 |
| Q2276063 | P264 | Q1430474 |
| Q5661768 | P31 | Q5 |
| Q7472016 | P31 | Q3863 |
| Q1237655 | P39 | Q29182 |
| Q318017 | P9 | Q3850456 |
| Q1397830 | P150 | Q22210 |
| Q2647423 | P106 | Q1930187 |
| Q1462855 | P102 | Q455038 |
| Q2327467 | P19 | Q6596 |
| Q3090678 | P22 | Q1890878 |
| Q3133282 | P131 | Q494011 |
| Q2321113 | P197 | Q2059086 |
| Q5006455 | P69 | Q13371 |
| Q1110084 | P31 | Q226730 |
| Q6218956 | P106 | Q2066131 |
| Q1849651 | P138 | Q2786563 |
| Q443067 | P27 | Q16 |
| Q17173279 | P19 | Q3616 |
| Q7009812 | P155 | Q7534753 |
| Q946349 | P31 | Q5 |
| Q2373513 | P39 | Q19360355 |
| Q6132938 | P735 | Q677191 |
| Q543710 | P27 | Q183 |
| Q27504 | P31 | Q5 |
| Q3010810 | P276 | Q2796641 |
| Q467927 | P734 | Q15080511 |
| Q14655016 | P106 | Q33999 |
| Q4001087 | P272 | Q3614072 |
| Q782490 | P155 | Q637139 |
| Q15982647 | P463 | Q618537 |
| Q5497133 | P106 | Q1930187 |
| Q28740 | P509 | Q188874 |
| Q7755958 | P449 | Q216108 |
| Q1203831 | P161 | Q1364909 |
| Q4813173 | P31 | Q14762205 |
| Q3484349 | P21 | Q6581097 |
| Q3492843 | P286 | Q7299749 |
| Q2924930 | P54 | Q298267 |
| Q736695 | P161 | Q3380016 |
| Q891363 | P20 | Q255802 |
| Q952045 | P106 | Q1281618 |
| Q2562617 | P106 | Q211346 |
| Q18334968 | P735 | Q1985538 |
| Q3294004 | P106 | Q1930187 |
| Q1326980 | P19 | Q2807 |
| Q2293972 | P915 | Q65 |
| Q4514816 | P31 | Q5 |
| Q5944753 | P27 | Q29 |
| Q7638177 | P264 | Q917561 |
| Q670245 | P31 | Q484170 |
| Q108666 | P740 | Q84 |
| Q6787306 | P27 | Q258 |
| Q1675399 | P156 | Q1634438 |
| Q1863550 | P735 | Q1795088 |
| Q71790 | P31 | Q5 |
| Q7621505 | P175 | Q259429 |
| Q17306266 | P641 | Q5372 |
| Q6110588 | P735 | Q18028597 |
| Q456467 | P161 | Q470260 |
| Q2856948 | P495 | Q142 |
| Q106730 | P20 | Q496056 |
| Q260344 | P21 | Q6581072 |
| Q392696 | P162 | Q59259 |
| Q6172174 | P31 | Q5 |
| Q826906 | P27 | Q183 |
| Q3080822 | P106 | Q36180 |
| Q4329399 | P161 | Q106743 |
| Q2052882 | P734 | Q1688722 |
| Q1165070 | P31 | Q484170 |
| Q5267608 | P20 | Q40738 |
| Q364635 | P27 | Q183 |
| Q2562063 | P106 | Q3387717 |
| Q17597620 | P1435 | Q916333 |
| Q3588330 | P27 | Q142 |
| Q16210084 | P735 | Q4925477 |
| Q18844876 | P279 | Q725 |
| Q5298546 | P69 | Q1399299 |
| Q15710824 | P467 | Q2458227 |
| Q1200481 | P31 | Q1289426 |
| Q2982896 | P31 | Q483453 |
| Q980257 | P161 | Q719529 |
| Q98605 | P31 | Q5 |
| Q10436799 | P735 | Q18402099 |
| Q172381 | P735 | Q4927589 |
| Q1412278 | P84 | Q12174873 |
| Q12012136 | P735 | Q8079337 |
| Q1068361 | P31 | Q43183 |
| Q3099503 | P735 | Q19793321 |
| Q616668 | P131 | Q1725768 |
| Q12890820 | P31 | Q5 |
| Q2897825 | P31 | Q5 |
| Q1561551 | P19 | Q4120832 |
| Q7794480 | P31 | Q5 |
| Q15436770 | P27 | Q183 |
| Q16979881 | P27 | Q145 |
| Q251931 | P500 | Q42880 |
| Q2003245 | P47 | Q1001647 |
| Q5389676 | P21 | Q6581097 |
| Q17610179 | P156 | Q17610330 |
| Q167540 | P735 | Q14371254 |
| Q716134 | P31 | Q5 |
| Q92937 | P31 | Q5 |
| Q708788 | P27 | Q183 |
| Q1072227 | P21 | Q6581097 |
| Q1453347 | P31 | Q5 |
| Q363049 | P106 | Q2526255 |
| Q1788523 | P27 | Q183 |
| Q1097677 | P106 | Q2066131 |
| Q1618378 | P31 | Q5 |
| Q4772624 | P21 | Q6581097 |
| Q796898 | P279 | Q1022125 |
| Q7597634 | P735 | Q17862013 |
| Q13945591 | P31 | Q12808966 |
| Q17491796 | P31 | Q3305213 |
| Q6137778 | P735 | Q677191 |
| Q1365502 | P19 | Q3777 |
| Q14240356 | P27 | Q183 |
| Q2371597 | P131 | Q235297 |
| Q362790 | P21 | Q6581097 |
| Q1581704 | P734 | Q8157228 |
| Q3767701 | P21 | Q6581097 |
| Q1756968 | P131 | Q302778 |
| Q4643245 | P179 | Q2744 |
| Q1468795 | P31 | Q5 |
| Q372888 | P1066 | Q658109 |
| Q2159505 | P735 | Q13564349 |
| Q3142278 | P437 | Q633454 |
| Q2078606 | P54 | Q170703 |
| Q15456735 | P27 | Q183 |
| Q4773154 | P31 | Q5 |
| Q3610189 | P21 | Q6581097 |
| Q18619597 | P186 | Q4259259 |
| Q1432671 | P31 | Q11424 |
| Q6184062 | P119 | Q252312 |
| Q1637939 | P161 | Q230424 |
| Q15073944 | P971 | Q516405 |
| Q1901634 | P108 | Q154804 |
| Q3588525 | P119 | Q1092107 |
| Q365909 | P108 | Q186285 |
| Q1613120 | P108 | Q152171 |
| Q3809209 | P108 | Q49112 |
| Q7034144 | P31 | Q16521 |
| Q152480 | P27 | Q28 |
| Q11779798 | P27 | Q36 |
| Q1909669 | P102 | Q49768 |
| Q5553032 | P31 | Q5 |
| Q4143281 | P131 | Q7677 |
| Q3959252 | P31 | Q5 |
| Q5928071 | P31 | Q5 |
| Q10513187 | P21 | Q6581097 |
| Q16225870 | P735 | Q18245781 |
| Q18352608 | P108 | Q622664 |
| Q1315907 | P495 | Q30 |
| Q2993591 | P20 | Q157246 |
| Q10401493 | P31 | Q16521 |
| Q1612701 | P735 | Q1158570 |
| Q267106 | P1344 | Q8415 |
| Q5543762 | P69 | Q7055270 |
| Q2790698 | P31 | Q641226 |
| Q6251982 | P21 | Q6581097 |
| Q526300 | P54 | Q19593 |
| Q18402350 | P106 | Q486748 |
| Q5361279 | P27 | Q30 |
| Q1023602 | P17 | Q142 |
| Q12351891 | P1412 | Q143 |
| Q729416 | P31 | Q482994 |
| Q69792 | P19 | Q1726 |
| Q335059 | P937 | Q646980 |
| Q378893 | P27 | Q739 |
| Q10354246 | P31 | Q5 |
| Q2130002 | P47 | Q1300392 |
| Q3026655 | P106 | Q14089670 |
| Q19359691 | P361 | Q19220658 |
| Q1650174 | P27 | Q30 |
| Q321122 | P69 | Q258464 |
| Q6519747 | P69 | Q219563 |
| Q6790850 | P31 | Q5 |
| Q191156 | P105 | Q334460 |
| Q3243531 | P21 | Q6581072 |
| Q19655965 | P138 | Q150747 |
| Q1554558 | P27 | Q145 |
| Q586589 | P162 | Q6758665 |
| Q879932 | P735 | Q364753 |
| Q1512168 | P161 | Q234581 |
| Q2285321 | P21 | Q6581072 |
| Q1717030 | P39 | Q17344251 |
| Q433728 | P69 | Q258464 |
| Q2355147 | P31 | Q34442 |
| Q2875177 | P9 | Q13427665 |
| Q14129509 | P27 | Q29 |
| Q1126256 | P150 | Q2113624 |
| Q361550 | P27 | Q183 |
| Q3633077 | P161 | Q6807665 |
| Q14077770 | P364 | Q1860 |
| Q17099809 | P21 | Q6581097 |
| Q2813614 | P166 | Q2547676 |
| Q18758936 | P360 | Q5 |
| Q19242427 | P155 | Q19242425 |
| Q879921 | P27 | Q30 |
| Q45250 | P166 | Q16336085 |
| Q556844 | P21 | Q6581072 |
| Q11445861 | P39 | Q17506823 |
| Q19468639 | P17 | Q55 |
| Q1724511 | P150 | Q200041 |
| Q1318697 | P105 | Q34740 |
| Q4864032 | P54 | Q849315 |
| Q16266468 | P155 | Q16746473 |
| Q3278502 | P17 | Q142 |
| Q333808 | P20 | Q320378 |
| Q745505 | P19 | Q326879 |
| Q10857434 | P39 | Q19803234 |
| Q93023 | P106 | Q36180 |
| Q1176842 | P178 | Q1889419 |
| Q2945030 | P123 | Q1371744 |
| Q254243 | P31 | Q484170 |
| Q3260689 | P166 | Q11593374 |
| Q5986988 | P54 | Q1128631 |
| Q18774690 | P17 | Q55 |
| Q787672 | P106 | Q639669 |
| Q156300 | P509 | Q12152 |
| Q1746124 | P735 | Q4927128 |
| Q1536603 | P361 | Q2239885 |
| Q4123384 | P27 | Q159 |
| Q14945515 | P31 | Q5 |
| Q4919827 | P69 | Q486156 |
| Q2900832 | P641 | Q542 |
| Q123846 | P27 | Q183 |
| Q5082576 | P69 | Q1149089 |
| Q2652245 | P21 | Q6581097 |
| Q265050 | P17 | Q17 |
| Q3092343 | P19 | Q90 |
| Q6534502 | P682 | Q14878786 |
| Q1620131 | P19 | Q497200 |
| Q2154721 | P31 | Q5 |
| Q8057631 | P361 | Q1658029 |
| Q18147747 | P527 | Q325648 |
| Q6883161 | P159 | Q35765 |
| Q599510 | P279 | Q862597 |
| Q3247743 | P161 | Q2929411 |
| Q3361 | P150 | Q836607 |
| Q4766504 | P735 | Q558067 |
| Q2915712 | P31 | Q188509 |
| Q3791066 | P161 | Q1179412 |
| Q5820724 | P21 | Q6581097 |
| Q11379 | P279 | Q35120 |
| Q764578 | P22 | Q573424 |
| Q1587049 | P108 | Q32120 |
| Q1432423 | P27 | Q183 |
| Q1544298 | P108 | Q122453 |
| Q3387066 | P21 | Q6581097 |
| Q12771789 | P21 | Q6581097 |
| Q110146 | P1344 | Q8558 |
| Q12176843 | P735 | Q617272 |
| Q7441460 | P27 | Q30 |
| Q13815768 | P106 | Q855091 |
| Q7287753 | P106 | Q16145150 |
| Q3545150 | P19 | Q11434728 |
| Q1298397 | P527 | Q19273492 |
| Q114172 | P106 | Q36180 |
| Q7980788 | P175 | Q7729259 |
| Q3767590 | P106 | Q201788 |
| Q3349229 | P21 | Q6581072 |
| Q530033 | P106 | Q937857 |
| Q17334994 | P1204 | Q1693 |
| Q49351 | P119 | Q1574424 |
| Q3204923 | P161 | Q3085009 |
| Q11512722 | P156 | Q11449940 |
| Q584535 | P54 | Q912247 |
| Q395755 | P19 | Q2044 |
| Q275960 | P86 | Q2121109 |
| Q7350769 | P21 | Q6581097 |
| Q1222145 | P166 | Q10905334 |
| Q3090265 | P1343 | Q17166797 |
| Q7612227 | P19 | Q128114 |
| Q3521378 | P735 | Q19803513 |
| Q309459 | P31 | Q11424 |
| Q2486115 | P17 | Q30 |
| Q1356727 | P19 | Q1489 |
| Q6186526 | P735 | Q2227398 |
| Q3002796 | P31 | Q5 |
| Q3823896 | P58 | Q1343394 |
| Q4792831 | P27 | Q16 |
| Q4306855 | P19 | Q898 |
| Q418099 | P735 | Q4700926 |
| Q5088940 | P264 | Q183387 |
| Q109290 | P108 | Q681250 |
| Q3452529 | P17 | Q142 |
| Q1989101 | P69 | Q215539 |
| Q1227732 | P31 | Q5 |
| Q4483063 | P735 | Q15731576 |
| Q5483119 | P1344 | Q8544 |
| Q1516684 | P31 | Q3918 |
| Q1852242 | P21 | Q6581097 |
| Q138591 | P21 | Q6581097 |
| Q4087615 | P27 | Q30 |
| Q6709245 | P27 | Q30 |
| Q14910640 | P31 | Q16521 |
| Q325973 | P20 | Q220 |
| Q4275847 | P21 | Q6581097 |
| Q21376 | P131 | Q2150573 |
| Q723491 | P175 | Q286596 |
| Q799817 | P39 | Q29182 |
| Q5081265 | P21 | Q6581097 |
| Q3309191 | P27 | Q142 |
| Q1961283 | P27 | Q142 |
| Q199644 | P21 | Q6581097 |
| Q763242 | P19 | Q625091 |
| Q4666319 | P21 | Q6581097 |
| Q232789 | P54 | Q2739 |
| Q5112591 | P31 | Q659103 |
| Q660030 | P17 | Q142 |
| Q6461262 | P196 | Q2179 |
| Q591809 | P39 | Q29182 |
| Q5776365 | P156 | Q5776449 |
| Q3169038 | P171 | Q140435 |
| Q4138699 | P27 | Q41 |
| Q1520732 | P161 | Q93957 |
| Q3083977 | P1412 | Q397 |
| Q2040986 | P105 | Q7432 |
| Q18710362 | P171 | Q6544822 |
| Q3127840 | P735 | Q668885 |
| Q6242151 | P21 | Q6581097 |
| Q12022481 | P19 | Q270704 |
| Q41079 | P150 | Q571219 |
| Q1578559 | P21 | Q6581097 |
| Q6124922 | P27 | Q30 |
| Q11884465 | P21 | Q6581097 |
| Q17495989 | P186 | Q4259259 |
| Q1505686 | P102 | Q49763 |
| Q123057 | P735 | Q19264720 |
| Q3236684 | P69 | Q3047595 |
| Q5518466 | P31 | Q482994 |
| Q111258 | P21 | Q6581097 |
| Q7340315 | P21 | Q6581097 |
| Q14634026 | P162 | Q1522276 |
| Q3158480 | P27 | Q142 |
| Q1578490 | P106 | Q635734 |
| Q3384862 | P937 | Q90 |
| Q1212630 | P161 | Q2004024 |
| Q15040646 | P176 | Q463261 |
| Q16097054 | P21 | Q6581097 |
| Q5448544 | P54 | Q371136 |
| Q7082998 | P106 | Q82955 |
| Q1862750 | P735 | Q2658970 |
| Q5708659 | P264 | Q557632 |
| Q1912743 | P21 | Q6581097 |
| Q76624 | P21 | Q6581097 |
| Q12286383 | P161 | Q12279836 |
| Q3839964 | P27 | Q38 |
| Q14512358 | P31 | Q16521 |
| Q807487 | P106 | Q13365117 |
| Q2865080 | P31 | Q5 |
| Q627861 | P156 | Q712744 |
| Q3105726 | P735 | Q1675463 |
| Q1440286 | P31 | Q5 |
| Q2410737 | P197 | Q2229953 |
| Q17490971 | P186 | Q296955 |
| Q392783 | P161 | Q235278 |
| Q904686 | P21 | Q6581072 |
| Q1579823 | P31 | Q5 |
| Q351426 | P31 | Q5 |
| Q1727278 | P131 | Q701072 |
| Q641445 | P17 | Q35 |
| Q517824 | P21 | Q6581097 |
| Q2061133 | P106 | Q42603 |
| Q3068 | P150 | Q653380 |
| Q7688610 | P400 | Q23882 |
| Q4864522 | P54 | Q205033 |
| Q9001319 | P509 | Q29496 |
| Q5152881 | P155 | Q7755601 |
| Q1668661 | P31 | Q5 |
| Q2436828 | P17 | Q30 |
| Q3776054 | P161 | Q289020 |
| Q1705061 | P21 | Q6581097 |
| Q5726326 | P19 | Q861627 |
| Q7135261 | P27 | Q668 |
| Q18211541 | P735 | Q2102316 |
| Q19243435 | P31 | Q21199 |
| Q232323 | P53 | Q852111 |
| Q3059538 | P106 | Q42973 |
| Q4164772 | P166 | Q403569 |
| Q5806128 | P106 | Q483501 |
| Q148356 | P65 | Q191684 |
| Q2425611 | P735 | Q18115390 |
| Q4811406 | P27 | Q20 |
| Q5873935 | P31 | Q16521 |
| Q3833180 | P54 | Q1538348 |
| Q718029 | P27 | Q55 |
| Q12353687 | P735 | Q4925623 |
| Q17439083 | P17 | Q55 |
| Q1045289 | P27 | Q38 |
| Q4728548 | P175 | Q254748 |
| Q3726042 | P735 | Q16908530 |
| Q586650 | P106 | Q482980 |
| Q6223036 | P27 | Q408 |
| Q1054560 | P21 | Q6581097 |
| Q1634253 | P735 | Q839387 |
| Q7793136 | P735 | Q18002322 |
| Q5550671 | P607 | Q362 |
| Q1704544 | P20 | Q1715 |
| Q3629978 | P19 | Q174234 |
| Q439776 | P27 | Q884 |
| Q5240782 | P106 | Q12299841 |
| Q1780852 | P19 | Q485253 |
| Q2639899 | P944 | Q13011 |
| Q3706766 | P27 | Q38 |
| Q978042 | P21 | Q6581097 |
| Q55007 | P131 | Q16120 |
| Q495287 | P54 | Q2565016 |
| Q1716692 | P21 | Q6581097 |
| Q3166201 | P21 | Q6581097 |
| Q10856433 | P21 | Q6581097 |
| Q722653 | P54 | Q194116 |
| Q1387025 | P27 | Q30 |
| Q561504 | P106 | Q81096 |
| Q950911 | P106 | Q170790 |
| Q164527 | P17 | Q213 |
| Q143644 | P21 | Q6581097 |
| Q1438437 | P479 | Q178805 |
| Q1022 | P150 | Q727750 |
| Q389355 | P421 | Q6655 |
| Q2093520 | P509 | Q8454 |
| Q5045254 | P131 | Q694 |
| Q3100008 | P413 | Q2270380 |
| Q6272552 | P21 | Q6581097 |
| Q597975 | P27 | Q30 |
| Q2062480 | P21 | Q6581072 |
| Q6943701 | P161 | Q705477 |
| Q1601980 | P21 | Q6581072 |
| Q2097256 | P106 | Q13382576 |
| Q5504856 | P19 | Q124539 |
| Q1085538 | P196 | Q2179 |
| Q1101218 | P106 | Q1028181 |
| Q6846492 | P735 | Q361309 |
| Q696695 | P735 | Q750186 |
| Q2039114 | P20 | Q1741 |
| Q3185034 | P106 | Q783906 |
| Q6239051 | P166 | Q2427600 |
| Q728989 | P178 | Q739711 |
| Q5386205 | P54 | Q1148233 |
| Q24276 | P131 | Q228 |
| Q3441558 | P21 | Q6581097 |
| Q3390565 | P47 | Q3450641 |
| Q4175945 | P156 | Q4175753 |
| Q15427472 | P161 | Q90760 |
| Q7697274 | P31 | Q16521 |
| Q5314616 | P241 | Q1752901 |
| Q11630108 | P175 | Q266852 |
| Q2060744 | P735 | Q2117521 |
| Q19249212 | P155 | Q19249211 |
| Q631546 | P190 | Q566156 |
| Q581128 | P21 | Q6581097 |
| Q573817 | P166 | Q315026 |
| Q6286364 | P735 | Q471788 |
| Q1900652 | P31 | Q5 |
| Q259961 | P27 | Q145 |
| Q1352925 | P21 | Q6581097 |
| Q2481005 | P31 | Q5 |
| Q11550152 | P31 | Q5 |
| Q2307428 | P136 | Q860626 |
| Q1215771 | P106 | Q15059856 |
| Q28003 | P138 | Q981207 |
| Q531718 | P25 | Q269815 |
| Q15432782 | P31 | Q5 |
| Q4009559 | P180 | Q35500 |
| Q11338028 | P264 | Q8194234 |
| Q12795307 | P31 | Q5 |
| Q489111 | P19 | Q2807 |
| Q956947 | P641 | Q328716 |
| Q3340483 | P735 | Q7029481 |
| Q8364922 | P17 | Q20 |
| Q3177608 | P21 | Q6581097 |
| Q13635614 | P19 | Q7880 |
| Q1638132 | P21 | Q6581097 |
| Q8017253 | P21 | Q6581097 |
| Q1147949 | P136 | Q1057172 |
| Q1442905 | P31 | Q5 |
| Q4441393 | P27 | Q212 |
| Q3167699 | P27 | Q142 |
| Q88914 | P20 | Q1741 |
| Q1825280 | P106 | Q1028181 |
| Q3267066 | P106 | Q11774891 |
| Q4953897 | P21 | Q6581097 |
| Q1448741 | P31 | Q5 |
| Q3377600 | P19 | Q3549 |
| Q361297 | P106 | Q2462658 |
| Q1019463 | P166 | Q2727598 |
| Q2486041 | P16 | Q1852230 |
| Q7078743 | P31 | Q134556 |
| Q3838578 | P27 | Q38 |
| Q6907590 | P57 | Q311219 |
| Q7569036 | P364 | Q1860 |
| Q3084582 | P106 | Q250867 |
| Q336912 | P106 | Q42603 |
| Q1174833 | P106 | Q16267607 |
| Q77452 | P131 | Q1165 |
| Q4054640 | P20 | Q9248 |
| Q4061138 | P19 | Q2801 |
| Q5537133 | P31 | Q5 |
| Q5978761 | P175 | Q2248393 |
| Q1726422 | P27 | Q39 |
| Q16217373 | P27 | Q30 |
| Q212642 | P17 | Q142 |
| Q918881 | P17 | Q16 |
| Q17595057 | P31 | Q18762207 |
| Q5372051 | P735 | Q18121477 |
| Q7148414 | P31 | Q571 |
| Q5575620 | P735 | Q18404297 |
| Q5233173 | P106 | Q40348 |
| Q44902 | P27 | Q142 |
| Q6638165 | P156 | Q6641129 |
| Q756861 | P21 | Q6581072 |
| Q5795789 | P106 | Q82955 |
| Q17616366 | P31 | Q41176 |
| Q2285273 | P735 | Q634916 |
| Q2374013 | P31 | Q16970 |
| Q14598336 | P166 | Q17231624 |
| Q3557606 | P40 | Q561201 |
| Q15971609 | P36 | Q19566 |
| Q1458664 | P735 | Q14038597 |
| Q66378 | P17 | Q39 |
| Q5666275 | P31 | Q5 |
| Q5944327 | P21 | Q6581097 |
| Q7562956 | P735 | Q18201529 |
| Q1399879 | P106 | Q11774891 |
| Q2505482 | P127 | Q568743 |
| Q15434159 | P102 | Q49763 |
| Q62125 | P17 | Q183 |
| Q14086244 | P106 | Q82955 |
| Q6792807 | P21 | Q6581097 |
| Q3430946 | P106 | Q17486376 |
| Q1595237 | P31 | Q5 |
| Q362146 | P735 | Q1795260 |
| Q731499 | P741 | Q3039938 |
| Q5362464 | P39 | Q18654736 |
| Q11153932 | P27 | Q399 |
| Q1601853 | P108 | Q1051840 |
| Q1527054 | P735 | Q2190619 |
| Q16018600 | P607 | Q362 |
| Q590842 | P735 | Q1343668 |
| Q1659531 | P161 | Q445044 |
| Q2927024 | P31 | Q5 |
| Q1560877 | P131 | Q597 |
| Q2930282 | P161 | Q2389393 |
| Q5149087 | P156 | Q6875448 |
| Q15065337 | P27 | Q34266 |
| Q7044683 | P264 | Q2338889 |
| Q1063846 | P31 | Q5 |
| Q5909022 | P106 | Q36834 |
| Q371748 | P131 | Q53711 |
| Q4863959 | P31 | Q5 |
| Q845844 | P190 | Q67249 |
| Q11896452 | P735 | Q3817554 |
| Q936816 | P553 | Q918 |
| Q4944147 | P175 | Q2557820 |
| Q6512983 | P54 | Q48925 |
| Q1697265 | P27 | Q183 |
| Q170978 | P31 | Q8148 |
| Q6557797 | P102 | Q216082 |
| Q1753406 | P175 | Q2808 |
| Q3613930 | P106 | Q611644 |
| Q6762241 | P735 | Q18760860 |
| Q1219363 | P161 | Q358990 |
| Q2910096 | P27 | Q801 |
| Q15352 | P150 | Q15937 |
| Q4832644 | P1344 | Q8567 |
| Q4965165 | P106 | Q3282637 |
| Q7448399 | P21 | Q6581097 |
| Q2015910 | P31 | Q1201493 |
| Q5284862 | P21 | Q6581097 |
| Q5377936 | P123 | Q2744153 |
| Q5076344 | P21 | Q6581097 |
| Q908693 | P69 | Q49112 |
| Q5230765 | P27 | Q408 |
| Q3172593 | P106 | Q14972848 |
| Q2304393 | P136 | Q1443316 |
| Q6833784 | P21 | Q6581097 |
| Q3741059 | P21 | Q6581097 |
| Q19912132 | P195 | Q160236 |
| Q10719196 | P138 | Q1355965 |
| Q15635799 | P186 | Q40089 |
| Q1727931 | P21 | Q6581097 |
| Q2083880 | P136 | Q270948 |
| Q6312220 | P166 | Q12201526 |
| Q718825 | P54 | Q796179 |
| Q1687119 | P31 | Q5 |
| Q4758280 | P69 | Q499510 |
| Q1152657 | P162 | Q259593 |
| Q1170303 | P27 | Q30 |
| Q1468017 | P27 | Q183 |
| Q15447020 | P20 | Q1709 |
| Q2337200 | P27 | Q183 |
| Q350799 | P27 | Q25 |
| Q868577 | P21 | Q6581072 |
| Q3360690 | P31 | Q11424 |
| Q3903015 | P106 | Q82955 |
| Q7917352 | P102 | Q2399535 |
| Q5087189 | P106 | Q488205 |
| Q1005361 | P495 | Q17 |
| Q5106495 | P21 | Q6581097 |
| Q1460419 | P27 | Q36 |
| Q7828851 | P479 | Q273140 |
| Q13452 | P6 | Q14076448 |
| Q3807354 | P106 | Q937857 |
| Q3450538 | P47 | Q3451069 |
| Q7472058 | P61 | Q735603 |
| Q15069001 | P166 | Q185493 |
| Q5509758 | P175 | Q546573 |
display(graph.inDegrees)
| id | inDegree |
|---|---|
| Q298267 | 333.0 |
| Q4925477 | 26147.0 |
| Q200041 | 2.0 |
| Q4927128 | 1916.0 |
| Q42603 | 22874.0 |
| Q484111 | 7.0 |
| Q17463666 | 1.0 |
| Q337758 | 1144.0 |
| Q4597662 | 2.0 |
| Q81860 | 4.0 |
| Q18002623 | 2627.0 |
| Q1157091 | 7.0 |
| Q733786 | 591.0 |
| Q202444 | 2914.0 |
| Q542520 | 6.0 |
| Q164160 | 29.0 |
| Q265058 | 1082.0 |
| Q727919 | 729.0 |
| Q14373094 | 4740.0 |
| Q461595 | 224.0 |
| Q15921764 | 2215.0 |
| Q18180842 | 394.0 |
| Q6010 | 77.0 |
| Q11108046 | 1.0 |
| Q16272 | 70.0 |
| Q82117 | 135.0 |
| Q214127 | 1520.0 |
| Q325130 | 50.0 |
| Q863865 | 18.0 |
| Q17592486 | 1821.0 |
| Q193409 | 48.0 |
| Q983109 | 6.0 |
| Q664609 | 108.0 |
| Q9623 | 440.0 |
| Q7725634 | 612.0 |
| Q6723 | 1938.0 |
| Q18326120 | 4.0 |
| Q8760 | 334.0 |
| Q1051417 | 16.0 |
| Q459030 | 32.0 |
| Q220584 | 71.0 |
| Q18615381 | 2.0 |
| Q2393877 | 3.0 |
| Q68442 | 8.0 |
| Q86984 | 6.0 |
| Q19058075 | 3.0 |
| Q92518 | 17.0 |
| Q22160 | 25.0 |
| Q47887 | 424.0 |
| Q443387 | 22.0 |
| Q2640827 | 309.0 |
| Q11993721 | 21.0 |
| Q15966841 | 3.0 |
| Q309 | 191.0 |
| Q14920387 | 348.0 |
| Q644085 | 28.0 |
| Q893308 | 23.0 |
| Q18711738 | 949.0 |
| Q9135 | 110.0 |
| Q234068 | 24.0 |
| Q681374 | 15.0 |
| Q536795 | 268.0 |
| Q1058664 | 20.0 |
| Q11658317 | 37.0 |
| Q1822658 | 223.0 |
| Q1787576 | 7.0 |
| Q12879643 | 3.0 |
| Q6665838 | 35.0 |
| Q2508289 | 8.0 |
| Q94505 | 15.0 |
| Q2345491 | 5.0 |
| Q261506 | 14.0 |
| Q6730 | 352.0 |
| Q170292 | 101.0 |
| Q18327552 | 71.0 |
| Q2081742 | 1.0 |
| Q18915128 | 265.0 |
| Q767925 | 3.0 |
| Q225212 | 10.0 |
| Q875889 | 95.0 |
| Q2835202 | 45.0 |
| Q72717 | 32.0 |
| Q6869568 | 16.0 |
| Q785697 | 46.0 |
| Q3752713 | 2.0 |
| Q2611488 | 2.0 |
| Q468756 | 145.0 |
| Q5790766 | 2.0 |
| Q2941960 | 40.0 |
| Q318348 | 247.0 |
| Q46890 | 5.0 |
| Q170507 | 79.0 |
| Q1637706 | 170.0 |
| Q19688717 | 61.0 |
| Q578221 | 11.0 |
| Q1173082 | 2.0 |
| Q14283996 | 1.0 |
| Q19257892 | 1.0 |
| Q514571 | 23.0 |
| Q11057182 | 1.0 |
| Q2793480 | 3.0 |
| Q36633 | 266.0 |
| Q18577439 | 61.0 |
| Q12981877 | 38.0 |
| Q1025678 | 23.0 |
| Q146456 | 51.0 |
| Q1320080 | 2.0 |
| Q5132316 | 60.0 |
| Q19830690 | 92.0 |
| Q5946 | 21.0 |
| Q457860 | 37.0 |
| Q9876 | 197.0 |
| Q790573 | 26.0 |
| Q2394333 | 13.0 |
| Q12026516 | 2.0 |
| Q4802759 | 2.0 |
| Q1080571 | 2.0 |
| Q1150813 | 4.0 |
| Q2102107 | 1.0 |
| Q1982491 | 104.0 |
| Q6686585 | 2.0 |
| Q483347 | 28.0 |
| Q12607934 | 2.0 |
| Q4083870 | 19.0 |
| Q2308650 | 15.0 |
| Q1356554 | 22.0 |
| Q6774064 | 1.0 |
| Q19263103 | 2.0 |
| Q2024529 | 25.0 |
| Q1283517 | 11.0 |
| Q15477 | 57.0 |
| Q2973157 | 1.0 |
| Q4681121 | 70.0 |
| Q783526 | 6.0 |
| Q18773292 | 13.0 |
| Q18180939 | 82.0 |
| Q209059 | 5.0 |
| Q53900 | 12.0 |
| Q234356 | 3.0 |
| Q3885548 | 25.0 |
| Q26600 | 273.0 |
| Q831235 | 4.0 |
| Q1664782 | 224.0 |
| Q1016159 | 31.0 |
| Q1808932 | 6.0 |
| Q669399 | 1.0 |
| Q3598650 | 2.0 |
| Q16280852 | 178.0 |
| Q15430602 | 6.0 |
| Q1044025 | 47.0 |
| Q832833 | 15.0 |
| Q472316 | 209.0 |
| Q1724026 | 2.0 |
| Q1346056 | 69.0 |
| Q2829058 | 96.0 |
| Q19521 | 237.0 |
| Q17318005 | 231.0 |
| Q3533779 | 4.0 |
| Q43247 | 51.0 |
| Q856083 | 19.0 |
| Q3069332 | 52.0 |
| Q2768413 | 6.0 |
| Q5471960 | 3.0 |
| Q16263 | 1.0 |
| Q17763 | 13.0 |
| Q837171 | 35.0 |
| Q26316 | 33.0 |
| Q2898153 | 4.0 |
| Q26050 | 13.0 |
| Q642433 | 92.0 |
| Q219647 | 2.0 |
| Q8226055 | 2.0 |
| Q354477 | 10.0 |
| Q1003336 | 4.0 |
| Q702096 | 1.0 |
| Q46130 | 329.0 |
| Q17595129 | 1.0 |
| Q9221240 | 1.0 |
| Q4513695 | 1.0 |
| Q1222 | 98.0 |
| Q14864655 | 11.0 |
| Q207501 | 111.0 |
| Q2184650 | 2.0 |
| Q6968341 | 125.0 |
| Q18109999 | 25.0 |
| Q2509482 | 22.0 |
| Q14290394 | 1.0 |
| Q1508941 | 60.0 |
| Q1651613 | 105.0 |
| Q2107890 | 2.0 |
| Q346540 | 108.0 |
| Q16163 | 125.0 |
| Q14820581 | 15.0 |
| Q11194421 | 2.0 |
| Q3018751 | 13.0 |
| Q10870299 | 1.0 |
| Q3889 | 126.0 |
| Q6669082 | 9.0 |
| Q650015 | 16.0 |
| Q3831543 | 64.0 |
| Q1070034 | 14.0 |
| Q708506 | 13.0 |
| Q18572794 | 13.0 |
| Q1106905 | 2.0 |
| Q546273 | 6.0 |
| Q309994 | 13.0 |
| Q171277 | 2.0 |
| Q402664 | 1.0 |
| Q5802535 | 1.0 |
| Q967999 | 63.0 |
| Q5323512 | 63.0 |
| Q155658 | 2.0 |
| Q6464788 | 2.0 |
| Q16670466 | 12.0 |
| Q7332914 | 2.0 |
| Q8064768 | 6.0 |
| Q3341114 | 1.0 |
| Q1725320 | 10.0 |
| Q54108 | 59.0 |
| Q6377001 | 173.0 |
| Q3190628 | 2.0 |
| Q318985 | 1.0 |
| Q1517021 | 51.0 |
| Q4195420 | 1.0 |
| Q535762 | 7.0 |
| Q2003540 | 193.0 |
| Q548982 | 12.0 |
| Q123567 | 7.0 |
| Q15771 | 12.0 |
| Q607016 | 6.0 |
| Q314640 | 14.0 |
| Q671287 | 2.0 |
| Q181227 | 6.0 |
| Q203232 | 211.0 |
| Q2043225 | 5.0 |
| Q1865 | 136.0 |
| Q145794 | 332.0 |
| Q16636472 | 1.0 |
| Q352246 | 4.0 |
| Q209790 | 27.0 |
| Q3917342 | 6.0 |
| Q461278 | 12.0 |
| Q61480 | 35.0 |
| Q237072 | 77.0 |
| Q18519993 | 1.0 |
| Q11315798 | 2.0 |
| Q2278569 | 19.0 |
| Q1138078 | 14.0 |
| Q3664754 | 12.0 |
| Q192958 | 6.0 |
| Q644249 | 4.0 |
| Q1424839 | 3.0 |
| Q6418706 | 15.0 |
| Q6714072 | 2.0 |
| Q6728299 | 2.0 |
| Q2745944 | 10.0 |
| Q727726 | 26.0 |
| Q17309954 | 76.0 |
| Q10869004 | 1.0 |
| Q18329641 | 1.0 |
| Q43742 | 23.0 |
| Q7650827 | 4.0 |
| Q1668024 | 28.0 |
| Q19688837 | 81.0 |
| Q7794524 | 1.0 |
| Q447910 | 71.0 |
| Q2863447 | 11.0 |
| Q147081 | 6.0 |
| Q14565638 | 8.0 |
| Q190994 | 49.0 |
| Q3098071 | 17.0 |
| Q1159185 | 4.0 |
| Q2927023 | 13.0 |
| Q54726 | 8.0 |
| Q14913515 | 1.0 |
| Q3299581 | 8.0 |
| Q6461236 | 2.0 |
| Q532545 | 1.0 |
| Q537869 | 73.0 |
| Q12727910 | 11.0 |
| Q18925300 | 11.0 |
| Q1503841 | 1.0 |
| Q5958102 | 1.0 |
| Q369217 | 10.0 |
| Q3740805 | 42.0 |
| Q477819 | 2.0 |
| Q751424 | 47.0 |
| Q7991 | 11.0 |
| Q7708727 | 2.0 |
| Q5136499 | 3.0 |
| Q215829 | 51.0 |
| Q51272 | 1.0 |
| Q1726631 | 6.0 |
| Q7711202 | 2.0 |
| Q614294 | 5.0 |
| Q13994616 | 1.0 |
| Q1429 | 3.0 |
| Q958785 | 14.0 |
| Q7027243 | 1.0 |
| Q5918105 | 2.0 |
| Q19776533 | 3.0 |
| Q9449556 | 2.0 |
| Q319283 | 31.0 |
| Q572754 | 6.0 |
| Q1845552 | 2.0 |
| Q3020443 | 2.0 |
| Q114975 | 10.0 |
| Q2868110 | 6.0 |
| Q253212 | 13.0 |
| Q11260078 | 2.0 |
| Q1483138 | 16.0 |
| Q459794 | 108.0 |
| Q10877222 | 17.0 |
| Q1699424 | 2.0 |
| Q220595 | 3.0 |
| Q5108046 | 14.0 |
| Q3384142 | 1.0 |
| Q273210 | 13.0 |
| Q4482828 | 13.0 |
| Q845662 | 9.0 |
| Q208574 | 97.0 |
| Q36507 | 42.0 |
| Q133825 | 34.0 |
| Q82837 | 17.0 |
| Q608698 | 1.0 |
| Q12727620 | 2.0 |
| Q162114 | 2.0 |
| Q16609257 | 2.0 |
| Q14454066 | 1.0 |
| Q2177715 | 20.0 |
| Q3100060 | 12.0 |
| Q357762 | 35.0 |
| Q656680 | 61.0 |
| Q209766 | 45.0 |
| Q46305 | 2.0 |
| Q6459 | 14.0 |
| Q4838283 | 1.0 |
| Q366383 | 12.0 |
| Q2734012 | 1.0 |
| Q823886 | 4.0 |
| Q6697964 | 2.0 |
| Q269982 | 12.0 |
| Q157256 | 34.0 |
| Q313204 | 24.0 |
| Q19241931 | 2.0 |
| Q256884 | 14.0 |
| Q1280932 | 58.0 |
| Q327840 | 1.0 |
| Q3051219 | 5.0 |
| Q2850597 | 11.0 |
| Q494723 | 55.0 |
| Q285631 | 5.0 |
| Q19649995 | 1.0 |
| Q981905 | 3.0 |
| Q1429577 | 14.0 |
| Q2055853 | 9.0 |
| Q1335355 | 33.0 |
| Q346 | 29.0 |
| Q7270 | 99.0 |
| Q1158231 | 63.0 |
| Q14863070 | 23.0 |
| Q1914832 | 18.0 |
| Q165363 | 44.0 |
| Q7776097 | 2.0 |
| Q1095594 | 1.0 |
| Q11194978 | 1.0 |
| Q16277823 | 32.0 |
| Q256785 | 2.0 |
| Q451986 | 9.0 |
| Q3539602 | 2.0 |
| Q536594 | 32.0 |
| Q922639 | 4.0 |
| Q3553646 | 13.0 |
| Q157290 | 18.0 |
| Q1828467 | 2.0 |
| Q2213808 | 2.0 |
| Q669476 | 7.0 |
| Q1932369 | 2.0 |
| Q737835 | 135.0 |
| Q11052127 | 1.0 |
| Q2567853 | 4.0 |
| Q19307447 | 3.0 |
| Q113971 | 15.0 |
| Q3741086 | 59.0 |
| Q7437813 | 6.0 |
| Q471145 | 17.0 |
| Q364264 | 3.0 |
| Q7617750 | 6.0 |
| Q641471 | 3.0 |
| Q865493 | 10.0 |
| Q462042 | 12.0 |
| Q2329132 | 2.0 |
| Q27143 | 6.0 |
| Q1042721 | 17.0 |
| Q374670 | 7.0 |
| Q2739127 | 14.0 |
| Q193105 | 74.0 |
| Q154339 | 2.0 |
| Q1511268 | 4.0 |
| Q2477872 | 31.0 |
| Q7472715 | 2.0 |
| Q433697 | 8.0 |
| Q1754163 | 32.0 |
| Q3572698 | 2.0 |
| Q1625604 | 9.0 |
| Q70294 | 33.0 |
| Q50718 | 26.0 |
| Q3959623 | 3.0 |
| Q661251 | 6.0 |
| Q1134339 | 5.0 |
| Q1071576 | 20.0 |
| Q3201194 | 12.0 |
| Q2996134 | 1.0 |
| Q4924652 | 92.0 |
| Q7450090 | 2.0 |
| Q360346 | 4.0 |
| Q14521122 | 1.0 |
| Q2625719 | 1.0 |
| Q403104 | 9.0 |
| Q2105302 | 1.0 |
| Q3566057 | 17.0 |
| Q11432001 | 2.0 |
| Q856941 | 12.0 |
| Q7784912 | 2.0 |
| Q5421400 | 1.0 |
| Q1285463 | 23.0 |
| Q52835 | 3.0 |
| Q14330803 | 38.0 |
| Q1277934 | 60.0 |
| Q11226682 | 1.0 |
| Q4956350 | 2.0 |
| Q3105985 | 15.0 |
| Q314561 | 2.0 |
| Q937697 | 1.0 |
| Q357 | 31.0 |
| Q1845656 | 1.0 |
| Q1341507 | 51.0 |
| Q700744 | 8.0 |
| Q260918 | 7.0 |
| Q2378505 | 11.0 |
| Q170842 | 4.0 |
| Q49199 | 55.0 |
| Q10896167 | 1.0 |
| Q4761237 | 9.0 |
| Q24458 | 8.0 |
| Q615115 | 13.0 |
| Q1753773 | 3.0 |
| Q58472 | 7.0 |
| Q242749 | 7.0 |
| Q240966 | 2.0 |
| Q1608464 | 2.0 |
| Q5746036 | 1.0 |
| Q222829 | 2.0 |
| Q43263 | 9.0 |
| Q2119002 | 1.0 |
| Q228512 | 17.0 |
| Q551828 | 30.0 |
| Q648471 | 8.0 |
| Q11073601 | 1.0 |
| Q3439198 | 47.0 |
| Q1510790 | 4.0 |
| Q1800376 | 8.0 |
| Q3268013 | 2.0 |
| Q1632587 | 3.0 |
| Q19261305 | 2.0 |
| Q88001 | 10.0 |
| Q350753 | 1.0 |
| Q2682795 | 51.0 |
| Q684305 | 2.0 |
| Q274201 | 8.0 |
| Q4916 | 68.0 |
| Q5052503 | 1.0 |
| Q15633370 | 3.0 |
| Q164479 | 7.0 |
| Q1702233 | 3.0 |
| Q2042015 | 7.0 |
| Q13806345 | 1.0 |
| Q33866 | 28.0 |
| Q6073899 | 4.0 |
| Q14346629 | 7.0 |
| Q342428 | 17.0 |
| Q81228 | 2.0 |
| Q2088585 | 2.0 |
| Q17319132 | 10.0 |
| Q99964 | 5.0 |
| Q7117513 | 9.0 |
| Q14027216 | 1.0 |
| Q465848 | 27.0 |
| Q11122003 | 3.0 |
| Q1382252 | 10.0 |
| Q706590 | 14.0 |
| Q426984 | 2.0 |
| Q916967 | 57.0 |
| Q581939 | 12.0 |
| Q181052 | 4.0 |
| Q318296 | 44.0 |
| Q765547 | 1.0 |
| Q10851308 | 8.0 |
| Q319046 | 1.0 |
| Q10950494 | 1.0 |
| Q204082 | 5.0 |
| Q5394255 | 2.0 |
| Q1171889 | 19.0 |
| Q502392 | 5.0 |
| Q869107 | 11.0 |
| Q5977539 | 2.0 |
| Q3978005 | 11.0 |
| Q959524 | 13.0 |
| Q192716 | 2.0 |
| Q21968 | 4.0 |
| Q14860112 | 5.0 |
| Q139656 | 3.0 |
| Q3604 | 24.0 |
| Q1501117 | 7.0 |
| Q1090526 | 6.0 |
| Q221394 | 17.0 |
| Q918733 | 12.0 |
| Q7144976 | 3.0 |
| Q2318 | 7.0 |
| Q568072 | 9.0 |
| Q3939151 | 4.0 |
| Q5441987 | 2.0 |
| Q932256 | 6.0 |
| Q15936940 | 24.0 |
| Q19803443 | 26.0 |
| Q1334949 | 19.0 |
| Q51720 | 6.0 |
| Q1378067 | 3.0 |
| Q538159 | 8.0 |
| Q603161 | 13.0 |
| Q109260 | 4.0 |
| Q709019 | 32.0 |
| Q7913874 | 20.0 |
| Q16918909 | 1.0 |
| Q6820933 | 15.0 |
| Q16277329 | 78.0 |
| Q18536157 | 3.0 |
| Q1017282 | 10.0 |
| Q19370747 | 1.0 |
| Q632443 | 13.0 |
| Q17769 | 10.0 |
| Q445135 | 7.0 |
| Q141514 | 11.0 |
| Q7751493 | 2.0 |
| Q14112175 | 1.0 |
| Q2627534 | 2.0 |
| Q133445 | 39.0 |
| Q261764 | 32.0 |
| Q7589975 | 70.0 |
| Q10946661 | 18.0 |
| Q1357045 | 1.0 |
| Q5512664 | 20.0 |
| Q460987 | 10.0 |
| Q4372933 | 2.0 |
| Q592190 | 9.0 |
| Q503352 | 28.0 |
| Q3131179 | 1.0 |
| Q192896 | 54.0 |
| Q3241976 | 16.0 |
| Q18964454 | 4.0 |
| Q5975829 | 1.0 |
| Q1147644 | 8.0 |
| Q378892 | 9.0 |
| Q690123 | 6.0 |
| Q2502784 | 2.0 |
| Q1363494 | 24.0 |
| Q1529138 | 15.0 |
| Q345763 | 9.0 |
| Q151707 | 6.0 |
| Q744536 | 35.0 |
| Q2864545 | 59.0 |
| Q574370 | 2.0 |
| Q833878 | 26.0 |
| Q10886913 | 2.0 |
| Q11516840 | 3.0 |
| Q2914944 | 1.0 |
| Q385134 | 3.0 |
| Q783726 | 2.0 |
| Q93633 | 11.0 |
| Q11416208 | 3.0 |
| Q17248 | 8.0 |
| Q3565459 | 18.0 |
| Q2708539 | 1.0 |
| Q320308 | 13.0 |
| Q15949680 | 2.0 |
| Q5371024 | 2.0 |
| Q3028111 | 4.0 |
| Q722619 | 8.0 |
| Q794257 | 7.0 |
| Q1060362 | 2.0 |
| Q465842 | 36.0 |
| Q213412 | 6.0 |
| Q43878 | 11.0 |
| Q18757446 | 2.0 |
| Q3462999 | 3.0 |
| Q47159 | 23.0 |
| Q525151 | 8.0 |
| Q7378 | 15.0 |
| Q5154113 | 2.0 |
| Q1761072 | 33.0 |
| Q874691 | 7.0 |
| Q5589688 | 9.0 |
| Q99947 | 18.0 |
| Q7250587 | 25.0 |
| Q15070438 | 2.0 |
| Q550320 | 7.0 |
| Q305780 | 8.0 |
| Q7711917 | 2.0 |
| Q18064227 | 2.0 |
| Q1885966 | 3.0 |
| Q1970219 | 12.0 |
| Q7802217 | 2.0 |
| Q631056 | 4.0 |
| Q427708 | 3.0 |
| Q508168 | 21.0 |
| Q9697643 | 2.0 |
| Q6261993 | 7.0 |
| Q1101255 | 7.0 |
| Q991010 | 10.0 |
| Q19262620 | 2.0 |
| Q391558 | 5.0 |
| Q1356598 | 12.0 |
| Q1349027 | 1.0 |
| Q501101 | 5.0 |
| Q5626129 | 1.0 |
| Q767073 | 6.0 |
| Q152520 | 4.0 |
| Q260492 | 2.0 |
| Q157092 | 11.0 |
| Q4771920 | 2.0 |
| Q25854 | 1.0 |
| Q10663689 | 1.0 |
| Q695591 | 5.0 |
| Q3449401 | 3.0 |
| Q266205 | 20.0 |
| Q2703962 | 5.0 |
| Q1886400 | 6.0 |
| Q848233 | 7.0 |
| Q839772 | 5.0 |
| Q10219312 | 2.0 |
| Q3970382 | 2.0 |
| Q942932 | 15.0 |
| Q1317642 | 4.0 |
| Q964475 | 4.0 |
| Q94586 | 26.0 |
| Q621042 | 2.0 |
| Q3659553 | 2.0 |
| Q7442166 | 7.0 |
| Q2161801 | 17.0 |
| Q1788731 | 1.0 |
| Q11446285 | 17.0 |
| Q156126 | 1.0 |
| Q1019591 | 11.0 |
| Q531645 | 5.0 |
| Q9174369 | 4.0 |
| Q3494011 | 2.0 |
| Q86593 | 14.0 |
| Q10515635 | 5.0 |
| Q495851 | 6.0 |
| Q863003 | 8.0 |
| Q5733929 | 1.0 |
| Q2504700 | 5.0 |
| Q5019492 | 16.0 |
| Q5288 | 4.0 |
| Q15894334 | 1.0 |
| Q4351728 | 2.0 |
| Q541859 | 13.0 |
| Q7278657 | 4.0 |
| Q2008712 | 2.0 |
| Q2097856 | 1.0 |
| Q15735634 | 6.0 |
| Q7299936 | 2.0 |
| Q102946 | 11.0 |
| Q10484679 | 7.0 |
| Q2597366 | 3.0 |
| Q3727169 | 2.0 |
| Q4985 | 10.0 |
| Q2036094 | 3.0 |
| Q20548 | 5.0 |
| Q9889 | 22.0 |
| Q7463857 | 2.0 |
| Q18040630 | 2.0 |
| Q6978251 | 2.0 |
| Q2225886 | 3.0 |
| Q19394909 | 2.0 |
| Q56065 | 6.0 |
| Q805391 | 6.0 |
| Q2219603 | 1.0 |
| Q1368128 | 8.0 |
| Q1961219 | 2.0 |
| Q16657471 | 1.0 |
| Q278325 | 2.0 |
| Q1491615 | 3.0 |
| Q427385 | 26.0 |
| Q2034080 | 2.0 |
| Q3926567 | 1.0 |
| Q11148261 | 1.0 |
| Q5002117 | 2.0 |
| Q3734506 | 30.0 |
| Q17195344 | 5.0 |
| Q1019349 | 10.0 |
| Q469494 | 31.0 |
| Q7009812 | 3.0 |
| Q720539 | 16.0 |
| Q2609138 | 4.0 |
| Q496384 | 3.0 |
| Q2114727 | 5.0 |
| Q1582306 | 4.0 |
| Q434826 | 11.0 |
| Q446941 | 38.0 |
| Q289355 | 7.0 |
| Q429065 | 2.0 |
| Q474697 | 30.0 |
| Q113245 | 2.0 |
| Q51192 | 11.0 |
| Q5496206 | 1.0 |
| Q2277274 | 1.0 |
| Q4941487 | 2.0 |
| Q366834 | 23.0 |
| Q313456 | 1.0 |
| Q7711958 | 2.0 |
| Q7355345 | 3.0 |
| Q3713141 | 1.0 |
| Q1264566 | 3.0 |
| Q233337 | 12.0 |
| Q60199 | 5.0 |
| Q378939 | 3.0 |
| Q4650117 | 7.0 |
| Q574076 | 1.0 |
| Q4733330 | 26.0 |
| Q18809836 | 17.0 |
| Q47036 | 23.0 |
| Q1116574 | 3.0 |
| Q1673853 | 15.0 |
| Q17538332 | 4.0 |
| Q15725056 | 1.0 |
| Q15093722 | 2.0 |
| Q19517503 | 19.0 |
| Q135684 | 1.0 |
| Q13479470 | 2.0 |
| Q254722 | 2.0 |
| Q2367724 | 13.0 |
| Q171892 | 1.0 |
| Q1771109 | 6.0 |
| Q3167844 | 2.0 |
| Q52682 | 14.0 |
| Q188409 | 1.0 |
| Q17597496 | 1.0 |
| Q282488 | 2.0 |
| Q18510559 | 11.0 |
| Q6053351 | 8.0 |
| Q17277242 | 5.0 |
| Q1472951 | 10.0 |
| Q1437441 | 4.0 |
| Q543660 | 3.0 |
| Q774496 | 2.0 |
| Q2632681 | 2.0 |
| Q6698706 | 2.0 |
| Q3106889 | 1.0 |
| Q7473859 | 2.0 |
| Q6585542 | 2.0 |
| Q3105890 | 1.0 |
| Q1727068 | 3.0 |
| Q6812586 | 4.0 |
| Q551420 | 12.0 |
| Q5808361 | 2.0 |
| Q730182 | 2.0 |
| Q191677 | 4.0 |
| Q17608430 | 2.0 |
| Q862735 | 8.0 |
| Q1169994 | 2.0 |
| Q3931476 | 2.0 |
| Q352624 | 3.0 |
| Q267993 | 2.0 |
| Q2537872 | 2.0 |
| Q16199951 | 1.0 |
| Q6694937 | 6.0 |
| Q19689428 | 22.0 |
| Q898786 | 9.0 |
| Q58636 | 5.0 |
| Q3879056 | 29.0 |
| Q10026565 | 1.0 |
| Q3205402 | 2.0 |
| Q1612211 | 2.0 |
| Q1142509 | 9.0 |
| Q11431344 | 2.0 |
| Q6853207 | 2.0 |
| Q427439 | 2.0 |
| Q235759 | 25.0 |
| Q658928 | 13.0 |
| Q272445 | 7.0 |
| Q162698 | 8.0 |
| Q1130053 | 7.0 |
| Q392102 | 5.0 |
| Q866786 | 7.0 |
| Q3448345 | 12.0 |
| Q5521623 | 4.0 |
| Q34962 | 7.0 |
| Q741555 | 33.0 |
| Q7574297 | 5.0 |
| Q335608 | 11.0 |
| Q2964731 | 5.0 |
| Q3483567 | 7.0 |
| Q10673362 | 3.0 |
| Q2524599 | 4.0 |
| Q1077880 | 2.0 |
| Q195662 | 11.0 |
| Q323186 | 1.0 |
| Q776694 | 2.0 |
| Q4411507 | 2.0 |
| Q965780 | 51.0 |
| Q738678 | 1.0 |
| Q652339 | 6.0 |
| Q7111562 | 1.0 |
| Q33575 | 1.0 |
| Q18735363 | 2.0 |
| Q936710 | 14.0 |
| Q269573 | 14.0 |
| Q9281633 | 2.0 |
| Q7465170 | 2.0 |
| Q248640 | 10.0 |
| Q18627288 | 1.0 |
| Q5679279 | 6.0 |
| Q2657037 | 2.0 |
| Q244224 | 11.0 |
| Q2162602 | 5.0 |
| Q2362118 | 19.0 |
| Q1062135 | 12.0 |
| Q2523659 | 2.0 |
| Q5283850 | 2.0 |
| Q1621936 | 4.0 |
| Q681168 | 24.0 |
| Q166931 | 23.0 |
| Q793611 | 3.0 |
| Q945715 | 1.0 |
| Q19249463 | 2.0 |
| Q1282960 | 1.0 |
| Q14589639 | 1.0 |
| Q2418926 | 5.0 |
| Q223973 | 23.0 |
| Q198231 | 5.0 |
| Q2443037 | 2.0 |
| Q1233562 | 5.0 |
| Q524434 | 9.0 |
| Q705981 | 4.0 |
| Q304436 | 6.0 |
| Q779377 | 5.0 |
| Q5030307 | 2.0 |
| Q835474 | 1.0 |
| Q14852015 | 10.0 |
| Q6229276 | 2.0 |
| Q1100730 | 23.0 |
| Q5004010 | 1.0 |
| Q156806 | 10.0 |
| Q134393 | 8.0 |
| Q1936839 | 5.0 |
| Q14763008 | 27.0 |
| Q957230 | 2.0 |
| Q4178491 | 3.0 |
| Q16937785 | 1.0 |
| Q2901 | 9.0 |
| Q700768 | 18.0 |
| Q1383559 | 2.0 |
| Q1113836 | 2.0 |
| Q929008 | 8.0 |
| Q3086401 | 10.0 |
| Q90136 | 14.0 |
| Q2597150 | 2.0 |
| Q17493611 | 1.0 |
| Q151170 | 2.0 |
| Q599004 | 2.0 |
| Q1629782 | 1.0 |
| Q15491087 | 4.0 |
| Q5954899 | 2.0 |
| Q11783587 | 2.0 |
| Q13408009 | 2.0 |
| Q564428 | 1.0 |
| Q18768637 | 3.0 |
| Q15127544 | 1.0 |
| Q3765897 | 3.0 |
| Q19249131 | 2.0 |
| Q3479508 | 2.0 |
| Q2401302 | 9.0 |
| Q1683103 | 4.0 |
| Q43690 | 16.0 |
| Q51419 | 6.0 |
| Q7097823 | 2.0 |
| Q863887 | 1.0 |
| Q6186235 | 1.0 |
| Q553333 | 1.0 |
| Q5121511 | 2.0 |
| Q766939 | 7.0 |
| Q576304 | 6.0 |
| Q3626706 | 5.0 |
| Q32335 | 11.0 |
| Q7998787 | 2.0 |
| Q1156459 | 2.0 |
| Q41483 | 2.0 |
| Q1217934 | 10.0 |
| Q16649655 | 4.0 |
| Q18585581 | 3.0 |
| Q962805 | 6.0 |
| Q1133753 | 18.0 |
| Q81025 | 23.0 |
| Q11243986 | 1.0 |
| Q11562490 | 1.0 |
| Q85848 | 4.0 |
| Q763905 | 2.0 |
| Q943503 | 8.0 |
| Q1247436 | 10.0 |
| Q5504765 | 1.0 |
| Q7604991 | 1.0 |
| Q5060915 | 2.0 |
| Q90663 | 9.0 |
| Q2165544 | 7.0 |
| Q209458 | 2.0 |
| Q16596114 | 4.0 |
| Q211208 | 8.0 |
| Q476083 | 9.0 |
| Q157915 | 14.0 |
| Q3032842 | 2.0 |
| Q138259 | 12.0 |
| Q7580679 | 2.0 |
| Q213723 | 9.0 |
| Q1345099 | 1.0 |
| Q3929190 | 21.0 |
| Q84217 | 4.0 |
| Q7705866 | 2.0 |
| Q18017534 | 7.0 |
| Q7940749 | 1.0 |
| Q10871674 | 1.0 |
| Q1264384 | 6.0 |
| Q2404173 | 2.0 |
| Q27262 | 5.0 |
| Q13611173 | 5.0 |
| Q248807 | 11.0 |
| Q2828863 | 2.0 |
| Q6755163 | 2.0 |
| Q352218 | 7.0 |
| Q81733 | 5.0 |
| Q10602579 | 4.0 |
| Q18574972 | 2.0 |
| Q19260072 | 2.0 |
| Q625657 | 5.0 |
| Q4854930 | 2.0 |
| Q7889366 | 2.0 |
| Q11113 | 20.0 |
| Q13904833 | 1.0 |
| Q18558874 | 3.0 |
| Q492625 | 3.0 |
| Q832107 | 3.0 |
| Q212642 | 9.0 |
| Q3428532 | 5.0 |
| Q461272 | 10.0 |
| Q14701640 | 2.0 |
| Q1082320 | 9.0 |
| Q5459493 | 7.0 |
| Q752526 | 3.0 |
| Q521775 | 5.0 |
| Q19010158 | 24.0 |
| Q2278120 | 5.0 |
| Q2369187 | 1.0 |
| Q877219 | 2.0 |
| Q156557 | 7.0 |
| Q282015 | 18.0 |
| Q765023 | 10.0 |
| Q84530 | 2.0 |
| Q1632 | 10.0 |
| Q11181104 | 1.0 |
| Q1039641 | 3.0 |
| Q680996 | 30.0 |
| Q558806 | 12.0 |
| Q5370814 | 2.0 |
| Q3095943 | 2.0 |
| Q17491984 | 1.0 |
| Q699306 | 6.0 |
| Q207879 | 2.0 |
| Q1616733 | 1.0 |
| Q17490716 | 1.0 |
| Q4729898 | 2.0 |
| Q721000 | 2.0 |
| Q385795 | 5.0 |
| Q15987625 | 2.0 |
| Q3617774 | 9.0 |
| Q4921839 | 1.0 |
| Q12049698 | 3.0 |
| Q275051 | 1.0 |
| Q3552065 | 6.0 |
| Q19334507 | 1.0 |
| Q4802648 | 7.0 |
| Q765591 | 7.0 |
| Q14863696 | 12.0 |
| Q1148707 | 4.0 |
| Q3730026 | 26.0 |
| Q136804 | 14.0 |
| Q16872729 | 7.0 |
| Q12039565 | 2.0 |
| Q6845491 | 1.0 |
| Q4541657 | 2.0 |
| Q1577872 | 2.0 |
display(graph.outDegrees)
| id | outDegree |
|---|---|
| Q7009812 | 6.0 |
| Q3100008 | 8.0 |
| Q212642 | 11.0 |
| Q16527372 | 7.0 |
| Q462541 | 8.0 |
| Q7111689 | 4.0 |
| Q6088156 | 9.0 |
| Q3426238 | 9.0 |
| Q6210681 | 7.0 |
| Q25854 | 14.0 |
| Q1356554 | 24.0 |
| Q3389055 | 6.0 |
| Q10663150 | 8.0 |
| Q5536413 | 4.0 |
| Q5451183 | 8.0 |
| Q18613082 | 9.0 |
| Q3742112 | 8.0 |
| Q4207553 | 7.0 |
| Q16226580 | 5.0 |
| Q43247 | 17.0 |
| Q1336075 | 7.0 |
| Q16207095 | 5.0 |
| Q1292902 | 23.0 |
| Q266114 | 11.0 |
| Q11443746 | 6.0 |
| Q7200876 | 5.0 |
| Q1382515 | 5.0 |
| Q11859977 | 6.0 |
| Q15453460 | 11.0 |
| Q4426465 | 5.0 |
| Q5388565 | 9.0 |
| Q1463701 | 6.0 |
| Q1747643 | 5.0 |
| Q3400684 | 5.0 |
| Q7807388 | 11.0 |
| Q1083401 | 6.0 |
| Q7817504 | 8.0 |
| Q1337896 | 15.0 |
| Q2454521 | 11.0 |
| Q20680 | 6.0 |
| Q2351798 | 8.0 |
| Q129456 | 9.0 |
| Q5977539 | 4.0 |
| Q15451328 | 6.0 |
| Q6700406 | 11.0 |
| Q3097931 | 10.0 |
| Q1587858 | 3.0 |
| Q15432563 | 7.0 |
| Q19389261 | 13.0 |
| Q609731 | 4.0 |
| Q1893811 | 8.0 |
| Q2679094 | 19.0 |
| Q8014269 | 6.0 |
| Q1892834 | 6.0 |
| Q2575434 | 6.0 |
| Q5339455 | 5.0 |
| Q3839408 | 6.0 |
| Q3102956 | 11.0 |
| Q3727434 | 7.0 |
| Q11496600 | 4.0 |
| Q17592516 | 6.0 |
| Q3434308 | 8.0 |
| Q2923674 | 5.0 |
| Q2597150 | 7.0 |
| Q40463 | 10.0 |
| Q16058748 | 8.0 |
| Q17122168 | 7.0 |
| Q694620 | 7.0 |
| Q19458151 | 7.0 |
| Q3819367 | 19.0 |
| Q6425978 | 7.0 |
| Q3359260 | 38.0 |
| Q18236610 | 5.0 |
| Q7852802 | 6.0 |
| Q269695 | 9.0 |
| Q1462367 | 9.0 |
| Q4908855 | 5.0 |
| Q4249513 | 10.0 |
| Q3501544 | 4.0 |
| Q1976498 | 9.0 |
| Q5394884 | 7.0 |
| Q3309495 | 14.0 |
| Q3644484 | 6.0 |
| Q18577439 | 63.0 |
| Q690123 | 12.0 |
| Q1155646 | 8.0 |
| Q2087467 | 11.0 |
| Q2288912 | 6.0 |
| Q15906820 | 5.0 |
| Q11718563 | 6.0 |
| Q16902103 | 6.0 |
| Q4149315 | 11.0 |
| Q5335126 | 5.0 |
| Q1649741 | 4.0 |
| Q5571559 | 6.0 |
| Q4528800 | 7.0 |
| Q5140096 | 8.0 |
| Q468424 | 6.0 |
| Q92308 | 8.0 |
| Q2861849 | 13.0 |
| Q6281500 | 7.0 |
| Q6708092 | 10.0 |
| Q19912270 | 6.0 |
| Q102713 | 8.0 |
| Q2665735 | 5.0 |
| Q15735634 | 7.0 |
| Q237470 | 8.0 |
| Q68185 | 9.0 |
| Q537253 | 7.0 |
| Q11980334 | 5.0 |
| Q15435075 | 11.0 |
| Q4905445 | 5.0 |
| Q11243986 | 2.0 |
| Q775407 | 15.0 |
| Q1688408 | 6.0 |
| Q6531122 | 5.0 |
| Q1698023 | 7.0 |
| Q7791880 | 7.0 |
| Q1052483 | 5.0 |
| Q4210695 | 8.0 |
| Q817492 | 5.0 |
| Q7850528 | 6.0 |
| Q3387458 | 5.0 |
| Q6652324 | 7.0 |
| Q2115097 | 8.0 |
| Q902746 | 5.0 |
| Q1465137 | 5.0 |
| Q4838336 | 15.0 |
| Q2545989 | 3.0 |
| Q244224 | 11.0 |
| Q3167925 | 8.0 |
| Q426923 | 24.0 |
| Q288491 | 23.0 |
| Q3606761 | 6.0 |
| Q3341114 | 8.0 |
| Q3822446 | 7.0 |
| Q17415605 | 5.0 |
| Q18883434 | 4.0 |
| Q3591015 | 9.0 |
| Q3828682 | 6.0 |
| Q3435646 | 8.0 |
| Q788779 | 6.0 |
| Q7128971 | 5.0 |
| Q2637616 | 8.0 |
| Q2484056 | 5.0 |
| Q78191 | 8.0 |
| Q13427376 | 6.0 |
| Q17610203 | 7.0 |
| Q374479 | 13.0 |
| Q3893970 | 11.0 |
| Q5110690 | 5.0 |
| Q956955 | 26.0 |
| Q7721646 | 7.0 |
| Q16448279 | 6.0 |
| Q5504765 | 5.0 |
| Q18565908 | 6.0 |
| Q4477478 | 6.0 |
| Q2590982 | 8.0 |
| Q1908450 | 10.0 |
| Q465415 | 7.0 |
| Q2964731 | 7.0 |
| Q19263096 | 4.0 |
| Q10465742 | 3.0 |
| Q5961885 | 6.0 |
| Q829871 | 6.0 |
| Q6774064 | 8.0 |
| Q17970926 | 5.0 |
| Q3808318 | 7.0 |
| Q2699732 | 8.0 |
| Q7572306 | 5.0 |
| Q4756867 | 7.0 |
| Q467016 | 9.0 |
| Q93633 | 10.0 |
| Q19404110 | 5.0 |
| Q4177721 | 7.0 |
| Q2910243 | 6.0 |
| Q19399648 | 4.0 |
| Q4978093 | 5.0 |
| Q5082867 | 10.0 |
| Q7822155 | 6.0 |
| Q1901428 | 7.0 |
| Q3269483 | 4.0 |
| Q113201 | 4.0 |
| Q1700851 | 8.0 |
| Q14914326 | 5.0 |
| Q17341552 | 5.0 |
| Q6489990 | 6.0 |
| Q1359210 | 8.0 |
| Q1705171 | 10.0 |
| Q409952 | 20.0 |
| Q2617390 | 5.0 |
| Q7963987 | 6.0 |
| Q7159564 | 5.0 |
| Q7426223 | 5.0 |
| Q5879705 | 5.0 |
| Q3119676 | 6.0 |
| Q11466345 | 5.0 |
| Q13734223 | 3.0 |
| Q196674 | 6.0 |
| Q7025321 | 7.0 |
| Q19801080 | 7.0 |
| Q976031 | 9.0 |
| Q1337384 | 7.0 |
| Q5627078 | 9.0 |
| Q62684 | 13.0 |
| Q691595 | 5.0 |
| Q4351386 | 6.0 |
| Q6135268 | 7.0 |
| Q7465330 | 6.0 |
| Q7667786 | 5.0 |
| Q1027742 | 4.0 |
| Q11532836 | 5.0 |
| Q7002711 | 5.0 |
| Q4011307 | 6.0 |
| Q4933766 | 9.0 |
| Q11677101 | 5.0 |
| Q2480182 | 12.0 |
| Q3612901 | 9.0 |
| Q18043517 | 5.0 |
| Q4154934 | 8.0 |
| Q6132989 | 9.0 |
| Q16447958 | 9.0 |
| Q658850 | 8.0 |
| Q7308445 | 6.0 |
| Q9262617 | 5.0 |
| Q2062844 | 3.0 |
| Q14818136 | 124.0 |
| Q1989968 | 5.0 |
| Q1361177 | 4.0 |
| Q6986771 | 5.0 |
| Q9889 | 25.0 |
| Q3181474 | 5.0 |
| Q6208750 | 7.0 |
| Q7323569 | 7.0 |
| Q6229276 | 6.0 |
| Q4917243 | 3.0 |
| Q7804998 | 4.0 |
| Q958089 | 9.0 |
| Q19263796 | 5.0 |
| Q4018069 | 6.0 |
| Q2128765 | 8.0 |
| Q2708539 | 5.0 |
| Q5487254 | 6.0 |
| Q3072890 | 7.0 |
| Q18666149 | 4.0 |
| Q10268060 | 14.0 |
| Q15106791 | 7.0 |
| Q1421687 | 7.0 |
| Q5543155 | 9.0 |
| Q6422033 | 6.0 |
| Q3220913 | 19.0 |
| Q41483 | 44.0 |
| Q16573805 | 7.0 |
| Q2977488 | 10.0 |
| Q176776 | 9.0 |
| Q3106266 | 6.0 |
| Q854448 | 7.0 |
| Q865493 | 3.0 |
| Q939718 | 14.0 |
| Q3078235 | 10.0 |
| Q17403622 | 6.0 |
| Q13882235 | 8.0 |
| Q2226180 | 7.0 |
| Q330960 | 7.0 |
| Q288141 | 6.0 |
| Q2323271 | 11.0 |
| Q4312203 | 8.0 |
| Q5769840 | 5.0 |
| Q443387 | 8.0 |
| Q10941765 | 3.0 |
| Q1766758 | 7.0 |
| Q4968079 | 7.0 |
| Q3632451 | 10.0 |
| Q13899930 | 3.0 |
| Q1362171 | 8.0 |
| Q17109874 | 5.0 |
| Q6207924 | 6.0 |
| Q15028676 | 5.0 |
| Q4681829 | 8.0 |
| Q2910054 | 5.0 |
| Q6731286 | 6.0 |
| Q5039880 | 5.0 |
| Q5541406 | 7.0 |
| Q839361 | 4.0 |
| Q7725634 | 5.0 |
| Q568507 | 20.0 |
| Q7029354 | 7.0 |
| Q4003201 | 15.0 |
| Q7128929 | 8.0 |
| Q4373267 | 8.0 |
| Q1528675 | 9.0 |
| Q18208623 | 12.0 |
| Q301678 | 9.0 |
| Q4308491 | 5.0 |
| Q18916873 | 7.0 |
| Q14936017 | 4.0 |
| Q11511280 | 7.0 |
| Q6701489 | 7.0 |
| Q1259250 | 5.0 |
| Q15713728 | 9.0 |
| Q188409 | 6.0 |
| Q18881844 | 4.0 |
| Q2431161 | 5.0 |
| Q2425465 | 9.0 |
| Q1222 | 93.0 |
| Q5587200 | 5.0 |
| Q5783202 | 6.0 |
| Q11594792 | 7.0 |
| Q4364669 | 10.0 |
| Q5494850 | 6.0 |
| Q1040257 | 8.0 |
| Q3900779 | 13.0 |
| Q996054 | 6.0 |
| Q7668201 | 6.0 |
| Q3894424 | 7.0 |
| Q4261577 | 6.0 |
| Q3305131 | 7.0 |
| Q3372618 | 41.0 |
| Q2473974 | 10.0 |
| Q7518359 | 7.0 |
| Q7173641 | 5.0 |
| Q1267 | 21.0 |
| Q14566771 | 3.0 |
| Q4880151 | 6.0 |
| Q5271124 | 5.0 |
| Q15969154 | 8.0 |
| Q2034080 | 10.0 |
| Q794257 | 3.0 |
| Q1611414 | 6.0 |
| Q6104858 | 6.0 |
| Q2650639 | 8.0 |
| Q1577097 | 4.0 |
| Q1565102 | 8.0 |
| Q2917130 | 13.0 |
| Q6054381 | 9.0 |
| Q10551263 | 5.0 |
| Q126682 | 6.0 |
| Q18211641 | 5.0 |
| Q2279544 | 20.0 |
| Q1535874 | 6.0 |
| Q16027287 | 8.0 |
| Q7800559 | 7.0 |
| Q2407633 | 4.0 |
| Q1340404 | 19.0 |
| Q210669 | 24.0 |
| Q1467210 | 6.0 |
| Q2644948 | 11.0 |
| Q807429 | 6.0 |
| Q4697281 | 5.0 |
| Q119144 | 12.0 |
| Q1393883 | 8.0 |
| Q4761385 | 12.0 |
| Q4322717 | 11.0 |
| Q6672923 | 9.0 |
| Q16105203 | 5.0 |
| Q99091 | 16.0 |
| Q16007860 | 8.0 |
| Q4131528 | 5.0 |
| Q4641667 | 5.0 |
| Q4141711 | 3.0 |
| Q1463181 | 8.0 |
| Q3766379 | 9.0 |
| Q384628 | 5.0 |
| Q430085 | 9.0 |
| Q5099139 | 3.0 |
| Q5343792 | 7.0 |
| Q17415823 | 5.0 |
| Q7328437 | 5.0 |
| Q15729936 | 7.0 |
| Q8849651 | 8.0 |
| Q6536837 | 9.0 |
| Q3535808 | 15.0 |
| Q804839 | 2.0 |
| Q500855 | 5.0 |
| Q3485377 | 5.0 |
| Q3566057 | 3.0 |
| Q2902017 | 20.0 |
| Q51192 | 12.0 |
| Q17331932 | 7.0 |
| Q3609896 | 9.0 |
| Q12377234 | 5.0 |
| Q962805 | 14.0 |
| Q360346 | 9.0 |
| Q3281060 | 8.0 |
| Q505695 | 8.0 |
| Q2441917 | 6.0 |
| Q1745257 | 7.0 |
| Q6730 | 42.0 |
| Q168173 | 7.0 |
| Q17615495 | 6.0 |
| Q477257 | 9.0 |
| Q10938752 | 3.0 |
| Q6232452 | 5.0 |
| Q3096374 | 8.0 |
| Q46130 | 319.0 |
| Q4970690 | 6.0 |
| Q5293714 | 5.0 |
| Q7240698 | 5.0 |
| Q3003487 | 5.0 |
| Q16028471 | 7.0 |
| Q5035837 | 5.0 |
| Q4353463 | 4.0 |
| Q146431 | 3.0 |
| Q6776994 | 6.0 |
| Q1373220 | 7.0 |
| Q6421961 | 6.0 |
| Q11139797 | 3.0 |
| Q7175194 | 8.0 |
| Q6191585 | 5.0 |
| Q542270 | 13.0 |
| Q1734482 | 7.0 |
| Q1941696 | 7.0 |
| Q311758 | 4.0 |
| Q10464003 | 3.0 |
| Q730135 | 5.0 |
| Q4678699 | 8.0 |
| Q17445336 | 6.0 |
| Q2156280 | 8.0 |
| Q777591 | 20.0 |
| Q1222093 | 6.0 |
| Q209638 | 4.0 |
| Q3108109 | 4.0 |
| Q10473640 | 2.0 |
| Q8004400 | 8.0 |
| Q5170378 | 10.0 |
| Q2057382 | 7.0 |
| Q2075276 | 10.0 |
| Q95874 | 7.0 |
| Q4949367 | 5.0 |
| Q551998 | 3.0 |
| Q15441311 | 6.0 |
| Q16649655 | 8.0 |
| Q5185069 | 4.0 |
| Q15851125 | 7.0 |
| Q16571633 | 5.0 |
| Q19261119 | 5.0 |
| Q17308233 | 5.0 |
| Q5706625 | 8.0 |
| Q17103356 | 6.0 |
| Q2082514 | 5.0 |
| Q458043 | 7.0 |
| Q3783431 | 7.0 |
| Q672217 | 4.0 |
| Q4395687 | 8.0 |
| Q12018973 | 6.0 |
| Q4070465 | 9.0 |
| Q254722 | 6.0 |
| Q4962344 | 5.0 |
| Q16298546 | 5.0 |
| Q2445366 | 6.0 |
| Q778017 | 9.0 |
| Q3667461 | 6.0 |
| Q6700337 | 7.0 |
| Q7324431 | 8.0 |
| Q5370919 | 6.0 |
| Q667712 | 7.0 |
| Q2248472 | 3.0 |
| Q9220240 | 4.0 |
| Q7738088 | 3.0 |
| Q17769 | 11.0 |
| Q7702128 | 7.0 |
| Q149406 | 28.0 |
| Q592904 | 5.0 |
| Q1192319 | 28.0 |
| Q1612211 | 10.0 |
| Q5055948 | 5.0 |
| Q877219 | 3.0 |
| Q1553524 | 8.0 |
| Q5076858 | 7.0 |
| Q5562311 | 6.0 |
| Q16651776 | 8.0 |
| Q1085825 | 7.0 |
| Q7597940 | 5.0 |
| Q3811109 | 9.0 |
| Q2725121 | 8.0 |
| Q6456600 | 6.0 |
| Q7966173 | 7.0 |
| Q4864074 | 9.0 |
| Q5151930 | 5.0 |
| Q3379240 | 6.0 |
| Q1929456 | 5.0 |
| Q7086549 | 5.0 |
| Q2627534 | 5.0 |
| Q54108 | 15.0 |
| Q5592107 | 9.0 |
| Q5055980 | 8.0 |
| Q5373279 | 10.0 |
| Q33866 | 38.0 |
| Q3104417 | 6.0 |
| Q7172660 | 6.0 |
| Q4240987 | 10.0 |
| Q157194 | 13.0 |
| Q683529 | 25.0 |
| Q64902 | 11.0 |
| Q5159009 | 2.0 |
| Q2484915 | 5.0 |
| Q4739574 | 6.0 |
| Q862735 | 11.0 |
| Q5982361 | 8.0 |
| Q6752292 | 10.0 |
| Q1511268 | 7.0 |
| Q2969466 | 4.0 |
| Q6306263 | 5.0 |
| Q11672086 | 6.0 |
| Q775789 | 11.0 |
| Q1269017 | 25.0 |
| Q1311528 | 6.0 |
| Q6218684 | 7.0 |
| Q2759567 | 5.0 |
| Q5835077 | 8.0 |
| Q18575764 | 8.0 |
| Q11738053 | 5.0 |
| Q1517369 | 13.0 |
| Q1147644 | 10.0 |
| Q123728 | 9.0 |
| Q3306304 | 5.0 |
| Q5379274 | 5.0 |
| Q13102661 | 6.0 |
| Q4407200 | 6.0 |
| Q8186583 | 5.0 |
| Q2715837 | 5.0 |
| Q2978105 | 6.0 |
| Q586996 | 5.0 |
| Q4524087 | 5.0 |
| Q1434467 | 6.0 |
| Q16447673 | 10.0 |
| Q502593 | 7.0 |
| Q1510790 | 9.0 |
| Q87970 | 11.0 |
| Q3230007 | 3.0 |
| Q5561098 | 5.0 |
| Q63826 | 16.0 |
| Q16014875 | 8.0 |
| Q17596012 | 7.0 |
| Q1692949 | 7.0 |
| Q151651 | 3.0 |
| Q335608 | 9.0 |
| Q1462586 | 11.0 |
| Q9389874 | 7.0 |
| Q16095627 | 8.0 |
| Q16440033 | 5.0 |
| Q13563224 | 5.0 |
| Q2899492 | 6.0 |
| Q1761354 | 7.0 |
| Q3571607 | 5.0 |
| Q3858242 | 8.0 |
| Q2480827 | 7.0 |
| Q3123909 | 6.0 |
| Q3963947 | 6.0 |
| Q2850597 | 14.0 |
| Q3208956 | 9.0 |
| Q7963363 | 10.0 |
| Q3840391 | 6.0 |
| Q7173626 | 6.0 |
| Q1559075 | 7.0 |
| Q3120101 | 6.0 |
| Q2494619 | 5.0 |
| Q94813 | 25.0 |
| Q6182628 | 8.0 |
| Q4925477 | 32.0 |
| Q373552 | 15.0 |
| Q192716 | 5.0 |
| Q2394333 | 2.0 |
| Q165994 | 2.0 |
| Q5145884 | 46.0 |
| Q1143271 | 4.0 |
| Q3562562 | 7.0 |
| Q17366732 | 6.0 |
| Q3123760 | 6.0 |
| Q2137976 | 9.0 |
| Q15997785 | 6.0 |
| Q3370754 | 7.0 |
| Q7278898 | 9.0 |
| Q434389 | 17.0 |
| Q882506 | 12.0 |
| Q229305 | 13.0 |
| Q1306229 | 3.0 |
| Q11416675 | 5.0 |
| Q237960 | 7.0 |
| Q2543137 | 12.0 |
| Q15447573 | 8.0 |
| Q5558017 | 7.0 |
| Q7789270 | 15.0 |
| Q5371442 | 6.0 |
| Q18773021 | 8.0 |
| Q2437074 | 7.0 |
| Q762668 | 5.0 |
| Q2008712 | 11.0 |
| Q12727620 | 4.0 |
| Q17493785 | 7.0 |
| Q85753 | 11.0 |
| Q5702473 | 5.0 |
| Q2985143 | 10.0 |
| Q149784 | 7.0 |
| Q15921764 | 27.0 |
| Q17456252 | 6.0 |
| Q6507902 | 5.0 |
| Q1743657 | 6.0 |
| Q15996333 | 5.0 |
| Q920991 | 10.0 |
| Q4935340 | 8.0 |
| Q681374 | 4.0 |
| Q3168126 | 8.0 |
| Q19883638 | 6.0 |
| Q4316803 | 5.0 |
| Q6669082 | 6.0 |
| Q469635 | 8.0 |
| Q5489254 | 5.0 |
| Q255943 | 3.0 |
| Q221394 | 9.0 |
| Q10948409 | 3.0 |
| Q1281850 | 22.0 |
| Q927592 | 7.0 |
| Q551901 | 13.0 |
| Q978114 | 6.0 |
| Q16065309 | 6.0 |
| Q1517641 | 17.0 |
| Q19521 | 6.0 |
| Q3812949 | 12.0 |
| Q3573326 | 5.0 |
| Q17154274 | 5.0 |
| Q10526787 | 5.0 |
| Q1343075 | 6.0 |
| Q3592172 | 5.0 |
| Q19658123 | 5.0 |
| Q425579 | 19.0 |
| Q1608877 | 11.0 |
| Q5058726 | 5.0 |
| Q7072132 | 5.0 |
| Q10941569 | 3.0 |
| Q79358 | 5.0 |
| Q16204635 | 7.0 |
| Q1383559 | 5.0 |
| Q7282772 | 9.0 |
| Q15650745 | 5.0 |
| Q15969462 | 8.0 |
| Q6780014 | 5.0 |
| Q5787568 | 6.0 |
| Q8561671 | 18.0 |
| Q2388389 | 6.0 |
| Q7150471 | 13.0 |
| Q5236563 | 9.0 |
| Q3705491 | 12.0 |
| Q6696330 | 5.0 |
| Q181540 | 47.0 |
| Q11576923 | 7.0 |
| Q1939184 | 4.0 |
| Q7155485 | 6.0 |
| Q6968455 | 5.0 |
| Q798673 | 27.0 |
| Q1403188 | 11.0 |
| Q6880354 | 5.0 |
| Q573248 | 5.0 |
| Q889532 | 9.0 |
| Q1889655 | 9.0 |
| Q1341653 | 6.0 |
| Q463504 | 8.0 |
| Q18574093 | 5.0 |
| Q5231996 | 7.0 |
| Q3860653 | 6.0 |
| Q105695 | 11.0 |
| Q17607584 | 6.0 |
| Q3027877 | 10.0 |
| Q1339792 | 15.0 |
| Q5569183 | 6.0 |
| Q17596343 | 7.0 |
| Q1449466 | 7.0 |
| Q13411225 | 7.0 |
| Q462665 | 10.0 |
| Q7962972 | 4.0 |
| Q323149 | 6.0 |
| Q1613137 | 8.0 |
| Q2827002 | 8.0 |
| Q15498265 | 7.0 |
| Q7282684 | 9.0 |
| Q1577440 | 14.0 |
| Q5476688 | 6.0 |
| Q1155498 | 9.0 |
| Q641755 | 6.0 |
| Q2343509 | 7.0 |
| Q81302 | 5.0 |
| Q1086170 | 7.0 |
| Q207528 | 11.0 |
| Q7721616 | 12.0 |
| Q7437008 | 8.0 |
| Q2062779 | 9.0 |
| Q7784118 | 5.0 |
| Q5564001 | 5.0 |
| Q3030993 | 6.0 |
| Q4730901 | 8.0 |
| Q5421400 | 5.0 |
| Q7052986 | 5.0 |
| Q18917945 | 13.0 |
| Q4726078 | 10.0 |
| Q6173588 | 5.0 |
| Q1929080 | 3.0 |
| Q1735755 | 5.0 |
| Q15081195 | 9.0 |
| Q73407 | 14.0 |
| Q19334318 | 9.0 |
| Q106427 | 8.0 |
| Q17595129 | 7.0 |
| Q2645336 | 7.0 |
| Q16265273 | 5.0 |
| Q77793 | 10.0 |
| Q17160484 | 5.0 |
| Q15080887 | 5.0 |
| Q16666831 | 4.0 |
| Q15437917 | 9.0 |
| Q602644 | 12.0 |
| Q1734744 | 5.0 |
| Q17300883 | 5.0 |
| Q5285822 | 5.0 |
| Q541859 | 13.0 |
| Q15619449 | 6.0 |
| Q18478644 | 18.0 |
| Q7335601 | 4.0 |
| Q2969057 | 5.0 |
| Q4078027 | 7.0 |
| Q5593380 | 5.0 |
| Q266932 | 3.0 |
| Q1329359 | 9.0 |
| Q1506904 | 13.0 |
| Q768867 | 14.0 |
| Q7282258 | 6.0 |
| Q95998 | 6.0 |
| Q1865 | 23.0 |
| Q1677098 | 13.0 |
| Q3703286 | 8.0 |
| Q7439069 | 5.0 |
| Q737356 | 13.0 |
| Q546273 | 4.0 |
| Q1690165 | 17.0 |
| Q181476 | 7.0 |
| Q5592809 | 6.0 |
| Q11096205 | 3.0 |
| Q3986841 | 11.0 |
| Q4970956 | 9.0 |
| Q9288424 | 6.0 |
| Q3463137 | 3.0 |
| Q5258997 | 7.0 |
| Q1214343 | 15.0 |
| Q2039935 | 9.0 |
| Q15452554 | 6.0 |
| Q2847776 | 8.0 |
| Q19838026 | 6.0 |
| Q10946661 | 23.0 |
| Q4802648 | 5.0 |
| Q78657 | 14.0 |
| Q3119441 | 5.0 |
| Q39593 | 10.0 |
| Q1870071 | 8.0 |
| Q1423606 | 8.0 |
| Q5654373 | 14.0 |
| Q2831539 | 7.0 |
| Q1503588 | 9.0 |
| Q6230292 | 12.0 |
| Q5386024 | 8.0 |
| Q443409 | 16.0 |
| Q6742352 | 5.0 |
| Q3204661 | 7.0 |
| Q3531066 | 7.0 |
| Q14468658 | 3.0 |
| Q1066887 | 8.0 |
| Q3265431 | 7.0 |
| Q3309200 | 7.0 |
| Q7486915 | 6.0 |
| Q2146246 | 6.0 |
| Q2700756 | 7.0 |
| Q4500463 | 7.0 |
| Q1361651 | 9.0 |
| Q3154330 | 9.0 |
| Q153291 | 9.0 |
| Q1610557 | 7.0 |
| Q70801 | 11.0 |
| Q19162754 | 5.0 |
| Q706441 | 5.0 |
| Q10443159 | 3.0 |
| Q369272 | 6.0 |
| Q7729430 | 29.0 |
| Q7762163 | 5.0 |
| Q3026988 | 6.0 |
| Q3110937 | 8.0 |
| Q3913166 | 4.0 |
| Q4724535 | 8.0 |
| Q1114799 | 9.0 |
| Q11399339 | 5.0 |
| Q8083637 | 10.0 |
| Q7287543 | 5.0 |
| Q209790 | 14.0 |
| Q1609090 | 5.0 |
| Q1725320 | 13.0 |
| Q1730633 | 9.0 |
| Q3838210 | 5.0 |
| Q6902773 | 10.0 |
| Q3497818 | 5.0 |
| Q6941693 | 7.0 |
| Q908512 | 5.0 |
| Q6459 | 10.0 |
| Q482672 | 8.0 |
| Q1983911 | 7.0 |
| Q5247188 | 3.0 |
| Q9219915 | 4.0 |
| Q3194868 | 10.0 |
| Q832790 | 4.0 |
| Q11538052 | 5.0 |
| Q2743155 | 5.0 |
| Q4175575 | 6.0 |
| Q7968723 | 9.0 |
| Q552857 | 12.0 |
| Q1690073 | 8.0 |
| Q7794785 | 9.0 |
| Q6206300 | 6.0 |
| Q1604346 | 7.0 |
| Q650015 | 15.0 |
| Q10877075 | 3.0 |
| Q3842122 | 25.0 |
| Q2633031 | 9.0 |
| Q4139649 | 3.0 |
| Q2551899 | 8.0 |
| Q2447803 | 3.0 |
| Q358432 | 15.0 |
| Q2413284 | 7.0 |
| Q704696 | 20.0 |
| Q6557790 | 9.0 |
| Q6042525 | 6.0 |
| Q13424091 | 6.0 |
| Q1664782 | 1.0 |
| Q1082236 | 9.0 |
| Q14426941 | 7.0 |
| Q7071185 | 8.0 |
| Q1152011 | 9.0 |
| Q448757 | 9.0 |
| Q72662 | 7.0 |
| Q3324799 | 2.0 |
| Q606262 | 10.0 |
| Q750242 | 9.0 |
| Q2140013 | 12.0 |
| Q3706968 | 11.0 |
| Q5411912 | 5.0 |
| Q1718348 | 6.0 |
| Q1236557 | 7.0 |
| Q3492587 | 9.0 |
| Q5719346 | 6.0 |
| Q17350067 | 9.0 |
| Q5880528 | 5.0 |
| Q726290 | 7.0 |
| Q71591 | 8.0 |
| Q6700446 | 6.0 |
| Q14216837 | 7.0 |
| Q2203318 | 5.0 |
| Q1106905 | 4.0 |
| Q196440 | 11.0 |
| Q4727159 | 5.0 |
| Q4983602 | 5.0 |
| Q5606289 | 10.0 |
| Q1326360 | 12.0 |
| Q7513520 | 4.0 |
| Q18433 | 22.0 |
| Q1389748 | 11.0 |
| Q5976499 | 4.0 |
| Q8051485 | 6.0 |
| Q12795468 | 7.0 |
| Q7449742 | 6.0 |
| Q763905 | 6.0 |
| Q3972260 | 18.0 |
| Q159592 | 13.0 |
| Q8177730 | 4.0 |
| Q2498708 | 6.0 |
| Q4994677 | 9.0 |
| Q8020540 | 5.0 |
| Q14088547 | 5.0 |
| Q17329933 | 5.0 |
| Q4757275 | 5.0 |
| Q18158784 | 6.0 |
| Q4211147 | 3.0 |
| Q443133 | 6.0 |
| Q381769 | 10.0 |
| Q6186489 | 5.0 |
| Q1504688 | 6.0 |
| Q4314633 | 7.0 |
| Q16652253 | 5.0 |
| Q2217911 | 9.0 |
| Q4893207 | 8.0 |
| Q11096920 | 7.0 |
| Q431989 | 8.0 |
| Q7282865 | 9.0 |
| Q16843793 | 6.0 |
| Q10520291 | 5.0 |
| Q325433 | 9.0 |
| Q3607449 | 8.0 |
| Q4933585 | 11.0 |
| Q1897036 | 6.0 |
| Q272918 | 7.0 |
| Q536594 | 10.0 |
| Q13983027 | 3.0 |
| Q18279673 | 5.0 |
| Q1097391 | 5.0 |
| Q5556342 | 5.0 |
| Q18095 | 22.0 |
| Q1361689 | 8.0 |
| Q357 | 4.0 |
| Q18763624 | 6.0 |
| Q234068 | 13.0 |
| Q18845578 | 4.0 |
| Q5719244 | 5.0 |
| Q6238670 | 5.0 |
| Q6085194 | 5.0 |
| Q3704637 | 10.0 |
| Q7631476 | 2.0 |
| Q1334949 | 21.0 |
| Q6177861 | 6.0 |
| Q15034427 | 6.0 |
| Q7822462 | 5.0 |
| Q16833448 | 5.0 |
| Q183210 | 14.0 |
| Q15907451 | 5.0 |
| Q19115447 | 5.0 |
| Q14584254 | 9.0 |
| Q16010426 | 5.0 |
| Q18646618 | 88.0 |
| Q1885966 | 5.0 |
| Q702096 | 4.0 |
| Q548502 | 5.0 |
| Q4104083 | 6.0 |
| Q1729776 | 8.0 |
| Q17329905 | 8.0 |
| Q18177714 | 6.0 |
| Q3752357 | 6.0 |
| Q11516840 | 7.0 |
| Q346 | 33.0 |
| Q334180 | 11.0 |
| Q17334334 | 33.0 |
| Q1701025 | 7.0 |
| Q1930887 | 5.0 |
| Q4296262 | 8.0 |
| Q4522439 | 14.0 |
| Q5203833 | 6.0 |
| Q446941 | 12.0 |
| Q4532408 | 9.0 |
| Q501941 | 7.0 |
| Q128757 | 8.0 |
| Q6271630 | 17.0 |
| Q1637787 | 17.0 |
| Q1041779 | 3.0 |
| Q1613519 | 10.0 |
| Q3112340 | 14.0 |
| Q11904802 | 3.0 |
| Q366834 | 13.0 |
| Q11881334 | 5.0 |
| Q4196027 | 12.0 |
| Q6538066 | 5.0 |
| Q623787 | 7.0 |
| Q16163 | 128.0 |
| Q4788748 | 5.0 |
| Q5232343 | 6.0 |
| Q6686585 | 6.0 |
| Q156600 | 9.0 |
| Q2891152 | 5.0 |
| Q114975 | 13.0 |
| Q365040 | 6.0 |
| Q932256 | 9.0 |
| Q17299229 | 5.0 |
| Q5310345 | 6.0 |
| Q19826384 | 5.0 |
| Q4985 | 14.0 |
| Q12351531 | 5.0 |
| Q385795 | 12.0 |
| Q155797 | 4.0 |
| Q1663371 | 6.0 |
| Q1349416 | 6.0 |
| Q15303868 | 6.0 |
| Q1043046 | 8.0 |
| Q64726 | 9.0 |
| Q11572913 | 9.0 |
| Q4726032 | 10.0 |
| Q2502784 | 4.0 |
| Q17386855 | 5.0 |
| Q3525048 | 5.0 |
| Q6186734 | 6.0 |
| Q2262510 | 5.0 |
| Q166931 | 25.0 |
| Q1645003 | 15.0 |
| Q447739 | 6.0 |
| Q15477 | 55.0 |
| Q4090481 | 9.0 |
| Q17386748 | 12.0 |
| Q2354251 | 28.0 |
| Q1366442 | 9.0 |
| Q1556616 | 7.0 |
| Q3521423 | 6.0 |
| Q1717821 | 7.0 |
| Q16007520 | 8.0 |
| Q11259642 | 5.0 |
| Q85342 | 12.0 |
| Q3609348 | 9.0 |
| Q14920689 | 6.0 |
| Q1491572 | 7.0 |
| Q3079187 | 7.0 |
| Q18792651 | 4.0 |
val twoCycles = graph.find("(a)-[r1]->(b); (b)-[r2]->(a)")
display(twoCycles)
val threeCycles = graph.find("(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(a)")
display(threeCycles)
val twoCyclesSym = twoCycles.filter("r1.rel == r2.rel")
display(twoCyclesSym)
val aSymFraction = twoCyclesSym.filter("r1.rel == \"a\"").count.toDouble / graph.edges.filter("rel == \"a\"").count.toDouble
print(s"${aSymFraction * 100}% relationships of type a are symmetric relations\n")
NaN% relationships of type a are symmetric relations
aSymFraction: Double = NaN
Fetching the descriptions
The original data did not contain any text descriptions of the different entities and relations. In order to perform interesting analysis of the knowledge graph we had to fetch textual descriptions and associat them with the data. The following script can be used to fetch textual descriptions for the different entities in the graph.
NOTE: The script was not run on the actual cluster, but on a local server. The descriptions were then uploaded to the databricks cluster, but we include this part for the sake of completion. If it is of interest it should be straightforward to run the same thing on the Databricks cluster.
import pandas as pd
from wikidata.client import Client
from tqdm import tqdm
from collections import defaultdict
import requests
import time
import urllib
TIMEOUT = 0.5
BATCHSIZE = 400
LANGUAGE_PRIORITY = dict([(x.lower(), i) if x.lower()[:2] != "en" else (x.lower(),0) for i, x in enumerate(["EN", "EN-CA", "EN-GB", "EN-AU", "SV", "DE", "ES", "PT", "PT-BR", "NL", "IT", "FR", "ZH", "JA", "AR", "RU", "EL"])])
SERVICEURL = "https://query.wikidata.org/sparql"
def batchfetch_WD(query, ids, which_ones : str = ["label", "description"]):
r = requests.get(SERVICEURL, params={'query': query}, headers={'Accept': 'application/sparql-results+json'})
if r.status_code == 429:
time.sleep(TIMEOUT)
return batchfetch_WD(query, ids, which_ones=which_ones)
data = r.json()['results']['bindings']
ret = defaultdict(dict)
for res_entry in data:
idx = res_entry['id']['value'].split("/")[-1]
if "label" not in ret[id]:
ret[idx]["label"] = []
if "description" not in ret[id]:
ret[idx]["description"] = []
for which in which_ones:
if which in res_entry:
ret[idx][which].append((res_entry[which]["xml:lang"][:2], res_entry[which]['value']))
for id in ret:
for which in which_ones:
if len(ret[id][which]) == 0:
ret[id][which] = "Unknown"
else:
ret[id][which] = sorted(ret[id][which], key=lambda x: LANGUAGE_PRIORITY[x[0].lower()])[0][1]
nullresp = []
for id in ids:
if id not in ret:
nullresp.append(id)
return ret, nullresp
df = pd.read_csv("dataset/ogbl_wikikg2/original/train_2015.csv.gz",header=None)
df.columns = ["head", "relation", "tail"]
unique_entities = sorted(set(df['head'].unique()).union(set(df['tail'].unique())))
unique_relations = sorted(set(df['relation'].unique()))
client = Client()
res = defaultdict(dict)
if args.skip_rels is False:
for rel in tqdm(unique_relations):
try:
rel = client.get(rel, load=True)
res[rel.id]["label"] = rel.label
res[rel.id]["description"] = rel.description
except urllib.error.HTTPError:
res[rel]["label"] = "Unknown"
res[rel]["description"] = "Unknown"
df = pd.DataFrame.from_dict(res, orient='index')
df.to_csv("relation_descriptions.csv")
res = defaultdict(dict)
with open("entity_descriptions_buffer.csv", "w+") as f:
f.write("id,label,description\n")
for entitites in tqdm(range(0, len(unique_entities), BATCHSIZE)):
ids = unique_entities[entitites:(entitites + BATCHSIZE)]
query = ["wd:" + ids[i] for i in range(len(ids))]
query = " ".join(query)
query = '''SELECT distinct * WHERE { VALUES ?id {''' + query + '''} ?id rdfs:label ?label . FILTER (langMatches( lang(?label), "EN" ) ) ?id schema:description ?description FILTER (langMatches( lang(?description), "EN" ) || langMatches( lang(?description), "SV" ) || langMatches( lang(?description), "PT") ) } '''
try:
results, nullresponse = batchfetch_WD(query, ids)
for result in results:
res[result]["label"] = results[result]["label"]
res[result]["description"] = results[result]["description"]
f.write("{},{},{}\n".format(result, results[result]["label"], results[result]["description"]))
for id in nullresponse:
res[id]["label"] = "Unknown"
res[id]["description"] = "Unknown"
f.write("{},{},{}\n".format(id, "Unknown", "Unknown"))
except:
for id in ids:
f.write("{},{},{}\n".format(id, "Error", "Error"))
df = pd.DataFrame.from_dict(res, orient='index')
df.to_csv("entities_descriptions.csv")
Load Wiki data
This short notebook loads the Wiki dataset into a GraphFrames dataframe. It is mostly a utility, that can be executed from other notebooks using the %run command.
// Imports
import spark.implicits._
import org.graphframes._
import spark.implicits._
import org.graphframes._
// Read data into datatframe (can take a couple minutes)
val df = spark.read.option("sep", ",").csv("dbfs:///wikikg-v2/original/train_2015.csv.gz")
df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
val list = List("src", "rel", "dst")
val edgesDF = df.toDF(list:_*)
list: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
var df1 = spark.sql("select entid, IF(label = 'Unknown', entid, label) as label, IF(description = 'Unknown', entid, description) as description from `entities_descriptions_1_csv`")
val entdescdf = df1.toDF()
df1: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
entdescdf: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
import spark.implicits._
val mergedDf = edgesDF.as("d1").join(entdescdf.select("entid","label").as("d2"), ($"d1.src" === $"d2.entid"))
val list = List("src", "rel", "dst", "srcentid", "srclabel")
val mergedDF = mergedDf.toDF(list:_*)
import spark.implicits._
mergedDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
list: List[String] = List(src, rel, dst, srcentid, srclabel)
mergedDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
val mergedDf2 = mergedDF.as("d1").join(entdescdf.select("entid","label").as("d2"), ($"d1.dst" === $"d2.entid"))
val list2 = List("src", "rel", "dst", "srcentid", "srclabel", "dstentid", "dstlabel")
val mergedDF2 = mergedDf2.toDF(list2:_*)
// Load the names of the different relations
val rel_name_df = spark.read.option("sep", ",").csv("dbfs:/FileStore/tables/relation_descriptions.csv")
//rel_name_df.show()
val list3 = List("relid", "label", "description")
val relnamedf = rel_name_df.toDF(list3:_*)
val finalDf = mergedDF2.as("d1").join(relnamedf.select("relid","label").as("d2"), ($"d1.rel" === $"d2.relid"))
val list4 = List("src", "rel", "dst", "srcentid", "srclabel", "dstentid", "dstlabel", "relid", "rellabel")
val finalDF = finalDf.toDF(list4:_*).select("srclabel", "rellabel", "dstlabel")
val edgesDF_ = finalDF.select("srclabel", "rellabel", "dstlabel")
val list5 = List("src", "rel", "dst")
val edgesDF = edgesDF_.toDF(list5:_*)
mergedDf2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
list2: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel)
mergedDF2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
rel_name_df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list3: List[String] = List(relid, label, description)
relnamedf: org.apache.spark.sql.DataFrame = [relid: string, label: string ... 1 more field]
finalDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 7 more fields]
list4: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel, relid, rellabel)
finalDF: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
edgesDF_: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
list5: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
// From https://stackoverflow.com/questions/57513292/how-to-make-graphframe-from-edge-dataframe-only
val verticesDf = edgesDF.select("src").union(edgesDF.select("dst")).distinct().withColumnRenamed("src", "id")
val graph = GraphFrame(verticesDf,edgesDF)
verticesDf: org.apache.spark.sql.DataFrame = [id: string]
graph: org.graphframes.GraphFrame = GraphFrame(v:[id: string], e:[src: string, dst: string ... 1 more field])
Exploring the WikiKG90Mv2 dataset
In this notebook we do some initial data exploration and visualization to get a feeling for the Wiki knowledge graph that we are working with. We start by loading the graph.
./02_load_data
import spark.implicits._
import org.graphframes._
df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
We will start our exploration by computing some basic graph properties.
df1: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
entdescdf: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
import spark.implicits._
mergedDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
list: List[String] = List(src, rel, dst, srcentid, srclabel)
mergedDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
val nNodes = graph.vertices.count
val nEdges = graph.edges.count
val density = nEdges.toFloat / (nNodes * (nNodes-1)) // Measure of density for directed graph, fraction of possible edges present
print(s"The graph has ${nNodes} nodes and ${nEdges} edges. This corresponds to a density of ${density}.\n")
The graph has 2317717 nodes and 16109182 edges. This corresponds to a density of 2.998837E-6.
nNodes: Long = 2317717
nEdges: Long = 16109182
density: Float = 2.998837E-6
mergedDf2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
list2: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel)
mergedDF2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
rel_name_df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list3: List[String] = List(relid, label, description)
relnamedf: org.apache.spark.sql.DataFrame = [relid: string, label: string ... 1 more field]
finalDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 7 more fields]
list4: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel, relid, rellabel)
finalDF: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
edgesDF_: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
list5: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
The graph is indeed massive, but the low density shows that it is also incredibly sparse. For a knowledge graph this level of sparsity is to be expected, as most concepts are not directly related.
Node Degrees
Next let's investigate the distribution (histograms) of node degrees in the graph. Since we have a directed graph we consider in- and out-degrees separately.
verticesDf: org.apache.spark.sql.DataFrame = [id: string]
graph: org.graphframes.GraphFrame = GraphFrame(v:[id: string], e:[src: string, dst: string ... 1 more field])
val inDegrees = graph.inDegrees
val outDegrees = graph.outDegrees
val degrees = inDegrees.join(outDegrees, "id").cache()
display(degrees)
| id | inDegree | outDegree |
|---|---|---|
| & Yet & Yet | 2.0 | 5.0 |
| (10499) 1986 RN5 | 2.0 | 6.0 |
| (11058) 1991 PN10 | 2.0 | 6.0 |
| (117404) 2005 AC9 | 1.0 | 5.0 |
| (13020) 1988 PW2 | 1.0 | 5.0 |
| (136198) 2003 UJ296 | 1.0 | 5.0 |
| (15141) 2000 EP106 | 2.0 | 4.0 |
| (15683) 1981 EX25 | 2.0 | 6.0 |
| (16307) 7569 P-L | 2.0 | 8.0 |
| (16467) 1990 FD3 | 2.0 | 6.0 |
| (17383) 1981 EE12 | 2.0 | 6.0 |
| (20188) 1997 AC18 | 2.0 | 6.0 |
| (20671) 1999 UX48 | 2.0 | 5.0 |
| (20927) 1126 T-1 | 2.0 | 8.0 |
| (21340) 1997 CS19 | 2.0 | 6.0 |
| (21944) 1999 VA118 | 2.0 | 6.0 |
| (21996) 1999 XP31 | 2.0 | 6.0 |
| (22133) 2000 UO56 | 2.0 | 6.0 |
| (22288) 1988 TR2 | 2.0 | 6.0 |
| (22313) 1991 GP3 | 2.0 | 6.0 |
| (22511) 1997 YC10 | 2.0 | 6.0 |
| (22726) 1998 SZ72 | 2.0 | 6.0 |
| (22755) 1998 WO9 | 2.0 | 6.0 |
| (23299) 2001 AP9 | 2.0 | 6.0 |
| (23723) 1998 HG40 | 2.0 | 6.0 |
| (24205) 1999 XC48 | 2.0 | 6.0 |
| (24831) 1995 SX4 | 2.0 | 6.0 |
| (25571) 1999 XP195 | 2.0 | 6.0 |
| (257203) 2008 RW122 | 2.0 | 6.0 |
| (25831) 2000 DH111 | 2.0 | 6.0 |
| (26030) 6004 P-L | 2.0 | 8.0 |
| (26094) 1988 NU | 2.0 | 6.0 |
| (26476) 2000 AK185 | 2.0 | 6.0 |
| (27142) 1998 XG61 | 2.0 | 6.0 |
| (27242) 1999 TN219 | 2.0 | 5.0 |
| (27484) 2000 GN94 | 2.0 | 6.0 |
| (28247) 1999 BP3 | 2.0 | 6.0 |
| (28404) 1999 TQ5 | 2.0 | 7.0 |
| (28463) 2000 AG168 | 2.0 | 5.0 |
| (28580) 2000 EJ104 | 2.0 | 6.0 |
| (28804) 2000 HC81 | 2.0 | 6.0 |
| (28960) 2001 DZ81 | 2.0 | 6.0 |
| (29000) 2607 P-L | 2.0 | 8.0 |
| (29022) 6630 P-L | 2.0 | 8.0 |
| (29387) 1996 JC6 | 2.0 | 6.0 |
| (29505) 1997 WV44 | 2.0 | 6.0 |
| (30466) 2000 OP14 | 2.0 | 6.0 |
| (30611) 2627 P-L | 2.0 | 8.0 |
| (30644) 6601 P-L | 2.0 | 8.0 |
| (30689) 4318 T-2 | 2.0 | 8.0 |
| (30845) 1991 PQ3 | 2.0 | 6.0 |
| (30896) 1993 FX26 | 2.0 | 6.0 |
| (31148) 1997 UO8 | 2.0 | 6.0 |
| (31259) 1998 EB3 | 2.0 | 6.0 |
| (31394) 1998 YX9 | 2.0 | 7.0 |
| (31497) 1999 CW61 | 2.0 | 6.0 |
| (31554) 1999 EJ2 | 2.0 | 6.0 |
| (31732) 1999 JB71 | 2.0 | 6.0 |
| (32382) 2000 QE187 | 2.0 | 6.0 |
| (32543) 2001 QL11 | 2.0 | 6.0 |
| (32791) 1989 TQ2 | 2.0 | 6.0 |
| (34242) 2000 QD100 | 2.0 | 6.0 |
| (343976) 2011 LC21 | 1.0 | 5.0 |
| (34712) 2001 ON103 | 2.0 | 6.0 |
| (34931) 6621 P-L | 2.0 | 8.0 |
| (35035) 1981 ER29 | 2.0 | 6.0 |
| (35051) 1981 ED47 | 2.0 | 6.0 |
| (35166) 1993 QD8 | 2.0 | 6.0 |
| (35182) 1993 US1 | 2.0 | 6.0 |
| (35224) 1995 BN1 | 2.0 | 6.0 |
| (35253) 1996 AB7 | 2.0 | 6.0 |
| (35480) 1998 FN5 | 2.0 | 5.0 |
| (35743) 1999 GP29 | 2.0 | 6.0 |
| (35803) 1999 JT40 | 2.0 | 6.0 |
| (36050) 1999 RE18 | 2.0 | 6.0 |
| (36481) 2000 QU30 | 2.0 | 6.0 |
| (37085) 2000 UO63 | 2.0 | 6.0 |
| (37160) 2000 WR5 | 2.0 | 6.0 |
| (37427) 2001 YJ82 | 2.0 | 6.0 |
| (37438) 2599 P-L | 2.0 | 8.0 |
| (37581) 1990 SU15 | 2.0 | 6.0 |
| (37697) 1995 YW4 | 2.0 | 6.0 |
| (38113) 1999 JB30 | 2.0 | 6.0 |
| (38403) 1999 RU197 | 2.0 | 6.0 |
| (38620) 2000 AQ186 | 2.0 | 6.0 |
| (39099) 2000 WS12 | 2.0 | 5.0 |
| (39298) 2001 FV132 | 2.0 | 5.0 |
| (39431) 5178 T-2 | 2.0 | 8.0 |
| (46556) 1991 FU3 | 1.0 | 5.0 |
| (58167) 1990 QM3 | 1.0 | 5.0 |
| (65225) 2002 EK44 | 1.0 | 5.0 |
| (6861) 1991 FA3 | 2.0 | 6.0 |
| (70304) 1999 RE133 | 1.0 | 5.0 |
| (73077) 2002 GT4 | 2.0 | 6.0 |
| (73262) 2002 JK47 | 2.0 | 6.0 |
| (73289) 2002 JW64 | 2.0 | 6.0 |
| (73291) 2002 JG65 | 2.0 | 6.0 |
| (73335) 2002 JN110 | 2.0 | 6.0 |
| (73344) 2002 JT119 | 2.0 | 6.0 |
| (73455) 2002 NT36 | 2.0 | 6.0 |
| (73550) 2003 PG9 | 2.0 | 6.0 |
| (73881) 1997 CD22 | 2.0 | 6.0 |
| (73924) 1997 MN3 | 2.0 | 6.0 |
| (74054) 1998 JT4 | 2.0 | 6.0 |
| (74411) 1999 AE5 | 2.0 | 6.0 |
| (76834) 2000 SA244 | 1.0 | 5.0 |
| (7951) 1992 WC2 | 2.0 | 6.0 |
| (82293) 2001 KJ38 | 2.0 | 6.0 |
| (82321) 2001 KE69 | 2.0 | 6.0 |
| (82945) 2001 QN117 | 2.0 | 6.0 |
| (9343) 1991 PO11 | 2.0 | 6.0 |
| (9575) 1989 BW1 | 2.0 | 6.0 |
| ...With the Spirit of a Traffic Jam... | 1.0 | 4.0 |
| 07 | 1.0 | 4.0 |
| 1090 | 4.0 | 8.0 |
| 10970 de Zeeuw | 2.0 | 8.0 |
| 10th Anniversary Album | 2.0 | 5.0 |
| 11 bit studios | 3.0 | 3.0 |
| 11055 Honduras | 2.0 | 6.0 |
| 11087 Yamasakimakoto | 2.0 | 7.0 |
| 1110 Jaroslawa | 2.0 | 6.0 |
| 11152 Oomine | 2.0 | 6.0 |
| 11365 NASA | 1.0 | 5.0 |
| 11581 Philipdejager | 2.0 | 6.0 |
| 1159 | 4.0 | 9.0 |
| 11773 Schouten | 2.0 | 9.0 |
| 11th Golden Globe Awards | 2.0 | 3.0 |
| 12161 Avienius | 2.0 | 9.0 |
| 13226 Soulié | 2.0 | 7.0 |
| 1328 SH | 1.0 | 1.0 |
| 1436 | 4.0 | 8.0 |
| 14424 Laval | 2.0 | 7.0 |
| 14499 Satotoshio | 2.0 | 7.0 |
| 1512 | 4.0 | 8.0 |
| 15506 Preygel | 2.0 | 6.0 |
| 1572 | 5.0 | 8.0 |
| 15th Canadian Parliament | 2.0 | 3.0 |
| 16077 Arayhamilton | 2.0 | 6.0 |
| 16090 Lukaszewski | 2.0 | 6.0 |
| 1820 Lohmann | 2.0 | 6.0 |
| 18294 Rudenko | 2.0 | 6.0 |
| 1865 Cerberus | 2.0 | 7.0 |
| 18699 Quigley | 1.0 | 5.0 |
| 1892 Wimbledon Championships – gentlemen's singles | 2.0 | 4.0 |
| 1897 in film | 2.0 | 4.0 |
| 1898 Paris–Roubaix | 2.0 | 3.0 |
| 19 Fortuna | 2.0 | 8.0 |
| 1910 Finnish football championship | 2.0 | 5.0 |
| 1922 U.S. National Championships | 2.0 | 3.0 |
| 1929 Wimbledon Championships – men's singles | 2.0 | 4.0 |
| 1930 Bulgarian State Football Championship | 2.0 | 4.0 |
| 1932 NFL season | 2.0 | 4.0 |
| 1936 Tour de France | 3.0 | 4.0 |
| 1941 Úrvalsdeild | 2.0 | 6.0 |
| 19425 Nicholasrapp | 2.0 | 6.0 |
| 1946 FA Cup Final | 4.0 | 10.0 |
| 1947–48 Serie B | 2.0 | 5.0 |
| 1949–50 Austrian football championship | 2.0 | 5.0 |
| 1951–52 Belgian First Division | 2.0 | 4.0 |
| 1956–57 Eredivisie | 1.0 | 5.0 |
| 1959–60 A PFG | 2.0 | 4.0 |
| 1965 Southeast Asian Peninsular Games | 2.0 | 3.0 |
| 1968–69 Czechoslovak First League | 2.0 | 4.0 |
| 1970s | 14.0 | 5.0 |
| 1971–72 European Cup Winners' Cup | 2.0 | 3.0 |
| 1972 US Open | 6.0 | 4.0 |
| 1980 Fischer-Grand Prix | 2.0 | 3.0 |
| 1984 Argentine Primera División | 2.0 | 4.0 |
| 1984 U.S. Pro Indoor | 2.0 | 3.0 |
| 1987 IAAF World Indoor Championships | 6.0 | 4.0 |
| 1987–88 Japan Soccer League | 2.0 | 3.0 |
| 1992–93 Danish Superliga | 2.0 | 6.0 |
| 1992–93 Scottish Premier Division | 2.0 | 3.0 |
| 1993 IAAF World Cross Country Championships | 2.0 | 4.0 |
| 1993 IAAF World Half Marathon Championships | 1.0 | 3.0 |
| 1993 RCA Championships | 2.0 | 4.0 |
| 1994 Formula One World Championship | 2.0 | 4.0 |
| 2000 FIFA Club World Championship | 1.0 | 2.0 |
| 2000 US Open – women's doubles | 3.0 | 5.0 |
| 2002 World Allround Speed Skating Championships | 3.0 | 11.0 |
| 2004 Hopman Cup | 2.0 | 6.0 |
| 2004 Torneo di Viareggio | 2.0 | 3.0 |
| 2007 China Open | 1.0 | 2.0 |
| 2007 Vuelta a España | 2.0 | 4.0 |
| 2008 DFS Classic | 2.0 | 6.0 |
| 2010 PTT Pattaya Open | 2.0 | 5.0 |
| 2010–11 Primera Divisió | 2.0 | 3.0 |
| 2010–11 Slovenian PrvaLiga | 2.0 | 4.0 |
| 2012–13 Russian Premier League | 2.0 | 4.0 |
| 2013 BB&T Atlanta Open | 1.0 | 3.0 |
| 2013 Internazionali BNL d'Italia | 4.0 | 5.0 |
| 2014 Australian Open – men's singles | 1.0 | 3.0 |
| 2014 European Men's Handball Championship | 1.0 | 21.0 |
| 20536 Tracicarter | 2.0 | 6.0 |
| 2069 | 4.0 | 8.0 |
| 2088 | 4.0 | 7.0 |
| 21.00: Eros Live World Tour 2009/2010 | 2.0 | 3.0 |
| 210432 Dietmarhopp | 1.0 | 5.0 |
| 2136 | 4.0 | 7.0 |
| 2162 | 2.0 | 5.0 |
| 21856 Heathermaria | 2.0 | 6.0 |
| 2294 | 2.0 | 4.0 |
| 23011 Petach | 2.0 | 6.0 |
| 23133 Rishinbehl | 2.0 | 6.0 |
| 23213 Ameliachang | 2.0 | 6.0 |
| 23769 Russellbabb | 2.0 | 6.0 |
| 23773 Sarugaku | 2.0 | 6.0 |
| 24249 Bobbiolson | 2.0 | 5.0 |
| 24318 Vivianlee | 2.0 | 6.0 |
| 25417 Coquillette | 2.0 | 6.0 |
| 2545 Verbiest | 2.0 | 6.0 |
| 25620 Jayaprakash | 2.0 | 6.0 |
| 25925 Jamesfenska | 2.0 | 6.0 |
| 25931 Peterhu | 2.0 | 6.0 |
| 26283 Oswalt | 2.0 | 6.0 |
| 2640 Hällström | 2.0 | 7.0 |
| 27277 Pattybrown | 2.0 | 6.0 |
| 2823 van der Laan | 2.0 | 9.0 |
| 28644 Michaelzhang | 2.0 | 6.0 |
| 28800 Speth | 2.0 | 6.0 |
| 28823 Archibald | 2.0 | 6.0 |
| 2904 | 2.0 | 4.0 |
| 29132 Bradpitt | 2.0 | 6.0 |
| 29438 Zhengjia | 2.0 | 6.0 |
| 296 | 4.0 | 8.0 |
| 29880 Andytran | 2.0 | 6.0 |
| 30211 Sheilah | 2.0 | 6.0 |
| 30241 Donnamower | 2.0 | 6.0 |
| 30441 Curly | 2.0 | 7.0 |
| 31491 Demessie | 2.0 | 6.0 |
| 31823 Viète | 2.0 | 7.0 |
| 3210 | 2.0 | 4.0 |
| 32428 Peterlangley | 2.0 | 6.0 |
| 32807 Quarenghi | 2.0 | 7.0 |
| 3338 Richter | 2.0 | 7.0 |
| 3370 Kohsai | 2.0 | 7.0 |
| 3414 | 2.0 | 4.0 |
| 34220 Pelagiamajoni | 2.0 | 6.0 |
| 34258 Pentland | 2.0 | 6.0 |
| 34273 Franklynwang | 2.0 | 6.0 |
| 34696 Risoldi | 2.0 | 7.0 |
| 34846 Vincent | 2.0 | 6.0 |
| 35403 Latimer | 2.0 | 6.0 |
| 3589 Loyola | 2.0 | 6.0 |
| 3606 | 2.0 | 4.0 |
| 3792 Preston | 2.0 | 7.0 |
| 38 Leda | 2.0 | 7.0 |
| 3846 Hazel | 2.0 | 6.0 |
| 3862 Agekian | 2.0 | 6.0 |
| 3959 | 2.0 | 4.0 |
| 3rd Rock from the Sun, season 2 | 2.0 | 5.0 |
| 3rd: Love Escalation! | 2.0 | 5.0 |
| 4032 | 2.0 | 5.0 |
| 40mm grenade | 8.0 | 1.0 |
| 4108 Rakos | 2.0 | 8.0 |
| 4218 AM | 2.0 | 3.0 |
| 431 Nephele | 2.0 | 7.0 |
| 4419 Allancook | 2.0 | 6.0 |
| 4494 Marimo | 2.0 | 6.0 |
| 4578 Kurashiki | 2.0 | 7.0 |
| 467 | 4.0 | 9.0 |
| 4686 Maisica | 2.0 | 6.0 |
| 475 Ocllo | 2.0 | 6.0 |
| 4821 | 2.0 | 4.0 |
| 4937 | 2.0 | 5.0 |
| 5009 Sethos | 2.0 | 9.0 |
| 5058 AM | 1.0 | 2.0 |
| 5198 AM | 1.0 | 2.0 |
| 5325 | 2.0 | 4.0 |
| 5483 AM | 2.0 | 3.0 |
| 5497 Sararussell | 2.0 | 6.0 |
| 54th Berlin International Film Festival | 3.0 | 3.0 |
| 5645 | 2.0 | 4.0 |
| 5671 Chanal | 2.0 | 5.0 |
| 5705 AM | 2.0 | 3.0 |
| 5736 Sanford | 2.0 | 6.0 |
| 5925 | 2.0 | 4.0 |
| 6056 Donatello | 2.0 | 9.0 |
| 6164 Gerhardmüller | 2.0 | 6.0 |
| 618 Elfriede | 2.0 | 6.0 |
| 6194 | 2.0 | 4.0 |
| 6207 Bourvil | 2.0 | 7.0 |
| 6240 | 2.0 | 4.0 |
| 6613 | 2.0 | 5.0 |
| 6731 | 2.0 | 4.0 |
| 675 | 4.0 | 8.0 |
| 691 | 4.0 | 9.0 |
| 7252 | 2.0 | 4.0 |
| 7273 | 2.0 | 4.0 |
| 7620 Willaert | 2.0 | 9.0 |
| 7711 | 2.0 | 4.0 |
| 7762 | 2.0 | 4.0 |
| 7796 Járacimrman | 2.0 | 7.0 |
| 78125 Salimbeni | 1.0 | 4.0 |
| 7901 Konnai | 2.0 | 6.0 |
| 7960 Condorcet | 2.0 | 7.0 |
| 8284 Cranach | 2.0 | 7.0 |
| 829 | 4.0 | 10.0 |
| 8304 | 2.0 | 4.0 |
| 8433 | 2.0 | 4.0 |
| 8579 Hieizan | 2.0 | 7.0 |
| 8599 Riparia | 2.0 | 9.0 |
| 8930 Kubota | 2.0 | 6.0 |
| 8999 Tashadunn | 2.0 | 6.0 |
| 9009 | 2.0 | 5.0 |
| 9030 | 2.0 | 4.0 |
| 9147 Kourakuen | 2.0 | 7.0 |
| 9225 Daiki | 2.0 | 6.0 |
| 924 Toni | 2.0 | 6.0 |
| 9346 Fernandel | 2.0 | 7.0 |
| 9583 | 2.0 | 4.0 |
| 9586 | 2.0 | 4.0 |
| 9945 Karinaxavier | 2.0 | 6.0 |
| 9993 | 2.0 | 4.0 |
| A Country Lad | 1.0 | 20.0 |
| A Drink Before the War | 1.0 | 7.0 |
| A Looking in View | 1.0 | 4.0 |
| A New Day Yesterday | 1.0 | 5.0 |
| A Night on the Town | 4.0 | 10.0 |
| A Nod Is As Good As a Wink... to a Blind Horse | 2.0 | 5.0 |
| A Violinist and a Flutist Playing Music together (The Musicians) | 1.0 | 7.0 |
| A Winter Haunting | 1.0 | 7.0 |
| A Winter Romance | 2.0 | 3.0 |
| A Young Person's Guide to King Crimson | 2.0 | 7.0 |
| A man dancing with a dog | 1.0 | 7.0 |
| A. D. German Warehouse | 1.0 | 5.0 |
| A.O. Segerberg | 9.0 | 5.0 |
| ABCC6 | 2.0 | 5.0 |
| AVN Hall of Fame | 280.0 | 4.0 |
| Abaurregaina/Abaurrea Alta | 3.0 | 7.0 |
| Abbaye de Buzay | 2.0 | 3.0 |
| Abby Elliott | 2.0 | 6.0 |
| Abner | 46.0 | 7.0 |
| Abo-shinnō | 4.0 | 6.0 |
| Abram Room | 3.0 | 12.0 |
| Abram van Rijckevorsel | 1.0 | 6.0 |
| Ace Attorney | 9.0 | 19.0 |
| Ache Records | 2.0 | 2.0 |
| Adam Williams | 13.0 | 23.0 |
| Adele Astaire | 5.0 | 15.0 |
| Adolf Svoboda | 1.0 | 10.0 |
| Adolf von Blome | 1.0 | 8.0 |
| Adrian Carmack | 1.0 | 6.0 |
| Adriean Videanu | 1.0 | 7.0 |
| Adèle Reinhardt | 4.0 | 4.0 |
| Adélard Godbout | 1.0 | 11.0 |
| Afraid of Sunlight | 1.0 | 6.0 |
| African Cookbook | 1.0 | 4.0 |
| Afshan Azad | 2.0 | 8.0 |
| Agatha Christie's Poirot, season 12 | 2.0 | 5.0 |
| Agawam | 1.0 | 3.0 |
| Agda Helin | 2.0 | 4.0 |
| Agnes of Baden | 1.0 | 6.0 |
| Agnes of Kuenring | 2.0 | 5.0 |
| Agnieszka Sitek | 1.0 | 5.0 |
| Aimo | 32.0 | 1.0 |
| Ain't Nothin' Like Me | 2.0 | 5.0 |
| Ainaro Municipality | 7.0 | 8.0 |
| Ainult unustamiseks | 1.0 | 3.0 |
| Airbus A319 | 3.0 | 5.0 |
| Aitkin | 1.0 | 5.0 |
| Akademie-Verlag | 1.0 | 3.0 |
| Akhil Reed Amar | 1.0 | 7.0 |
| Akimi Yoshida | 4.0 | 9.0 |
| Akinobu Uraka | 1.0 | 7.0 |
| Akinori Iwamura | 1.0 | 7.0 |
| Al Jahra SC | 4.0 | 4.0 |
| Al Santos | 3.0 | 17.0 |
| Al-Khayzuran | 3.0 | 5.0 |
| Aladrén | 3.0 | 7.0 |
| Alan Garner | 5.0 | 25.0 |
| Alan Mills | 1.0 | 19.0 |
| Alan Morinis | 1.0 | 6.0 |
| Alaungpaya | 6.0 | 13.0 |
| Albert Cossery | 2.0 | 10.0 |
| Albert Duquesne | 2.0 | 3.0 |
| Albert Fennell | 7.0 | 3.0 |
| Albert Lindhagen | 6.0 | 16.0 |
| Albert Rueprecht | 16.0 | 7.0 |
| Albertine disparue | 3.0 | 8.0 |
| Alcyonidiidae | 1.0 | 3.0 |
| Alejandro Goic | 3.0 | 12.0 |
| Alejandro Matas Britos | 1.0 | 5.0 |
| Alejandro Portero Igual | 1.0 | 6.0 |
| Aleksandr Boyarsky | 1.0 | 8.0 |
| Alena Procházková | 1.0 | 9.0 |
| Ales | 63.0 | 15.0 |
| Alexander Fehling | 6.0 | 6.0 |
| Alexander Moissi | 2.0 | 11.0 |
| Alexandra Powers | 6.0 | 6.0 |
| Alexandre Bertrand | 4.0 | 14.0 |
| Alexandre Falguière's grave | 1.0 | 6.0 |
| Alexandre-François Desportes | 1.0 | 8.0 |
| Alfons X | 2.0 | 6.0 |
| Alfonso Cassini | 33.0 | 7.0 |
| Alfonso II d'Este | 2.0 | 8.0 |
| Alfonso XI of Castile | 12.0 | 20.0 |
| Alfred Horatio Belo | 1.0 | 6.0 |
| Alfred Meyer | 1.0 | 29.0 |
| Alfred Zeisler | 14.0 | 9.0 |
| Alfred, Hereditary Prince of Saxe-Coburg and Gotha | 4.0 | 11.0 |
| Alice Pike Barney | 3.0 | 9.0 |
| Alice and Bob | 2.0 | 7.0 |
| All Ceylon Tamil Congress | 3.0 | 3.0 |
| All of Me (Boy Oh Boy) | 1.0 | 4.0 |
| Allaire | 9.0 | 10.0 |
| Allan Kardec's tomb | 1.0 | 9.0 |
| Alligny-en-Morvan | 2.0 | 4.0 |
| Allis-Chalmers | 1.0 | 3.0 |
| Allison Anders | 9.0 | 9.0 |
| Almbach | 1.0 | 4.0 |
| Alpirsbach | 12.0 | 5.0 |
| Alseno | 14.0 | 12.0 |
| Altavilla Irpina | 14.0 | 13.0 |
| Altenkirchen | 27.0 | 14.0 |
| Amable | 3.0 | 8.0 |
| Amanda Walsh | 8.0 | 6.0 |
| Amanojaku | 1.0 | 5.0 |
| Amaryllis | 5.0 | 20.0 |
| Amazing | 14.0 | 45.0 |
| Amazon basin | 2.0 | 12.0 |
| Ameinias of Athens | 4.0 | 7.0 |
| American Idol, season 4 | 2.0 | 4.0 |
| Amiens railway station | 4.0 | 13.0 |
| Ammergau Alps | 1.0 | 3.0 |
| Amongst Women | 1.0 | 4.0 |
| Amor y rock and roll | 2.0 | 5.0 |
| Ampleforth College | 123.0 | 1.0 |
| Amstenrade Castle: brick wall southwest of the gate to the vegetable garden | 1.0 | 6.0 |
| Amulet | 3.0 | 9.0 |
| Anastasia of Serbia | 4.0 | 9.0 |
| Andrea Ahmann | 1.0 | 7.0 |
| Andrea Costantini | 4.0 | 7.0 |
| Andrew Dasburg | 1.0 | 10.0 |
| Andrew Divoff | 24.0 | 7.0 |
| Andrew Wells | 1.0 | 8.0 |
| Andriy Bandera | 4.0 | 9.0 |
| Andronikos II of Trebizond | 4.0 | 7.0 |
| András Fricsay | 3.0 | 6.0 |
| André Forcier | 8.0 | 8.0 |
| André Hazes | 4.0 | 11.0 |
| André-Paul Antoine | 8.0 | 11.0 |
| Andrés Cuevas González | 1.0 | 4.0 |
| Angels & Stars | 3.0 | 8.0 |
| Angels' Story | 1.0 | 4.0 |
| Anima Rossa | 2.0 | 6.0 |
| Animetal Marathon V | 2.0 | 5.0 |
| Anisogammaridae | 8.0 | 3.0 |
| Anita Gillette | 8.0 | 8.0 |
| Anita Laurenzi | 2.0 | 5.0 |
| Ann Rinaldi | 7.0 | 5.0 |
| Annalena | 6.0 | 1.0 |
| Annaram | 1.0 | 4.0 |
| Anne Goursaud | 3.0 | 7.0 |
| Anne of Lorraine, duchess of Aumale | 5.0 | 9.0 |
| Annie Degroote | 2.0 | 6.0 |
| Annie Dufresne | 3.0 | 6.0 |
| Annie Rosar | 31.0 | 8.0 |
| Anodyne | 1.0 | 9.0 |
| Anpyeong station | 1.0 | 4.0 |
| Ans Kremer | 6.0 | 5.0 |
| Anthonie Verstraelen | 1.0 | 8.0 |
| Anthonio | 2.0 | 4.0 |
| Anthony Andrews | 14.0 | 9.0 |
| Anthony I, Count of Ligny | 4.0 | 8.0 |
| Antipater of Tarsus | 3.0 | 6.0 |
| Antoine Balpêtré | 40.0 | 9.0 |
| Antonin Lovrier | 1.0 | 8.0 |
| Antonio Rey González | 1.0 | 6.0 |
| Antrenas | 3.0 | 5.0 |
| Antwerp | 1992.0 | 61.0 |
| António Lobo Antunes | 1.0 | 11.0 |
| Anushka Sharma | 8.0 | 9.0 |
| Anything but Mine | 2.0 | 6.0 |
| Anyue County | 71.0 | 72.0 |
| Anywhere Is | 2.0 | 6.0 |
| Apart | 2.0 | 13.0 |
| Aphrodite Terra quadrangle | 1.0 | 3.0 |
| Apinae | 18.0 | 3.0 |
| Apodi | 2.0 | 3.0 |
| Apphia Yu | 1.0 | 4.0 |
| April Grace | 12.0 | 6.0 |
| Arabella Figg | 1.0 | 5.0 |
| Araglas | 2.0 | 6.0 |
| Aragonese Party | 2.0 | 5.0 |
| Arales | 2.0 | 3.0 |
| Araruama | 5.0 | 3.0 |
| Arava | 1.0 | 5.0 |
| Arbourse | 1.0 | 4.0 |
| Archettes | 2.0 | 4.0 |
| Archibald Primrose, 5th Earl of Rosebery | 2.0 | 13.0 |
| Are Hilstad | 1.0 | 5.0 |
| Argente | 1.0 | 5.0 |
| Argenton | 2.0 | 3.0 |
| Aristobulus of Chalcis | 2.0 | 4.0 |
| Arizona Combat Sports | 6.0 | 1.0 |
| Arkhangelsk Governorate | 7.0 | 4.0 |
| Arlersteeg | 1.0 | 4.0 |
| Arlington, Vermont | 4.0 | 3.0 |
| Arne Mattsson | 10.0 | 8.0 |
| Arnes | 6.0 | 9.0 |
| Arnold Pinnock | 5.0 | 6.0 |
| Arres | 5.0 | 8.0 |
| Art Zoyd | 4.0 | 1.0 |
| Artaxerxes I of Persia | 4.0 | 7.0 |
| Artur Olech | 1.0 | 11.0 |
| Arturo de Córdova | 19.0 | 8.0 |
| Arzacq-Arraziguet | 3.0 | 4.0 |
| As Neves | 1.0 | 4.0 |
| Asakusa | 20.0 | 3.0 |
| Asian Dreamer | 2.0 | 5.0 |
| Asiya bint Muzahim | 1.0 | 4.0 |
| Assigny | 5.0 | 8.0 |
| Assis Brasil | 1.0 | 4.0 |
| Associated Artists Productions | 2.0 | 2.0 |
| Associação Desportiva Bahia de Feira | 2.0 | 4.0 |
| Astronaute | 3.0 | 6.0 |
| Atiqah Hasiholan | 3.0 | 5.0 |
| Auburn High School | 5.0 | 3.0 |
| Auchy-la-Montagne | 1.0 | 4.0 |
| Audiophiles | 2.0 | 9.0 |
| Audouin Dollfus | 1.0 | 7.0 |
| Audun-le-Roman | 8.0 | 13.0 |
| Augustus the Younger, Duke of Brunswick-Lüneburg | 8.0 | 20.0 |
| Austin M. Purves, Jr. | 2.0 | 6.0 |
| Autonomous University of Santo Domingo | 25.0 | 1.0 |
| Aventignan | 2.0 | 5.0 |
| Avex Group | 205.0 | 6.0 |
| Aviron Bayonnais FC | 6.0 | 3.0 |
| Avondance | 2.0 | 4.0 |
| Awala-Yalimapo | 2.0 | 4.0 |
| Axintele | 3.0 | 9.0 |
| Ayacucho Department, San Luis | 1.0 | 3.0 |
| Azalea | 2.0 | 3.0 |
| Azzo | 8.0 | 1.0 |
| Azé | 2.0 | 4.0 |
| BGM | 3.0 | 10.0 |
| Baba Saad | 2.0 | 6.0 |
| Babasónica Electrónica | 2.0 | 3.0 |
| Baby by Me | 2.0 | 9.0 |
| Babylon Squared | 2.0 | 6.0 |
| Bacchus and Ariadne | 4.0 | 103.0 |
| Back for More | 3.0 | 9.0 |
| Bad Feilnbach | 8.0 | 3.0 |
| Bahawalnagar District | 1.0 | 2.0 |
| Baikonur Cosmodrome | 8.0 | 2.0 |
| Bairo | 7.0 | 8.0 |
| Bakit Baligtad Magbasa ng Libro ang mga Pilipino? | 2.0 | 7.0 |
| Balige | 1.0 | 3.0 |
| Ballesteros | 1.0 | 4.0 |
| Bang Nai Si | 1.0 | 3.0 |
| Bangalore | 286.0 | 12.0 |
| Banksy | 3.0 | 13.0 |
| Banlung | 1.0 | 4.0 |
| Banquet | 4.0 | 10.0 |
| Banquet of Squad D of the Crossbow Civic Guards, „The Braspenning Banquet“ | 1.0 | 4.0 |
| Bantega | 2.0 | 4.0 |
| Bar-le-Duc | 77.0 | 9.0 |
| Barbara Adolph | 8.0 | 6.0 |
| Barbara London | 1.0 | 14.0 |
| Barges | 2.0 | 8.0 |
| Barjac | 19.0 | 24.0 |
| Barnt Green | 3.0 | 4.0 |
| Bartolini Salimbeni Annunciation | 1.0 | 10.0 |
| Basiliscus | 1.0 | 7.0 |
| Bastiaan | 21.0 | 1.0 |
| Batmobile | 1.0 | 3.0 |
| Battle of Austerlitz | 13.0 | 3.0 |
| Battle of Five Armies | 8.0 | 11.0 |
| Battle of Singapore | 6.0 | 1.0 |
| Bay of Islands | 3.0 | 4.0 |
| Baños de Tajo | 4.0 | 8.0 |
| Be Ready Boys: Appalachia to Abilene | 2.0 | 5.0 |
| Beata Schimscheiner | 1.0 | 5.0 |
| Beatriz Michelena | 2.0 | 9.0 |
| Beaumont-de-Lomagne | 7.0 | 5.0 |
| Beechcraft Musketeer | 2.0 | 3.0 |
| Beer for My Horses | 2.0 | 20.0 |
| Before I Go to Sleep | 1.0 | 23.0 |
| Beijing Sport University F.C. | 8.0 | 3.0 |
| Belarusian Orthodox Church | 2.0 | 7.0 |
| Belle Plaine | 7.0 | 9.0 |
| Belleisle-class ironclad | 2.0 | 5.0 |
| Belmontet | 1.0 | 4.0 |
| Belvoir Castle | 6.0 | 4.0 |
| Benagéber | 3.0 | 6.0 |
| Benas | 2.0 | 1.0 |
| Benedikt Gollhardt | 1.0 | 5.0 |
| Benedito Leite | 1.0 | 3.0 |
| Benigembla | 6.0 | 9.0 |
| Benito Sagredo | 1.0 | 4.0 |
| Benny Beimer | 5.0 | 15.0 |
| Benson Records | 14.0 | 1.0 |
| Benxi | 11.0 | 11.0 |
| Beorn (DNB00) | 1.0 | 4.0 |
| Beppe Cardile | 1.0 | 7.0 |
| Berg bei Rohrbach | 3.0 | 4.0 |
| Bernadette Paaßen | 2.0 | 5.0 |
| Bernard of Świdnica | 11.0 | 13.0 |
| Bernd Förster | 1.0 | 15.0 |
| Bernward | 16.0 | 1.0 |
| Bertelsmann | 17.0 | 12.0 |
| Berville | 4.0 | 5.0 |
| Bessarabia | 18.0 | 1.0 |
| Bethlehem Sparrows Point Shipyard | 3.0 | 2.0 |
| Beuvillers | 8.0 | 14.0 |
| Bever | 14.0 | 20.0 |
| Beverley Callard | 1.0 | 5.0 |
| Bhim Singh Rana | 1.0 | 5.0 |
| Biebrich, Rhineland Palatinate | 2.0 | 3.0 |
| Big Pokey | 1.0 | 4.0 |
| Bill Bergson Lives Dangerously | 2.0 | 37.0 |
| Bill Chott | 3.0 | 4.0 |
| Bill Mason | 7.0 | 12.0 |
| Bill Williams | 22.0 | 46.0 |
| Billy Breathes | 1.0 | 4.0 |
| Billy Wirth | 7.0 | 6.0 |
| Bilthoven railway station | 2.0 | 6.0 |
| Bingcun | 1.0 | 3.0 |
| Bingen | 5.0 | 6.0 |
| Binnenweg | 23.0 | 35.0 |
| Birmingham Railway Carriage and Wagon Company | 4.0 | 4.0 |
| Bisignano | 19.0 | 15.0 |
| Bize | 4.0 | 9.0 |
| Bjørg Tingstad | 1.0 | 4.0 |
| Black Moses | 1.0 | 4.0 |
| Blandford-Blenheim | 2.0 | 3.0 |
| Blattodea | 8.0 | 4.0 |
| Blazon Stone | 2.0 | 5.0 |
| Blincourt | 1.0 | 4.0 |
| Blood Promise | 2.0 | 14.0 |
| Bloodletting & Miraculous Cures | 1.0 | 5.0 |
| Blue Nile | 2.0 | 9.0 |
| Blue Ribbon | 3.0 | 3.0 |
| Blue Suede Shoes | 1.0 | 5.0 |
| Bluffton | 5.0 | 6.0 |
| Blythewood | 1.0 | 3.0 |
| Blåsut metro station | 2.0 | 8.0 |
| Bmp8a | 2.0 | 5.0 |
| Bob Stephenson | 7.0 | 32.0 |
| Bobby Andrews | 4.0 | 6.0 |
| Bobby Roth | 20.0 | 7.0 |
| Bodil Steensen-Leth | 1.0 | 5.0 |
| Boeing 737 Next Generation | 62.0 | 5.0 |
| Bogy | 6.0 | 12.0 |
| Boldklubben 1913 | 1.0 | 4.0 |
| Bolesławiec | 37.0 | 7.0 |
| Bom Jesus do Norte, Espírito Santo | 1.0 | 3.0 |
| Book of Angels | 1.0 | 3.0 |
| Boris Isaković | 16.0 | 5.0 |
| Borkowski | 9.0 | 2.0 |
| Borough of Manhattan Community College | 1.0 | 4.0 |
| Borrazópolis | 1.0 | 3.0 |
| Borsoniidae | 9.0 | 3.0 |
| Boršov nad Vltavou | 7.0 | 10.0 |
| Botiza | 3.0 | 9.0 |
| Boualem Sansal | 1.0 | 8.0 |
| Boulin | 2.0 | 5.0 |
| Boyd Morgan | 4.0 | 10.0 |
| Bradford Dillman | 38.0 | 8.0 |
| Break a Dawn | 2.0 | 4.0 |
| Brendan James | 5.0 | 12.0 |
| Brian Does Hollywood | 2.0 | 5.0 |
| Brian Freeman | 1.0 | 13.0 |
| Brian Harold Mason | 2.0 | 13.0 |
| Brian Michael Bendis | 1.0 | 8.0 |
| Brian Tyler | 8.0 | 16.0 |
| Brian in Love | 2.0 | 4.0 |
| Britta | 85.0 | 1.0 |
| Brockham | 1.0 | 5.0 |
| Broekland | 1.0 | 4.0 |
| Brothers & Sisters, season 3 | 2.0 | 5.0 |
| Brotton | 1.0 | 2.0 |
| Bruce County | 16.0 | 11.0 |
| Bruce Degen | 3.0 | 4.0 |
| Bruno Hübner | 16.0 | 15.0 |
| Bruno Wolkowitch | 13.0 | 8.0 |
| Bryan Gregory | 1.0 | 6.0 |
| Bud Powell's Moods | 1.0 | 4.0 |
| Bughea de Sus | 3.0 | 9.0 |
| Bumble Bees | 1.0 | 4.0 |
| Burgemeester van Rijnsingel | 3.0 | 4.0 |
| Burning Bridges | 7.0 | 25.0 |
| Bussloo | 1.0 | 3.0 |
| By | 2.0 | 5.0 |
| Byl jednou jeden král… | 1.0 | 7.0 |
| Bárbara Lennie | 2.0 | 6.0 |
| Bélarga | 3.0 | 5.0 |
| Bérault | 2.0 | 7.0 |
| C# | 13.0 | 6.0 |
| C-130R Hercules | 1.0 | 7.0 |
| C-3PO | 2.0 | 7.0 |
| C.F. União de Coimbra | 2.0 | 3.0 |
| CD34 molecule | 1.0 | 32.0 |
| Cabinet Schmidt III | 2.0 | 4.0 |
| Cabral Ibacka | 1.0 | 6.0 |
| Cadaqués | 10.0 | 7.0 |
| Cajamarca | 28.0 | 30.0 |
| Calanca | 15.0 | 22.0 |
| Calatorao | 1.0 | 5.0 |
| Caldana | 2.0 | 4.0 |
| Californium | 21.0 | 9.0 |
| Caminha | 4.0 | 4.0 |
| Camminghastraat | 6.0 | 4.0 |
| Campeonato Sul-Mato-Grossense | 1.0 | 3.0 |
| Campo Marzio | 1.0 | 4.0 |
| Can Can/Promise You | 2.0 | 4.0 |
| Can It Be All So Simple | 2.0 | 5.0 |
| Canadian County | 5.0 | 5.0 |
| Capayán Department | 1.0 | 3.0 |
| Cappel | 6.0 | 12.0 |
| Cappelle sul Tavo | 7.0 | 10.0 |
| Capurso | 15.0 | 11.0 |
| Capvern | 3.0 | 5.0 |
| Carabaya Province | 1.0 | 3.0 |
| Caracal | 7.0 | 13.0 |
| Carbonia | 32.0 | 17.0 |
| Carbost | 1.0 | 6.0 |
| Carita Holmström | 1.0 | 9.0 |
| Carl Craig | 2.0 | 9.0 |
| Carl Spitzweg | 7.0 | 10.0 |
| Carl-Herbert Dieden | 2.0 | 5.0 |
| Carla Bartheel | 1.0 | 8.0 |
| Carmen Franco, 1st Duchess of Franco | 6.0 | 12.0 |
| Caroline Munro | 14.0 | 8.0 |
| Carsten Sieling | 1.0 | 11.0 |
| Casalabriva | 2.0 | 3.0 |
| Castaneda | 7.0 | 11.0 |
| Castellcir | 9.0 | 12.0 |
| Castelldefels railway station | 2.0 | 4.0 |
| Castello d'Agogna | 6.0 | 9.0 |
| Castelnau-de-Montmiral | 3.0 | 4.0 |
| Castelnuovo di Val di Cecina | 13.0 | 10.0 |
| Castelu | 3.0 | 9.0 |
| Castle Rising | 1.0 | 5.0 |
| Category:2010s in the United Kingdom | 10.0 | 3.0 |
| Category:April 29, 2010 | 2.0 | 5.0 |
| Category:August 26, 2008 | 2.0 | 5.0 |
| Category:British Islands | 1.0 | 3.0 |
| Category:Brown algae | 1.0 | 2.0 |
| Category:Deaths in Bentivoglio | 1.0 | 4.0 |
| Category:Deaths in Borgo Tossignano | 1.0 | 4.0 |
| Category:Deaths in Cantù | 1.0 | 4.0 |
| Category:Deaths in Carcare | 1.0 | 4.0 |
| Category:Deaths in Castel Ritaldi | 1.0 | 4.0 |
| Category:Deaths in Chiari, Lombardy | 1.0 | 4.0 |
| Category:Deaths in Clusone | 1.0 | 4.0 |
| Category:Deaths in Coeur d'Alene | 1.0 | 4.0 |
| Category:Deaths in Dießen am Ammersee | 1.0 | 4.0 |
| Category:Deaths in Don Benito | 1.0 | 4.0 |
| Category:Deaths in Douai | 1.0 | 4.0 |
| Category:Deaths in Framura | 1.0 | 4.0 |
| Category:Deaths in Gabrovo | 1.0 | 4.0 |
| Category:Deaths in Garlasco | 1.0 | 4.0 |
| Category:Deaths in Governorate of Livonia | 1.0 | 4.0 |
| Category:Deaths in Kirkkonummi | 1.0 | 4.0 |
| Category:Deaths in Ksar el-Kebir | 1.0 | 4.0 |
| Category:Deaths in Kyzylorda Province | 1.0 | 4.0 |
| Category:Deaths in Königs Wusterhausen | 1.0 | 4.0 |
| Category:Deaths in Lake Havasu City | 1.0 | 4.0 |
| Category:Deaths in Lorenzago di Cadore | 1.0 | 4.0 |
| Category:Deaths in Lyons-la-Forêt | 1.0 | 4.0 |
| Category:Deaths in Manchester | 1.0 | 4.0 |
| Category:Deaths in Mukacheve Raion | 1.0 | 4.0 |
| Category:Deaths in Nanping | 1.0 | 4.0 |
| Category:Deaths in Oristano | 1.0 | 4.0 |
| Category:Deaths in Rolampont | 1.0 | 4.0 |
| Category:Deaths in Sondika | 1.0 | 4.0 |
| Category:Deaths in Struga | 1.0 | 4.0 |
| Category:Deaths in Toano | 1.0 | 4.0 |
| Category:Deaths in Vitoria-Gasteiz | 1.0 | 4.0 |
| Category:February 16, 2008 | 2.0 | 5.0 |
| Category:February 9, 2015 | 2.0 | 5.0 |
| Category:Fictional mammals | 2.0 | 2.0 |
| Category:Films set in Lebanon | 1.0 | 4.0 |
| Category:Films set in Marseille | 1.0 | 4.0 |
| Category:Films shot in Bahrain | 2.0 | 5.0 |
| Category:Films shot in Melun | 1.0 | 4.0 |
| Category:Films shot in Philadelphia | 2.0 | 5.0 |
| Category:Films shot in Potenza | 1.0 | 4.0 |
| Category:Films shot in Rio Grande do Sul | 1.0 | 4.0 |
| Category:Films shot in San Diego | 2.0 | 5.0 |
| Category:Films shot in South Dakota | 2.0 | 5.0 |
| Category:Films shot in Trentino-South Tyrol | 1.0 | 4.0 |
| Category:Jordanian people | 1.0 | 4.0 |
| Category:July 30, 2008 | 2.0 | 5.0 |
| Category:June 29, 2010 | 2.0 | 5.0 |
| Category:March 16, 2011 | 2.0 | 5.0 |
| Category:March 28, 2006 | 2.0 | 5.0 |
| Category:May 10, 2005 | 2.0 | 5.0 |
| Category:October 18, 2005 | 2.0 | 5.0 |
| Category:People from Michalovce | 1.0 | 4.0 |
| Category:People from Sigulda | 1.0 | 4.0 |
| Category:September 20, 2010 | 2.0 | 5.0 |
| Category:Two and a Half Men characters | 1.0 | 4.0 |
| Catherine Sutherland | 2.0 | 6.0 |
| Catshuis | 2.0 | 7.0 |
| Cattle in a Meadow | 1.0 | 4.0 |
| Caucasus Mountains | 9.0 | 6.0 |
| Caught You | 2.0 | 5.0 |
| Cayo Lara | 1.0 | 9.0 |
| Cazevieille | 2.0 | 5.0 |
| Cedrasco | 6.0 | 9.0 |
| Ceillac | 9.0 | 11.0 |
| Celeste Cid | 1.0 | 6.0 |
| Central Bible College | 1.0 | 2.0 |
| Cergy-Pontoise University | 1.0 | 3.0 |
| Certosa di Pavia railway station | 1.0 | 5.0 |
| Cestona | 4.0 | 5.0 |
| Ceyssac | 1.0 | 4.0 |
| Ceyzérieu | 12.0 | 13.0 |
| Chajiao Subdistrict, Guangzhou | 1.0 | 3.0 |
| Chaltyr | 2.0 | 4.0 |
| Chambolle-Musigny | 3.0 | 6.0 |
| Chameyrat | 8.0 | 10.0 |
| Chandi | 2.0 | 2.0 |
| Chandni | 3.0 | 18.0 |
| Chandra Wilson | 5.0 | 8.0 |
| Changdong Town | 1.0 | 3.0 |
| Changli | 1.0 | 3.0 |
| Changli County | 17.0 | 19.0 |
| Chantraines | 2.0 | 5.0 |
| Chapelle-Royale | 1.0 | 4.0 |
| Charles Berkeley, 2nd Earl of Berkeley | 7.0 | 17.0 |
| Charles Planat | 1.0 | 11.0 |
| Charles Wellford Leavitt | 2.0 | 7.0 |
| Charles William, Duke of Saxe-Meiningen | 4.0 | 15.0 |
| Charles, Prince of Rochefort | 3.0 | 5.0 |
| Charlevoix-Est Regional County Municipality | 8.0 | 8.0 |
| Charlotte Chaffanjon | 1.0 | 10.0 |
| Charlotte Desmares | 1.0 | 11.0 |
| Charnay | 6.0 | 8.0 |
| Chauvigny | 8.0 | 8.0 |
| Chauvincourt-Provemont | 2.0 | 4.0 |
| Cheech & Chong | 11.0 | 5.0 |
| Chelsea Girl | 2.0 | 7.0 |
| Chemin de Fer du Blanc-Argent | 5.0 | 3.0 |
| Chen Yannian | 1.0 | 5.0 |
| Chesnois-Auboncourt | 7.0 | 11.0 |
| Chicago VIII | 2.0 | 5.0 |
| Chikuhei Nakajima | 1.0 | 5.0 |
| Chimney's Afire | 1.0 | 4.0 |
| Chintila | 1.0 | 6.0 |
| Chiry-Ourscamp | 3.0 | 4.0 |
| Chitonina | 3.0 | 4.0 |
| Chloranthaceae | 4.0 | 6.0 |
| Chlothar I | 20.0 | 26.0 |
| Chorzów Batory | 6.0 | 3.0 |
| Chouain | 6.0 | 9.0 |
| Chris Ofili | 1.0 | 10.0 |
| Chris Petersen | 2.0 | 17.0 |
| Chris Thomas | 4.0 | 30.0 |
| Christ Church Nichola Town Parish | 1.0 | 3.0 |
| Christiaen Jansz van Bieselingen | 1.0 | 7.0 |
| Christian Décamps | 1.0 | 8.0 |
| Christian Erickson | 2.0 | 6.0 |
| Christian Lorenz | 1.0 | 8.0 |
| Christian Pikes | 2.0 | 5.0 |
| Christian Schramm | 1.0 | 7.0 |
| Christian Stolte | 5.0 | 6.0 |
| Christine Carère | 16.0 | 7.0 |
| Christine Haas | 2.0 | 5.0 |
| Christoph Ahlhaus | 2.0 | 10.0 |
| Christoph Schönborn | 1.0 | 18.0 |
| Christoph Zrenner | 1.0 | 5.0 |
| Christopher Cornford | 4.0 | 6.0 |
| Christopher Hewett | 4.0 | 8.0 |
| Christopher Monger | 3.0 | 7.0 |
| Chromatics | 3.0 | 2.0 |
| Chrzanów County | 6.0 | 9.0 |
| Chuck Versus the Role Models | 2.0 | 5.0 |
| Church Minshull | 1.0 | 5.0 |
| Château d'Haroué | 2.0 | 6.0 |
| Château de Monte-Cristo | 1.0 | 7.0 |
| Château de Passy-les-Tours | 1.0 | 4.0 |
| Châteaubourg | 10.0 | 12.0 |
| Châteauneuf-Miravail | 8.0 | 11.0 |
| Châteauneuf-Val-de-Bargis | 3.0 | 5.0 |
| Ciego de Ávila | 4.0 | 2.0 |
| Cikarang railway station | 2.0 | 5.0 |
| Cikudapateuh railway station | 2.0 | 4.0 |
| Cinema Bizarre | 6.0 | 1.0 |
| Cinzia De Carolis | 10.0 | 7.0 |
| Cirrus | 2.0 | 5.0 |
| Cis, Trentino | 5.0 | 8.0 |
| City College of New York | 475.0 | 5.0 |
| City of Cockburn | 4.0 | 6.0 |
| City of Ljubuški | 1.0 | 2.0 |
| City of Matlosana | 3.0 | 4.0 |
| City of Zavidovići | 2.0 | 3.0 |
| Clansayes | 1.0 | 3.0 |
| Clap Yo Hands | 2.0 | 5.0 |
| Claude Lamoral, 3rd Prince of Ligne | 3.0 | 11.0 |
| Claude Santelli | 1.0 | 7.0 |
| Claude-Jean Philippe | 3.0 | 9.0 |
| Claudio Pizarro | 1.0 | 12.0 |
| Claus Friedrich von Reden | 1.0 | 7.0 |
| Claville | 2.0 | 4.0 |
| Clement Hurd | 4.0 | 6.0 |
| Clockwork | 1.0 | 12.0 |
| Clown Prince | 1.0 | 4.0 |
| Club Juan Aurich | 38.0 | 5.0 |
| Clémence Bretécher | 2.0 | 6.0 |
| Coccinellidae | 36.0 | 3.0 |
| Coccotremataceae | 1.0 | 3.0 |
| Cocullo | 8.0 | 11.0 |
| Codogno | 61.0 | 15.0 |
| Cogullada | 1.0 | 3.0 |
| Colorado College | 17.0 | 3.0 |
| Colubridae | 22.0 | 4.0 |
| Commander of the Order of Leopold II | 52.0 | 1.0 |
| Comrat | 10.0 | 5.0 |
| Concertación | 1.0 | 3.0 |
| Condette | 2.0 | 4.0 |
| Condoto | 2.0 | 3.0 |
| Conrad II, Count of Oldenburg | 3.0 | 8.0 |
| Consort Qi | 5.0 | 9.0 |
| Conspiritus | 1.0 | 3.0 |
| Constantin Melnik | 1.0 | 10.0 |
| Conti di Ceccano | 4.0 | 2.0 |
| Corbola | 9.0 | 9.0 |
| Corbère | 7.0 | 11.0 |
| Cornelia Stuyvesant Vanderbilt | 4.0 | 8.0 |
| Corrado Guarducci | 21.0 | 6.0 |
| Corre | 1.0 | 5.0 |
| Corvera de Toranzo | 1.0 | 4.0 |
| Cory Monteith | 4.0 | 11.0 |
| Cosmere | 6.0 | 2.0 |
| Countess Claudine Rhédey von Kis-Rhéde | 2.0 | 8.0 |
| Countess Ermesinde II, Countess of Luxembourg | 6.0 | 9.0 |
| Craig Pearce | 4.0 | 6.0 |
| Criminal Minds, season 4 | 2.0 | 5.0 |
| Crimson and Clover | 2.0 | 5.0 |
| Criquetot-sur-Ouville | 2.0 | 3.0 |
| Crisis | 7.0 | 59.0 |
| Crocefieschi | 7.0 | 9.0 |
| Cross Over | 2.0 | 12.0 |
| Crusade | 6.0 | 34.0 |
| Cybernetic Dreams of Pi | 1.0 | 3.0 |
| Cyrillaceae | 1.0 | 5.0 |
| César Herráiz Pujol | 1.0 | 5.0 |
| Cézan | 1.0 | 4.0 |
| Côtière | 1.0 | 1.0 |
| D.S. | 1.0 | 5.0 |
| DNA repair-deficiency disorder | 1.0 | 1.0 |
| Da Nang | 8.0 | 6.0 |
| Dactylopodida | 2.0 | 3.0 |
| Daisy Campbell | 2.0 | 4.0 |
| Dalbe Station | 2.0 | 4.0 |
| Dan Le Sac | 1.0 | 4.0 |
| Daniel Conley | 2.0 | 8.0 |
| Daniel Day-Lewis | 33.0 | 18.0 |
| Daniel Isăilă | 1.0 | 6.0 |
| Daniel Lupi | 6.0 | 3.0 |
| Dans un autre monde | 2.0 | 7.0 |
| Dantivarman | 2.0 | 5.0 |
| Daphné Roulier | 2.0 | 5.0 |
| Dario D'Ambrosio | 1.0 | 7.0 |
| Darren Jeffries | 1.0 | 5.0 |
| Dashboard Confessional | 8.0 | 2.0 |
| Date Muratomi | 2.0 | 5.0 |
| Dava Sobel | 1.0 | 11.0 |
| Dave Brown | 2.0 | 112.0 |
| David Mills | 3.0 | 55.0 |
| David Valcin | 1.0 | 4.0 |
| Davyd Sviatoslavich | 5.0 | 7.0 |
| De Gregori | 2.0 | 5.0 |
| De Mi Puño y Letra | 1.0 | 4.0 |
| DeRuyter | 2.0 | 3.0 |
| Dean Edwards | 1.0 | 29.0 |
| Dear Miss Lonelyhearts | 1.0 | 6.0 |
| Decade of Decadence | 2.0 | 3.0 |
| Decas | 2.0 | 4.0 |
| Deeper, Deeper, Deeper Still | 2.0 | 6.0 |
| Delia Fiallo | 2.0 | 5.0 |
| Delmark Records | 8.0 | 3.0 |
| Denis Lazure | 1.0 | 12.0 |
| Denise Clair | 5.0 | 6.0 |
| Der Tunnel | 2.0 | 3.0 |
| Derbidae | 98.0 | 3.0 |
| Derrick O'Connor | 14.0 | 6.0 |
| Destination Berlin | 2.0 | 4.0 |
| Devrim Evin | 1.0 | 5.0 |
| Diana Hardcastle | 1.0 | 4.0 |
| Dianne Buckner | 1.0 | 5.0 |
| Dihydrofolate reductase | 1.0 | 10.0 |
| Dilys Laye | 2.0 | 6.0 |
| Dimitrios Vranopoulos | 1.0 | 9.0 |
| Dimítris Kókkinos | 1.0 | 4.0 |
| Diocese of Haderslev | 4.0 | 2.0 |
| Dirk Oldenburg | 1.0 | 8.0 |
| Disraeli | 1.0 | 34.0 |
| Dissing+Weitling | 1.0 | 5.0 |
| District of Alaska | 5.0 | 4.0 |
| Dmitry Vasilyevich | 1.0 | 4.0 |
| Do It | 4.0 | 8.0 |
| Dobrinsky District | 2.0 | 3.0 |
| Doctor P | 1.0 | 5.0 |
| Dodești | 3.0 | 9.0 |
| Dogville | 1.0 | 36.0 |
We can note that most nodes have quite low in- and out-degrees, but a few nodes stand out. Some nodes have up to 900 out-degree and some over 1 million in-degree! This high in-degree is expected, as these nodes mainly correspond to descriptions of other entities. We can verify that the nodes with highest in-degrees do themselves not have significantly high out-degrees.
display(degrees.sort($"inDegree".desc))
| id | inDegree | outDegree |
|---|---|---|
| human | 1521012.0 | 20.0 |
| male | 1277099.0 | 6.0 |
| United States of America | 281936.0 | 174.0 |
| female | 235329.0 | 6.0 |
| politician | 223963.0 | 3.0 |
| Germany | 205497.0 | 272.0 |
| France | 178816.0 | 132.0 |
| association football player | 129414.0 | 5.0 |
| Netherlands | 115585.0 | 98.0 |
| taxon | 114543.0 | 3.0 |
| actor | 110153.0 | 3.0 |
| Italy | 99514.0 | 226.0 |
| genus | 99476.0 | 2.0 |
| United Kingdom | 82023.0 | 73.0 |
| film | 75652.0 | 3.0 |
| English | 55215.0 | 10.0 |
| Rijksmonument | 54183.0 | 2.0 |
| People's Republic of China | 52849.0 | 106.0 |
| album | 52191.0 | 4.0 |
| writer | 51369.0 | 4.0 |
| author | 50638.0 | 2.0 |
| journalist | 44990.0 | 3.0 |
| Canada | 43389.0 | 152.0 |
| painter | 42688.0 | 4.0 |
| Russia | 42317.0 | 210.0 |
| French | 41775.0 | 12.0 |
| asteroid | 41446.0 | 2.0 |
| single | 39820.0 | 4.0 |
| Paris | 39791.0 | 255.0 |
| asteroid belt | 39051.0 | 2.0 |
| Sweden | 37293.0 | 113.0 |
| association football | 36644.0 | 4.0 |
| singer | 36142.0 | 3.0 |
| composer | 36132.0 | 5.0 |
| Spain | 32922.0 | 72.0 |
| Japan | 32825.0 | 91.0 |
| sportsperson | 32713.0 | 4.0 |
| commune of France | 32480.0 | 7.0 |
| Poland | 30725.0 | 90.0 |
| Soviet Union | 30483.0 | 52.0 |
| Austria | 30315.0 | 73.0 |
| Australia | 29186.0 | 167.0 |
| Norway | 28945.0 | 86.0 |
| Switzerland | 28801.0 | 72.0 |
| John | 26314.0 | 68.0 |
| painting | 25980.0 | 6.0 |
| UTC+01:00 | 25664.0 | 1.0 |
| lawyer | 23193.0 | 5.0 |
| Priest | 22874.0 | 39.0 |
| Book | 22553.0 | 6.0 |
| baseball player | 22223.0 | 4.0 |
| Brazil | 21923.0 | 231.0 |
| Berlin | 21839.0 | 121.0 |
| Democratic Party | 21099.0 | 94.0 |
| film director | 20997.0 | 5.0 |
| Moscow | 20802.0 | 113.0 |
| Belgium | 20442.0 | 70.0 |
| India | 20407.0 | 92.0 |
| oil paint | 19848.0 | 3.0 |
| township in China | 19561.0 | 2.0 |
| historian | 19522.0 | 4.0 |
| street | 19369.0 | 2.0 |
| ice hockey player | 18802.0 | 4.0 |
| musician | 18760.0 | 3.0 |
| video game | 18637.0 | 6.0 |
| Finland | 18554.0 | 65.0 |
| poet | 18284.0 | 3.0 |
| New York City | 18242.0 | 79.0 |
| Republican Party | 18053.0 | 22.0 |
| Architect | 18023.0 | 7.0 |
| Rome | 17014.0 | 90.0 |
| Argentina | 16694.0 | 75.0 |
| Catholicism | 16466.0 | 1.0 |
| engineer | 16224.0 | 3.0 |
| diplomat | 16221.0 | 4.0 |
| World War I | 16198.0 | 10.0 |
| Order of Lenin | 15971.0 | 3.0 |
| screenwriter | 15462.0 | 4.0 |
| World War II | 15347.0 | 3.0 |
| single-player video game | 15224.0 | 1.0 |
| basketball player | 15173.0 | 5.0 |
| Wikimedia category | 15121.0 | 13.0 |
| Czech Republic | 15046.0 | 86.0 |
| Canvas | 14968.0 | 14.0 |
| Vienna | 14665.0 | 91.0 |
| Q14371254 | 14538.0 | 13.0 |
| Harvard University | 14468.0 | 5.0 |
| member of the French National Assembly | 13798.0 | 4.0 |
| London | 13631.0 | 105.0 |
| Lincoln Near-Earth Asteroid Research | 12993.0 | 3.0 |
| MIT Lincoln Laboratory | 12986.0 | 2.0 |
| Robert | 12613.0 | 59.0 |
| Road | 12489.0 | 9.0 |
| township of the People's Republic of China | 12486.0 | 4.0 |
| Romania | 12135.0 | 85.0 |
| village | 11973.0 | 3.0 |
| railway station | 11905.0 | 5.0 |
| Iran | 11900.0 | 67.0 |
| sculptor | 11643.0 | 3.0 |
| Mexico | 11600.0 | 104.0 |
| judge | 11367.0 | 5.0 |
| Amsterdam | 11290.0 | 147.0 |
| James | 11078.0 | 24.0 |
| David | 11047.0 | 179.0 |
| Charles | 11009.0 | 42.0 |
| gridiron football player | 10941.0 | 4.0 |
| United States representative | 10898.0 | 5.0 |
| Greece | 10847.0 | 70.0 |
| military officer | 10736.0 | 3.0 |
| New Zealand | 10681.0 | 56.0 |
| mayor | 10641.0 | 6.0 |
| Spanish | 10640.0 | 11.0 |
| theologian | 10595.0 | 3.0 |
| Rijksmuseum | 10589.0 | 18.0 |
| Paul | 10558.0 | 107.0 |
| Q18002322 | 10335.0 | 16.0 |
| Peter | 10234.0 | 62.0 |
| house | 10221.0 | 2.0 |
| Denmark | 10182.0 | 126.0 |
| British Army | 10163.0 | 6.0 |
| city | 10146.0 | 7.0 |
| Hero of the Soviet Union | 10080.0 | 4.0 |
| natural number | 10003.0 | 2.0 |
| novelist | 9961.0 | 3.0 |
| Munich | 9815.0 | 177.0 |
| silent film | 9733.0 | 1.0 |
| novel | 9701.0 | 3.0 |
| Member of Parliament in the United Kingdom | 9671.0 | 4.0 |
| German | 9652.0 | 17.0 |
| George | 9643.0 | 88.0 |
| guitar | 9614.0 | 2.0 |
| Nordisk familjebok | 9578.0 | 2.0 |
| church | 9524.0 | 14.0 |
| doctorate | 9405.0 | 1.0 |
| conductor | 9361.0 | 4.0 |
| Russian Empire | 9317.0 | 10.0 |
| 2012 Summer Olympics | 9246.0 | 17.0 |
| 2008 Summer Olympics | 9125.0 | 9.0 |
| Michael | 9122.0 | 102.0 |
| Saint Petersburg | 9117.0 | 121.0 |
| building | 9103.0 | 1.0 |
| J-pop | 9067.0 | 1.0 |
| physician | 9015.0 | 3.0 |
| mathematician | 8993.0 | 3.0 |
| economist | 8948.0 | 7.0 |
| Los Angeles | 8893.0 | 61.0 |
| short film | 8871.0 | 3.0 |
| Social Democratic Party of Germany | 8833.0 | 26.0 |
| jurist | 8832.0 | 3.0 |
| bishop | 8672.0 | 7.0 |
| photographer | 8583.0 | 4.0 |
| Order of the Red Star | 8386.0 | 4.0 |
| municipality of Germany | 8385.0 | 4.0 |
| Hungary | 8352.0 | 61.0 |
| 8320.0 | 16.0 | |
| drama film | 8226.0 | 3.0 |
| cricketer | 8220.0 | 4.0 |
| Milan | 8132.0 | 113.0 |
| comune of Italy | 8101.0 | 5.0 |
| Richard | 8080.0 | 31.0 |
| rugby union player | 8021.0 | 4.0 |
| place of death | 8021.0 | 3.0 |
| Film producer | 8016.0 | 3.0 |
| philosopher | 7964.0 | 4.0 |
| 2004 Summer Olympics | 7756.0 | 8.0 |
| jazz | 7744.0 | 1.0 |
| Ukraine | 7707.0 | 71.0 |
| singer-songwriter | 7698.0 | 3.0 |
| translator | 7606.0 | 5.0 |
| Hans | 7595.0 | 42.0 |
| episode | 7569.0 | 4.0 |
| Joseph | 7542.0 | 91.0 |
| physicist | 7534.0 | 3.0 |
| Hamburg | 7451.0 | 64.0 |
| Order of the Patriotic War 1st class | 7424.0 | 2.0 |
| Nazi Party | 7401.0 | 13.0 |
| family | 7401.0 | 7.0 |
| South Africa | 7385.0 | 57.0 |
| association football club | 7383.0 | 5.0 |
| Turkey | 7195.0 | 123.0 |
| sport cyclist | 7100.0 | 3.0 |
| entrepreneur | 7096.0 | 4.0 |
| Q12808966 | 7071.0 | 2.0 |
| Karl | 7070.0 | 11.0 |
| Indonesia | 7034.0 | 74.0 |
| Jean | 7007.0 | 49.0 |
| Order of the Red Banner | 6902.0 | 3.0 |
| goalkeeper | 6884.0 | 22.0 |
| Wikimedia list article | 6847.0 | 2.0 |
| multiplayer game | 6807.0 | 1.0 |
| Musée d'Orsay | 6776.0 | 12.0 |
| Artist | 6624.0 | 5.0 |
| 2000 Summer Olympics | 6618.0 | 10.0 |
| Italian | 6612.0 | 6.0 |
| human settlement | 6605.0 | 1.0 |
| Johann | 6586.0 | 25.0 |
| England | 6583.0 | 20.0 |
| Christian Democratic Union | 6485.0 | 36.0 |
| species | 6475.0 | 3.0 |
| opera singer | 6405.0 | 3.0 |
| mayor of a place in France | 6351.0 | 4.0 |
| Portugal | 6316.0 | 55.0 |
| television presenter | 6281.0 | 4.0 |
| South Korea | 6248.0 | 51.0 |
| municipality of the Czech Republic | 6195.0 | 6.0 |
| linguist | 6186.0 | 3.0 |
| music educator | 6183.0 | 3.0 |
| East Germany | 6156.0 | 45.0 |
| José | 6154.0 | 12.0 |
| Microsoft Windows | 6129.0 | 12.0 |
| Australian rules football player | 6054.0 | 5.0 |
| Guggenheim Fellowship | 5980.0 | 2.0 |
| Q13564349 | 5956.0 | 16.0 |
| Royal Society | 5867.0 | 2.0 |
| botanist | 5862.0 | 3.0 |
| chemist | 5823.0 | 3.0 |
| Henry | 5810.0 | 47.0 |
| Tokyo | 5766.0 | 110.0 |
| 1996 Summer Olympics | 5710.0 | 8.0 |
| Prague | 5585.0 | 183.0 |
| Pierre | 5561.0 | 23.0 |
| rural settlement of Russia | 5543.0 | 3.0 |
| tennis player | 5489.0 | 4.0 |
| Chile | 5405.0 | 41.0 |
| Louis | 5393.0 | 25.0 |
| musical group | 5391.0 | 2.0 |
| United States Navy | 5383.0 | 6.0 |
| Knight of the Legion of Honour | 5375.0 | 4.0 |
| pianist | 5242.0 | 3.0 |
| Daniel | 5200.0 | 66.0 |
| myocardial infarction | 5158.0 | 3.0 |
| Walter | 5125.0 | 19.0 |
| educationist | 5096.0 | 3.0 |
| University of Michigan | 5084.0 | 3.0 |
| musicologist | 5056.0 | 3.0 |
| Nicholas | 5055.0 | 36.0 |
| even number | 5000.0 | 2.0 |
| odd number | 5000.0 | 2.0 |
| river | 4971.0 | 3.0 |
| archaeologist | 4961.0 | 6.0 |
| Yale University | 4921.0 | 4.0 |
| Carl | 4906.0 | 11.0 |
| Naples | 4899.0 | 61.0 |
| anthropologist | 4895.0 | 3.0 |
| Leipzig | 4889.0 | 32.0 |
| Senator of the French Fifth Republic | 4862.0 | 5.0 |
| Frank | 4854.0 | 50.0 |
| Florence | 4854.0 | 100.0 |
| company | 4829.0 | 7.0 |
| portrait | 4825.0 | 5.0 |
| seiyū | 4823.0 | 2.0 |
| Budapest | 4823.0 | 78.0 |
| Vladimir | 4814.0 | 27.0 |
| Alexander | 4804.0 | 134.0 |
| municipality of Spain | 4800.0 | 7.0 |
| Chicago | 4779.0 | 125.0 |
| town | 4745.0 | 5.0 |
| rugby league player | 4740.0 | 4.0 |
| Ireland | 4736.0 | 76.0 |
| Fellow of the Royal Society | 4722.0 | 2.0 |
| 1992 Summer Olympics | 4694.0 | 8.0 |
| Edward | 4691.0 | 21.0 |
| Metropolitan Museum of Art | 4689.0 | 6.0 |
| Dresden | 4676.0 | 66.0 |
| Smith | 4630.0 | 26.0 |
| art historian | 4621.0 | 3.0 |
| voice actor | 4595.0 | 3.0 |
| athletics | 4575.0 | 3.0 |
| Martin | 4540.0 | 60.0 |
| teacher | 4530.0 | 7.0 |
| male given name | 4520.0 | 3.0 |
| landscape art | 4496.0 | 4.0 |
| soldier | 4494.0 | 18.0 |
| television series season | 4454.0 | 3.0 |
| Model | 4422.0 | 3.0 |
| Friedrich | 4408.0 | 8.0 |
| Central European Time | 4405.0 | 2.0 |
| Palomar Observatory | 4395.0 | 3.0 |
| Columbia University | 4366.0 | 6.0 |
| Japanese | 4353.0 | 6.0 |
| Israel | 4309.0 | 100.0 |
| Franz | 4300.0 | 38.0 |
| Christian | 4299.0 | 52.0 |
| Albert | 4288.0 | 32.0 |
| Ivan | 4243.0 | 24.0 |
| pop music | 4243.0 | 2.0 |
| Member of Parliament in the Parliament of England | 4239.0 | 4.0 |
| songwriter | 4215.0 | 3.0 |
| Wilhelm | 4202.0 | 11.0 |
| rock music | 4200.0 | 3.0 |
| monument historique classé | 4167.0 | 2.0 |
| monotypic taxon | 4157.0 | 2.0 |
| Cologne | 4112.0 | 58.0 |
| Officer's Cross of the Order of Merit of the Federal Republic of Germany | 4105.0 | 2.0 |
| Thailand | 4096.0 | 102.0 |
| archbishop | 4090.0 | 3.0 |
| Member of the European Parliament (MEP) | 4079.0 | 3.0 |
| astronomer | 4076.0 | 3.0 |
| member of the House of Commons of Canada | 4069.0 | 5.0 |
| kecamatan | 4060.0 | 4.0 |
| canton of France (until 2015) | 4026.0 | 5.0 |
| publisher | 4006.0 | 6.0 |
| monument historique inscrit | 3997.0 | 2.0 |
| Stuttgart | 3974.0 | 64.0 |
| 1988 Summer Olympics | 3947.0 | 12.0 |
| Juan | 3943.0 | 13.0 |
| Antonio | 3943.0 | 12.0 |
| Philippines | 3932.0 | 47.0 |
| Montreal | 3928.0 | 66.0 |
| film actor | 3923.0 | 3.0 |
| Mark | 3917.0 | 26.0 |
| Q13365117 | 3916.0 | 4.0 |
| Frankfurt am Main | 3914.0 | 74.0 |
| Alfred | 3913.0 | 11.0 |
| playwright | 3903.0 | 3.0 |
| Arthur | 3899.0 | 93.0 |
| Cross of the Order of Merit of the Federal Republic of Germany | 3874.0 | 2.0 |
| Heinrich | 3851.0 | 25.0 |
| Q13382286 | 3823.0 | 4.0 |
| Conservative Party | 3811.0 | 30.0 |
| Moscow State University | 3800.0 | 52.0 |
| fencer | 3793.0 | 3.0 |
| member of the German Bundestag | 3792.0 | 4.0 |
| Order of the Patriotic War 2nd class | 3788.0 | 2.0 |
| Turin | 3738.0 | 56.0 |
| Giovanni | 3736.0 | 44.0 |
| comedy film | 3725.0 | 4.0 |
| television program | 3717.0 | 3.0 |
| Georg | 3713.0 | 29.0 |
| Svensk uppslagsbok | 3698.0 | 3.0 |
| Maria | 3690.0 | 96.0 |
| wood | 3679.0 | 4.0 |
| University of California, Berkeley | 3677.0 | 3.0 |
| Venice | 3672.0 | 122.0 |
| Josef | 3659.0 | 14.0 |
| Metro station | 3654.0 | 2.0 |
| University of Tokyo | 3631.0 | 2.0 |
| Islam | 3619.0 | 2.0 |
| Otto | 3611.0 | 25.0 |
| Buenos Aires | 3600.0 | 133.0 |
| farmhouse | 3594.0 | 7.0 |
| Madrid | 3594.0 | 113.0 |
| cultural property | 3573.0 | 1.0 |
| Princeton University | 3573.0 | 6.0 |
| Hindi | 3549.0 | 7.0 |
| horror film | 3537.0 | 3.0 |
| television series | 3534.0 | 3.0 |
| explorer | 3525.0 | 3.0 |
| Columbia Records | 3457.0 | 5.0 |
| song | 3454.0 | 1.0 |
| basketball coach | 3447.0 | 3.0 |
| Labour Party | 3446.0 | 44.0 |
| sculpture | 3444.0 | 2.0 |
| Mary | 3424.0 | 120.0 |
| Commander of the Order of the British Empire | 3415.0 | 6.0 |
| Warsaw | 3410.0 | 69.0 |
| Opera | 3381.0 | 53.0 |
| woman | 3374.0 | 4.0 |
| Jacques | 3358.0 | 18.0 |
| Ernst | 3334.0 | 17.0 |
| civil parish | 3315.0 | 4.0 |
| Hero of Socialist Labour | 3310.0 | 3.0 |
| Washington, D.C. | 3299.0 | 47.0 |
| subdivision of Russia | 3292.0 | 10.0 |
| Zürich | 3275.0 | 137.0 |
| Croix de guerre 1914–1918 | 3274.0 | 4.0 |
| Hermann | 3263.0 | 15.0 |
| Tom | 3263.0 | 12.0 |
| Mike | 3261.0 | 28.0 |
| Bill | 3243.0 | 12.0 |
| athletics competitor | 3238.0 | 4.0 |
| science fiction | 3221.0 | 4.0 |
| François | 3220.0 | 25.0 |
| University of Wisconsin–Madison | 3220.0 | 2.0 |
| Basketball | 3215.0 | 30.0 |
| Bulgaria | 3214.0 | 68.0 |
| municipality seat | 3213.0 | 2.0 |
| Serbia | 3208.0 | 46.0 |
| chess player | 3202.0 | 4.0 |
| year | 3180.0 | 5.0 |
| Q12809484 | 3170.0 | 1.0 |
| Kyiv | 3169.0 | 68.0 |
| Stanford University | 3165.0 | 5.0 |
| Johannes | 3150.0 | 45.0 |
| Q10905276 | 3149.0 | 2.0 |
| Curculionidae | 3136.0 | 4.0 |
| Giuseppe | 3132.0 | 12.0 |
| 1972 Summer Olympics | 3126.0 | 9.0 |
| piano | 3122.0 | 2.0 |
| Père Lachaise Cemetery | 3116.0 | 12.0 |
| Q17489143 | 3111.0 | 1.0 |
| 1984 Summer Olympics | 3105.0 | 7.0 |
| North Brabant | 3096.0 | 83.0 |
| Stockholm | 3093.0 | 36.0 |
| psychologist | 3092.0 | 4.0 |
| California | 3083.0 | 190.0 |
| German Academy of Sciences Leopoldina | 3081.0 | 9.0 |
| Documentary film | 3066.0 | 3.0 |
| professor | 3058.0 | 5.0 |
| Medal "For the Victory over Germany in the Great Patriotic War 1941–1945" | 3055.0 | 1.0 |
| Uruguay | 3052.0 | 56.0 |
| radio personality | 3048.0 | 4.0 |
| volleyball player | 3034.0 | 4.0 |
| Cornell University | 3029.0 | 5.0 |
| Philadelphia | 3016.0 | 50.0 |
| Carlos | 3004.0 | 56.0 |
| Colombia | 2987.0 | 65.0 |
| Jack | 2980.0 | 68.0 |
| guitarist | 2976.0 | 3.0 |
| Eton College | 2975.0 | 13.0 |
| Officer of the Order of the British Empire | 2973.0 | 4.0 |
| Hanover | 2971.0 | 33.0 |
| Rudolf | 2968.0 | 7.0 |
| baronet | 2964.0 | 2.0 |
| illustrator | 2946.0 | 4.0 |
| Chris | 2945.0 | 9.0 |
| action film | 2925.0 | 3.0 |
| homosexuality | 2917.0 | 1.0 |
| given name | 2914.0 | 7.0 |
| Samuel | 2913.0 | 25.0 |
| Rotterdam | 2910.0 | 88.0 |
| Esperanto | 2905.0 | 11.0 |
| badminton player | 2902.0 | 4.0 |
| Q16735927 | 2898.0 | 2.0 |
| Joe | 2896.0 | 44.0 |
| Anna | 2886.0 | 127.0 |
| Henri | 2882.0 | 37.0 |
| Andrew | 2881.0 | 60.0 |
| Belarus | 2865.0 | 65.0 |
| Greeks | 2864.0 | 1.0 |
| commune of Romania | 2861.0 | 4.0 |
| Harry | 2851.0 | 16.0 |
| Brussels | 2850.0 | 53.0 |
| Jones | 2832.0 | 27.0 |
| suicide | 2823.0 | 2.0 |
| University of Chicago | 2817.0 | 5.0 |
| Wikimedia disambiguation page | 2808.0 | 7.0 |
| Genoa | 2802.0 | 46.0 |
| librarian | 2794.0 | 4.0 |
| Jim | 2791.0 | 21.0 |
| Williams | 2780.0 | 1.0 |
| member of parliament | 2779.0 | 3.0 |
| Royal Navy | 2773.0 | 10.0 |
| Cuba | 2752.0 | 70.0 |
| André | 2752.0 | 29.0 |
| Esperantist | 2734.0 | 3.0 |
| television actor | 2733.0 | 2.0 |
| 1976 Summer Olympics | 2732.0 | 8.0 |
| soprano | 2729.0 | 2.0 |
| Brown | 2726.0 | 3.0 |
| Patrick | 2721.0 | 33.0 |
| member of the Chamber of Representatives of Belgium | 2718.0 | 4.0 |
| Bob | 2712.0 | 15.0 |
| New York University | 2695.0 | 4.0 |
| San Francisco | 2694.0 | 153.0 |
| Q19622166 | 2684.0 | 3.0 |
| Rijksmonument complex | 2683.0 | 2.0 |
| member of the Chamber of Deputies of the Italian Republic | 2682.0 | 4.0 |
| Biblioteca Museu Víctor Balaguer | 2682.0 | 3.0 |
| Order of the Badge of Honour | 2681.0 | 2.0 |
| banker | 2679.0 | 4.0 |
| Slovenia | 2669.0 | 33.0 |
| sociologist | 2667.0 | 3.0 |
| Middelburg | 2665.0 | 32.0 |
| 2014 Winter Olympics | 2658.0 | 4.0 |
| Francisco | 2652.0 | 44.0 |
| New York | 2646.0 | 75.0 |
| romantic comedy | 2646.0 | 3.0 |
| Socialist Unity Party of Germany | 2645.0 | 9.0 |
| Düsseldorf | 2640.0 | 63.0 |
| Massachusetts Institute of Technology | 2638.0 | 6.0 |
| female given name | 2636.0 | 3.0 |
| Francesco | 2626.0 | 43.0 |
| university | 2614.0 | 4.0 |
| man | 2613.0 | 4.0 |
| Estonia | 2610.0 | 49.0 |
| Steve | 2610.0 | 39.0 |
| Q19595175 | 2610.0 | 3.0 |
| Cerambycidae | 2597.0 | 4.0 |
| Slovakia | 2595.0 | 53.0 |
| Knight's Cross of the Iron Cross | 2582.0 | 3.0 |
| Legionnaire of Legion of Merit | 2570.0 | 1.0 |
| Athens | 2561.0 | 102.0 |
| Wolfgang | 2547.0 | 3.0 |
| Andreas | 2546.0 | 33.0 |
| Mario | 2538.0 | 47.0 |
| National Academy of Sciences | 2534.0 | 2.0 |
| sports season of a sports club | 2534.0 | 2.0 |
| field hockey player | 2531.0 | 5.0 |
| Toronto | 2527.0 | 45.0 |
| swimmer | 2516.0 | 3.0 |
| Scotland | 2507.0 | 37.0 |
| Officer of the Legion of Honour | 2499.0 | 5.0 |
| university teacher | 2494.0 | 6.0 |
| Tehran | 2492.0 | 47.0 |
| rugby player | 2491.0 | 4.0 |
| Anton | 2487.0 | 26.0 |
| Victor | 2481.0 | 73.0 |
| Malaysia | 2476.0 | 44.0 |
| cinematographer | 2474.0 | 4.0 |
| Lyon | 2470.0 | 50.0 |
| 2010 Winter Olympics | 2468.0 | 13.0 |
| Bologna | 2464.0 | 40.0 |
| cadastral populated place in the Netherlands | 2461.0 | 2.0 |
| Johnson | 2456.0 | 4.0 |
| Bremen | 2454.0 | 61.0 |
| record producer | 2453.0 | 4.0 |
| municipality of Switzerland | 2450.0 | 5.0 |
| member of the Reichstag of the German Empire | 2449.0 | 4.0 |
| Francis | 2444.0 | 65.0 |
| political party | 2424.0 | 1.0 |
| Saxophone | 2421.0 | 3.0 |
| Q13156709 | 2419.0 | 5.0 |
| person | 2417.0 | 2.0 |
| Manuel | 2417.0 | 24.0 |
| Brooklyn | 2416.0 | 39.0 |
| jazz musician | 2411.0 | 3.0 |
| Trinity College | 2409.0 | 15.0 |
| Medal of Honor | 2409.0 | 41.0 |
| Roger | 2409.0 | 14.0 |
| Michel | 2406.0 | 22.0 |
| Croatia | 2405.0 | 55.0 |
| Computer scientist | 2401.0 | 4.0 |
| Member of the Chamber of Deputies of Mexico | 2392.0 | 3.0 |
| 1968 Summer Olympics | 2389.0 | 7.0 |
| University of Toronto | 2388.0 | 3.0 |
| The Hague | 2380.0 | 40.0 |
| zoologist | 2370.0 | 3.0 |
| member of the Parliament of Norway | 2362.0 | 5.0 |
| municipality of Austria | 2362.0 | 4.0 |
| Royal Swedish Academy of Sciences | 2348.0 | 9.0 |
| Peru | 2346.0 | 66.0 |
| thriller | 2346.0 | 3.0 |
| Russian | 2340.0 | 5.0 |
| Stalin Prize | 2339.0 | 4.0 |
| religious painting | 2334.0 | 3.0 |
| Wrocław | 2333.0 | 21.0 |
| Herbert | 2331.0 | 17.0 |
| Brian | 2327.0 | 5.0 |
| romance film | 2327.0 | 3.0 |
| Liberal Party of Canada | 2324.0 | 5.0 |
| announcer | 2321.0 | 2.0 |
| Copenhagen | 2315.0 | 25.0 |
| La Silla Observatory | 2315.0 | 6.0 |
| member of the Lok Sabha | 2308.0 | 4.0 |
| biologist | 2307.0 | 3.0 |
| Stephen | 2306.0 | 66.0 |
| Marseille | 2302.0 | 89.0 |
| Communist Party of Germany | 2300.0 | 12.0 |
| Bonn | 2298.0 | 27.0 |
| Werner | 2290.0 | 14.0 |
| 1980 Summer Olympics | 2284.0 | 7.0 |
| Tom Gehrels | 2282.0 | 9.0 |
| Adam | 2281.0 | 80.0 |
| Latin | 2279.0 | 4.0 |
| University of California, Los Angeles | 2278.0 | 2.0 |
| Fritz | 2278.0 | 17.0 |
| Ingrid van Houten-Groeneveld | 2269.0 | 9.0 |
| murder | 2269.0 | 3.0 |
| Cornelis Johannes van Houten | 2268.0 | 8.0 |
| University of Vienna | 2262.0 | 6.0 |
| Boston | 2258.0 | 66.0 |
| tenor | 2248.0 | 1.0 |
| Q15277251 | 2228.0 | 8.0 |
| Ortsteil | 2228.0 | 3.0 |
| Lithuania | 2218.0 | 48.0 |
| Utrecht | 2218.0 | 69.0 |
| University of Edinburgh | 2217.0 | 4.0 |
| Georges | 2215.0 | 36.0 |
| television film | 2210.0 | 2.0 |
| Knight of the Order of Polonia Restituta | 2210.0 | 1.0 |
| RCA Records, Inc. | 2208.0 | 4.0 |
| Order of the October Revolution | 2207.0 | 1.0 |
| Harvard Law School | 2207.0 | 4.0 |
| Wilson | 2202.0 | 61.0 |
| August | 2193.0 | 64.0 |
| cardinal | 2190.0 | 4.0 |
| Australian Labor Party | 2188.0 | 5.0 |
| Groningen | 2175.0 | 87.0 |
| Centre Party | 2173.0 | 23.0 |
| family name | 2169.0 | 6.0 |
| PlayStation 2 | 2165.0 | 8.0 |
| Luis | 2163.0 | 17.0 |
| Noctuidae | 2162.0 | 4.0 |
| American Civil War | 2162.0 | 10.0 |
| University of Göttingen | 2161.0 | 4.0 |
| 1964 Summer Olympics | 2152.0 | 7.0 |
| Bernard | 2143.0 | 28.0 |
| 2010 Asian Games | 2136.0 | 7.0 |
| Rio de Janeiro | 2134.0 | 159.0 |
| Sydney | 2127.0 | 34.0 |
| science fiction film | 2124.0 | 4.0 |
| Oslo | 2112.0 | 51.0 |
| Latvia | 2111.0 | 152.0 |
| Luxembourg | 2111.0 | 110.0 |
| Barcelona | 2111.0 | 86.0 |
| Q17412908 | 2109.0 | 2.0 |
| Basel | 2108.0 | 17.0 |
| Museum of Modern Art | 2108.0 | 4.0 |
| Ludwig | 2107.0 | 43.0 |
| Marie | 2104.0 | 60.0 |
| Kevin | 2100.0 | 6.0 |
| Johan | 2093.0 | 37.0 |
| Bavarian Order of Merit | 2090.0 | 4.0 |
| record label | 2089.0 | 4.0 |
| racing driver | 2082.0 | 2.0 |
| member of the House of Representatives of the Netherlands | 2064.0 | 4.0 |
| musical film | 2062.0 | 3.0 |
| 1960 Summer Olympics | 2062.0 | 7.0 |
| Kurt | 2059.0 | 4.0 |
| University of Oxford | 2056.0 | 4.0 |
| Egypt | 2055.0 | 58.0 |
| Tony | 2043.0 | 45.0 |
| Eric Walter Elst | 2041.0 | 6.0 |
| Bronze Star Medal | 2039.0 | 3.0 |
| Atlantic Records | 2036.0 | 6.0 |
| 1952 Summer Olympics | 2034.0 | 8.0 |
| comedy drama | 2034.0 | 3.0 |
| live album | 2028.0 | 1.0 |
| Nigeria | 2027.0 | 68.0 |
| Epic Records | 2025.0 | 5.0 |
| Carlo | 2020.0 | 11.0 |
| tuberculosis | 1998.0 | 2.0 |
| Claude | 1996.0 | 20.0 |
| Heidelberg | 1994.0 | 40.0 |
| Antwerp | 1992.0 | 61.0 |
| crime film | 1989.0 | 3.0 |
| Simon | 1973.0 | 51.0 |
| Commander of the Legion of Honour | 1970.0 | 5.0 |
| choreographer | 1969.0 | 3.0 |
| Bruno | 1963.0 | 99.0 |
| Least Concern | 1963.0 | 1.0 |
| Alan | 1960.0 | 5.0 |
| Q13217683 | 1959.0 | 5.0 |
| Q17744604 | 1957.0 | 1.0 |
| Luigi | 1956.0 | 8.0 |
| René | 1950.0 | 15.0 |
| Nuremberg | 1949.0 | 86.0 |
| 2006 Winter Olympics | 1947.0 | 9.0 |
| Europe | 1946.0 | 30.0 |
| saint | 1946.0 | 3.0 |
| municipality of Brazil | 1942.0 | 4.0 |
| Andrea | 1939.0 | 70.0 |
| protein | 1938.0 | 1.0 |
| UTC+02:00 | 1938.0 | 1.0 |
| Palermo | 1937.0 | 61.0 |
| Maurice | 1936.0 | 45.0 |
| Ancient Rome | 1927.0 | 57.0 |
| Anthony | 1925.0 | 46.0 |
| Warner Bros. Records | 1925.0 | 4.0 |
| Gerhard | 1921.0 | 21.0 |
| psychiatrist | 1918.0 | 4.0 |
| Stefan | 1916.0 | 25.0 |
| Kitt Peak National Observatory | 1915.0 | 3.0 |
| pornographic actor | 1913.0 | 2.0 |
| Member of Parliament of Great Britain | 1912.0 | 3.0 |
| stadium | 1909.0 | 3.0 |
| Roberto | 1909.0 | 14.0 |
| Fred | 1905.0 | 43.0 |
| Academy of Sciences of the USSR | 1898.0 | 4.0 |
| Philippe | 1897.0 | 27.0 |
| Norwegian Labour Party | 1893.0 | 5.0 |
| Spacewatch | 1890.0 | 5.0 |
| motorcycle rider | 1888.0 | 3.0 |
| fictional character | 1886.0 | 7.0 |
| computer keyboard | 1885.0 | 3.0 |
| Frederick | 1881.0 | 39.0 |
| house mouse | 1880.0 | 4.0 |
| USSR State Prize | 1874.0 | 2.0 |
| 1936 Summer Olympics | 1874.0 | 8.0 |
| University of Southern California | 1869.0 | 5.0 |
| alpine skier | 1863.0 | 3.0 |
| Marco | 1852.0 | 20.0 |
| Mexico City | 1848.0 | 61.0 |
| Western film | 1847.0 | 4.0 |
| Purple Heart | 1846.0 | 9.0 |
| Marc | 1844.0 | 21.0 |
| 2006 Asian Games | 1844.0 | 6.0 |
| Hong Kong | 1844.0 | 55.0 |
| colonel | 1843.0 | 11.0 |
| Anderson Mesa Station | 1842.0 | 3.0 |
| Istanbul | 1842.0 | 98.0 |
| Gustav | 1841.0 | 16.0 |
| dancer | 1841.0 | 5.0 |
| Armenia | 1833.0 | 63.0 |
| Malayalam | 1828.0 | 3.0 |
| public art | 1826.0 | 2.0 |
| Müller | 1826.0 | 24.0 |
| fantasy | 1823.0 | 2.0 |
| member of the Parliament of Finland | 1821.0 | 2.0 |
| Member of the Order of the British Empire | 1821.0 | 4.0 |
| businessperson | 1817.0 | 3.0 |
| theatrical director | 1808.0 | 3.0 |
| University of Cambridge | 1808.0 | 4.0 |
| Philadelphia Phillies | 1805.0 | 4.0 |
| 1948 Summer Olympics | 1803.0 | 7.0 |
| Klaus | 1802.0 | 12.0 |
| Arlington National Cemetery | 1802.0 | 6.0 |
| EMI | 1801.0 | 8.0 |
| Socialist Party | 1801.0 | 48.0 |
| American Academy of Arts and Sciences | 1799.0 | 3.0 |
| Haarlem | 1798.0 | 22.0 |
| Novodevichy Cemetery | 1797.0 | 7.0 |
| municipal district | 1795.0 | 3.0 |
| Leeuwarden | 1792.0 | 37.0 |
| Georgia | 1788.0 | 283.0 |
| fictional human | 1785.0 | 3.0 |
| Freiburg im Breisgau | 1775.0 | 35.0 |
| University of Warsaw | 1774.0 | 2.0 |
| Karlsruhe | 1767.0 | 69.0 |
| right-handedness | 1763.0 | 2.0 |
| 1924 Summer Olympics | 1759.0 | 6.0 |
| Pedro | 1758.0 | 29.0 |
| member of the Wisconsin State Assembly | 1751.0 | 4.0 |
| CD-ROM | 1743.0 | 1.0 |
| Pittsburgh Pirates | 1742.0 | 6.0 |
| Capitol Records | 1741.0 | 5.0 |
| Ludwig Maximilian University of Munich | 1738.0 | 8.0 |
| Tour de France | 1733.0 | 110.0 |
| St. Louis Cardinals | 1727.0 | 6.0 |
| Yakov | 1727.0 | 19.0 |
| surgeon | 1726.0 | 4.0 |
| Helsinki | 1721.0 | 29.0 |
| Miller | 1715.0 | 6.0 |
| political scientist | 1713.0 | 3.0 |
| castle | 1711.0 | 17.0 |
| Christopher | 1710.0 | 40.0 |
| Geneva | 1708.0 | 35.0 |
| University of Bonn | 1706.0 | 5.0 |
| Social Democratic Party of Austria | 1704.0 | 6.0 |
| Alex | 1702.0 | 9.0 |
| Schutzstaffel | 1701.0 | 10.0 |
| Benjamin | 1701.0 | 71.0 |
| ski jumper | 1701.0 | 3.0 |
| Chicago Cubs | 1697.0 | 5.0 |
| Riga | 1695.0 | 42.0 |
| autobiographer | 1692.0 | 3.0 |
| University of Minnesota | 1691.0 | 3.0 |
| Texas Department of Transportation | 1690.0 | 1.0 |
| Gabriel | 1689.0 | 43.0 |
| Q17781726 | 1684.0 | 5.0 |
| Lübeck | 1684.0 | 31.0 |
| shooting guard | 1683.0 | 1.0 |
| Iceland | 1677.0 | 64.0 |
| Tim | 1674.0 | 60.0 |
| Adolf | 1674.0 | 12.0 |
| Alberto | 1673.0 | 19.0 |
| abbot | 1670.0 | 3.0 |
| Dublin | 1669.0 | 35.0 |
| University of Paris | 1668.0 | 3.0 |
| biathlete | 1665.0 | 5.0 |
| Philip | 1662.0 | 53.0 |
| violin | 1654.0 | 1.0 |
| Hugo | 1651.0 | 56.0 |
| Heinz | 1650.0 | 23.0 |
| University of Tübingen | 1647.0 | 9.0 |
| Gary | 1647.0 | 13.0 |
| Algeria | 1646.0 | 75.0 |
| University College London | 1640.0 | 4.0 |
| Anne | 1639.0 | 28.0 |
| classical philologist | 1638.0 | 3.0 |
| Order of Honour | 1637.0 | 3.0 |
| sport shooter | 1636.0 | 3.0 |
| Dave | 1636.0 | 52.0 |
| fantasy film | 1628.0 | 4.0 |
| Emil | 1628.0 | 7.0 |
| judoka | 1627.0 | 4.0 |
| tennis | 1626.0 | 4.0 |
| 1956 Summer Olympics | 1626.0 | 7.0 |
| Minsk | 1626.0 | 68.0 |
| Scott | 1625.0 | 16.0 |
| 1912 Summer Olympics | 1624.0 | 5.0 |
| registered immobile cultural heritage of Slovenia | 1622.0 | 1.0 |
| classical music | 1621.0 | 1.0 |
| Chicago White Sox | 1618.0 | 6.0 |
| 1920 Olympics | 1618.0 | 6.0 |
| Santiago | 1616.0 | 97.0 |
| Leipzig University | 1615.0 | 6.0 |
| Bern | 1611.0 | 38.0 |
| Free Democratic Party | 1608.0 | 14.0 |
| Green Bay Packers | 1608.0 | 6.0 |
| ROM cartridge | 1598.0 | 1.0 |
| Companion of the Order of the Bath | 1597.0 | 5.0 |
| member of the Reichstag of the Weimar Republic | 1596.0 | 5.0 |
| Xbox 360 | 1594.0 | 6.0 |
| São Paulo | 1593.0 | 80.0 |
| PlayStation 3 | 1593.0 | 6.0 |
| University of Oslo | 1590.0 | 3.0 |
| University of Pennsylvania | 1588.0 | 13.0 |
| Strasbourg | 1584.0 | 24.0 |
| Maastricht | 1584.0 | 23.0 |
| Lentapedia | 1584.0 | 3.0 |
| DOS | 1580.0 | 1.0 |
| Jonathan | 1574.0 | 19.0 |
| Washington Commanders | 1574.0 | 5.0 |
| United States Military Academy | 1574.0 | 6.0 |
| rural municipality of Poland | 1573.0 | 4.0 |
| Barbara | 1572.0 | 84.0 |
| Jorge | 1572.0 | 23.0 |
| Q17535155 | 1571.0 | 4.0 |
| philologist | 1571.0 | 4.0 |
| AV idol | 1570.0 | 5.0 |
| bridge | 1569.0 | 3.0 |
| Distinguished Flying Cross | 1564.0 | 5.0 |
| Ben | 1564.0 | 39.0 |
| Brown University | 1560.0 | 4.0 |
| organist | 1558.0 | 4.0 |
| Q17320547 | 1557.0 | 3.0 |
| Institutional Revolutionary Party | 1556.0 | 5.0 |
| Dictionary of Art Historians | 1556.0 | 1.0 |
| Q17456783 | 1552.0 | 2.0 |
| disc jockey | 1551.0 | 2.0 |
| Tübingen | 1551.0 | 38.0 |
| Göttingen | 1549.0 | 14.0 |
| point guard | 1548.0 | 1.0 |
| order | 1547.0 | 14.0 |
| center | 1546.0 | 1.0 |
| Don | 1545.0 | 59.0 |
| Distinguished Service Order | 1544.0 | 3.0 |
| ship class | 1542.0 | 2.0 |
| Dan | 1540.0 | 7.0 |
| award | 1535.0 | 1.0 |
| Nicolas | 1534.0 | 8.0 |
| Austrian People's Party | 1534.0 | 8.0 |
| Raymond | 1533.0 | 23.0 |
| 2002 Winter Olympics | 1532.0 | 8.0 |
| Ernest | 1531.0 | 5.0 |
| Belgrade | 1531.0 | 32.0 |
| Ian | 1529.0 | 22.0 |
| trumpet | 1529.0 | 2.0 |
| Korean War | 1528.0 | 3.0 |
| power forward | 1527.0 | 1.0 |
| Erich | 1523.0 | 7.0 |
| Erik | 1523.0 | 23.0 |
| University of Illinois system | 1523.0 | 2.0 |
| Graz | 1521.0 | 39.0 |
| Geometridae | 1520.0 | 4.0 |
| genre painting | 1520.0 | 3.0 |
| Kiel | 1518.0 | 37.0 |
| Venezuela | 1516.0 | 65.0 |
| Essanay Studios | 1514.0 | 2.0 |
| Julius | 1513.0 | 14.0 |
| Rostock | 1509.0 | 25.0 |
| Liberal Democratic Party | 1507.0 | 30.0 |
| member of the Ontario Provincial Parliament | 1506.0 | 6.0 |
| platform game | 1505.0 | 2.0 |
| sprinter | 1505.0 | 4.0 |
| compilation album | 1502.0 | 1.0 |
| Polish United Workers' Party | 1500.0 | 5.0 |
| Antoine | 1500.0 | 25.0 |
| mangaka | 1497.0 | 4.0 |
| Halle (Saale) | 1496.0 | 16.0 |
| Braunschweig | 1496.0 | 19.0 |
| Virgin Records | 1495.0 | 3.0 |
| municipality of the Philippines | 1488.0 | 3.0 |
| Appletons' Cyclopædia of American Biography | 1488.0 | 1.0 |
| Odessa | 1488.0 | 55.0 |
| Elizabeth | 1486.0 | 88.0 |
| Military Cross | 1485.0 | 2.0 |
| Donald | 1482.0 | 1.0 |
| 1482.0 | 19.0 | |
| Q17590876 | 1482.0 | 2.0 |
| twin | 1481.0 | 4.0 |
| civil engineer | 1480.0 | 3.0 |
| comic strip | 1479.0 | 1.0 |
| lung cancer | 1477.0 | 10.0 |
| xkcd | 1472.0 | 4.0 |
| Swedish Social Democratic Party | 1471.0 | 6.0 |
| stroke | 1471.0 | 2.0 |
| speed skater | 1470.0 | 3.0 |
| United States Air Force | 1468.0 | 61.0 |
| Order "For Merit to the Fatherland" IV class | 1465.0 | 3.0 |
| action game | 1465.0 | 2.0 |
| Creative Commons Attribution-NonCommercial | 1464.0 | 4.0 |
| Randall Munroe | 1463.0 | 11.0 |
| Legion of Honour | 1461.0 | 8.0 |
| Miguel | 1457.0 | 18.0 |
| Breda | 1457.0 | 45.0 |
| Johns Hopkins University | 1456.0 | 5.0 |
| Eduard | 1455.0 | 9.0 |
| small forward | 1454.0 | 1.0 |
| Darmstadt | 1452.0 | 48.0 |
| Fernando | 1451.0 | 20.0 |
| Marcel | 1448.0 | 9.0 |
| county of China | 1447.0 | 2.0 |
| Pietro | 1444.0 | 22.0 |
| association football venue | 1444.0 | 2.0 |
| Lisbon | 1443.0 | 110.0 |
| Knight Commander of the Order of the Bath | 1443.0 | 6.0 |
| Manhattan | 1441.0 | 54.0 |
| island | 1439.0 | 2.0 |
| ambassador | 1437.0 | 3.0 |
| Pilot | 1434.0 | 351.0 |
| 1928 Summer Olympics | 1433.0 | 6.0 |
| Conservative Party of Norway | 1432.0 | 5.0 |
| baritone | 1431.0 | 2.0 |
| Sturmabteilung | 1426.0 | 7.0 |
| Polydor Records | 1423.0 | 5.0 |
| Jeff | 1421.0 | 27.0 |
| Royal Air Force | 1416.0 | 5.0 |
| Bernhard | 1412.0 | 9.0 |
| Helmut | 1411.0 | 3.0 |
| Department of Paintings of the Louvre | 1409.0 | 5.0 |
| Indian National Congress | 1405.0 | 4.0 |
| Cicadellidae | 1405.0 | 3.0 |
| Padua | 1405.0 | 37.0 |
| Northwestern University | 1400.0 | 3.0 |
| Kazakhstan | 1399.0 | 51.0 |
| comics artist | 1396.0 | 7.0 |
| position | 1394.0 | 2.0 |
| 's-Hertogenbosch | 1390.0 | 24.0 |
| Ken | 1389.0 | 21.0 |
| Ohio State University | 1387.0 | 5.0 |
| Guy | 1386.0 | 59.0 |
| Melbourne | 1384.0 | 30.0 |
| BBC | 1382.0 | 8.0 |
| Stockholm Municipality | 1382.0 | 29.0 |
| deputy of Chile | 1382.0 | 3.0 |
| rural municipality of Austria | 1380.0 | 3.0 |
| University of Texas at Austin | 1378.0 | 4.0 |
| Paraguay | 1377.0 | 47.0 |
| Liberal Party | 1375.0 | 78.0 |
| Mainz | 1375.0 | 36.0 |
| member of the State Senate of New York | 1374.0 | 4.0 |
| video game industry | 1374.0 | 1.0 |
| Matt | 1369.0 | 7.0 |
| New Orleans | 1368.0 | 49.0 |
| scientist | 1365.0 | 4.0 |
| London School of Economics and Political Science | 1365.0 | 5.0 |
| Member of the Swiss National Council | 1365.0 | 5.0 |
| film editor | 1363.0 | 2.0 |
| Pneumonia | 1362.0 | 6.0 |
| Baltimore | 1360.0 | 28.0 |
| Paolo | 1359.0 | 19.0 |
| J. | 1359.0 | 2.0 |
| comic book album | 1355.0 | 2.0 |
| member of the Hellenic Parliament | 1355.0 | 5.0 |
| Kyoto University | 1352.0 | 3.0 |
| Münster | 1352.0 | 39.0 |
| comedian | 1347.0 | 3.0 |
| neck gable building | 1344.0 | 1.0 |
| Detroit | 1341.0 | 34.0 |
| catholic bishop | 1340.0 | 3.0 |
| 2002 Asian Games | 1340.0 | 4.0 |
| Stanley Cup | 1338.0 | 4.0 |
| profession | 1337.0 | 1.0 |
| Carabidae | 1333.0 | 3.0 |
| Ralph | 1332.0 | 5.0 |
| Ferdinand | 1330.0 | 28.0 |
| St. Louis | 1328.0 | 43.0 |
| North Rhine-Westphalia | 1325.0 | 28.0 |
| Jupiter trojan | 1325.0 | 1.0 |
| Bordeaux | 1317.0 | 40.0 |
| Q1248362 | 1316.0 | 3.0 |
| Bucharest | 1314.0 | 41.0 |
| presenter | 1312.0 | 2.0 |
| Rabbi | 1311.0 | 2.0 |
| Alkmaar | 1310.0 | 27.0 |
| class A Swiss cultural property of national significance | 1305.0 | 3.0 |
| member of the Pennsylvania House of Representatives | 1303.0 | 3.0 |
| Short story | 1302.0 | 4.0 |
| German Archaeological Institute | 1301.0 | 4.0 |
| Harold | 1301.0 | 18.0 |
| Jason | 1301.0 | 25.0 |
| mountain | 1300.0 | 3.0 |
| musher | 1300.0 | 4.0 |
| sports video game | 1299.0 | 2.0 |
| Kenya | 1297.0 | 33.0 |
| archivist | 1294.0 | 3.0 |
| Kanagawa Prefecture | 1290.0 | 35.0 |
| New Jersey | 1290.0 | 22.0 |
| Jimmy | 1289.0 | 23.0 |
| Toulouse | 1287.0 | 40.0 |
| Edinburgh | 1286.0 | 29.0 |
| Heidelberg University | 1286.0 | 11.0 |
| essayist | 1285.0 | 3.0 |
| Crimean Astrophysical Observatory | 1283.0 | 3.0 |
| Silver Star | 1280.0 | 19.0 |
| rural district of Iran | 1277.0 | 3.0 |
| Azerbaijan | 1276.0 | 100.0 |
| Billy | 1271.0 | 63.0 |
| Tbilisi | 1270.0 | 32.0 |
| Würzburg | 1269.0 | 108.0 |
| Leo | 1267.0 | 22.0 |
| member of the Swedish Riksdag | 1266.0 | 4.0 |
| Texas | 1266.0 | 70.0 |
| Asia | 1265.0 | 33.0 |
| Montevideo | 1265.0 | 32.0 |
| farmer | 1263.0 | 9.0 |
| inventor | 1262.0 | 3.0 |
| Landrat | 1261.0 | 1.0 |
| Sofia | 1258.0 | 51.0 |
| Christoph | 1257.0 | 14.0 |
| Matthew | 1257.0 | 18.0 |
| French Academy of Sciences | 1256.0 | 4.0 |
| Jürgen | 1253.0 | 8.0 |
| Joachim | 1253.0 | 27.0 |
| Lars | 1251.0 | 6.0 |
| paleontologist | 1251.0 | 4.0 |
| Nice | 1250.0 | 69.0 |
display(degrees.sort($"outDegree".desc))
| id | inDegree | outDegree |
|---|---|---|
| statue of Sacred Heart of Jesus Christ | 515.0 | 2161.0 |
| Molenstraat | 7.0 | 1288.0 |
| Molenweg | 50.0 | 1178.0 |
| Pas-de-Calais | 917.0 | 911.0 |
| Wilhelminastraat | 39.0 | 883.0 |
| Moselle | 897.0 | 882.0 |
| Aisne | 881.0 | 835.0 |
| Kerkstraat | 728.0 | 833.0 |
| John Smith | 9.0 | 820.0 |
| Madonna and Child | 610.0 | 816.0 |
| Central District | 801.0 | 705.0 |
| Seine-et-Oise | 705.0 | 703.0 |
| Self-portrait | 341.0 | 698.0 |
| Meurthe | 689.0 | 697.0 |
| Bezirk Lothringen | 686.0 | 693.0 |
| Dorpsstraat | 569.0 | 681.0 |
| Eikenlaan | 4.0 | 679.0 |
| Nord-Pas-de-Calais | 22.0 | 668.0 |
| Prins Bernhardstraat | 5.0 | 624.0 |
| John Williams | 32.0 | 608.0 |
| Emmastraat | 18.0 | 588.0 |
| Meurthe-et-Moselle | 587.0 | 585.0 |
| Venus and Adonis | 4.0 | 576.0 |
| John Brown | 7.0 | 570.0 |
| Haute-Garonne | 564.0 | 557.0 |
| Hautes-Pyrénées | 620.0 | 555.0 |
| Vosges | 586.0 | 551.0 |
| Raadhuisstraat | 96.0 | 550.0 |
| Bas-Rhin | 553.0 | 549.0 |
| Calvados | 600.0 | 548.0 |
| Manche | 559.0 | 542.0 |
| Doubs | 524.0 | 531.0 |
| Pyrénées-Atlantiques | 528.0 | 525.0 |
| Dordogne | 532.0 | 524.0 |
| Seine-et-Marne | 534.0 | 522.0 |
| Orne | 522.0 | 521.0 |
| Eure | 523.0 | 521.0 |
| Haut-Rhin | 553.0 | 520.0 |
| Unterelsaß | 502.0 | 509.0 |
| Portrait of a man | 24.0 | 507.0 |
| Portrait of a Man | 13.0 | 491.0 |
| Self-Portrait | 17.0 | 481.0 |
| Saône-et-Loire | 513.0 | 476.0 |
| Yonne | 476.0 | 475.0 |
| John Taylor | 10.0 | 471.0 |
| Adoration of the Magi | 153.0 | 465.0 |
| Untitled | 24.0 | 459.0 |
| Haute-Marne | 453.0 | 450.0 |
| Ain | 457.0 | 449.0 |
| John Anderson | 56.0 | 440.0 |
| John Wilson | 2.0 | 437.0 |
| Raadhuisplein | 41.0 | 436.0 |
| Bernhardstraat | 1.0 | 430.0 |
| Les Misérables | 26.0 | 428.0 |
| William Smith | 44.0 | 427.0 |
| Portrait of a Woman | 18.0 | 424.0 |
| Wilhelminalaan | 14.0 | 394.0 |
| Virgin and Child | 11.0 | 388.0 |
| George Smith | 1.0 | 379.0 |
| Ille-et-Vilaine | 377.0 | 377.0 |
| The Three Musketeers | 17.0 | 377.0 |
| Loire | 437.0 | 372.0 |
| Upper Alsace | 365.0 | 367.0 |
| Landscape | 236.0 | 366.0 |
| Hérault | 374.0 | 362.0 |
| David Smith | 11.0 | 362.0 |
| Stationsplein | 25.0 | 358.0 |
| Annunciation | 255.0 | 354.0 |
| Home | 77.0 | 353.0 |
| Pilot | 1434.0 | 351.0 |
| Kerkplein | 216.0 | 350.0 |
| Allier | 348.0 | 347.0 |
| Cleopatra | 94.0 | 345.0 |
| De Hoop | 2.0 | 343.0 |
| Hoofdstraat | 312.0 | 339.0 |
| The Death of Cleopatra | 6.0 | 337.0 |
| John Jones | 3.0 | 334.0 |
| Province of Turin | 331.0 | 332.0 |
| Rhône | 406.0 | 330.0 |
| John Martin | 14.0 | 323.0 |
| David Brown | 25.0 | 317.0 |
| John Moore | 6.0 | 316.0 |
| Korenbloemstraat | 1.0 | 315.0 |
| Li Shi | 279.0 | 314.0 |
| Ottův slovník naučný | 1.0 | 313.0 |
| Bathsheba | 43.0 | 313.0 |
| Markt | 458.0 | 311.0 |
| Crucifixion | 34.0 | 311.0 |
| John Campbell | 5.0 | 309.0 |
| Nederlands Hervormde Kerk | 2.0 | 305.0 |
| Prins Bernhardlaan | 2.0 | 303.0 |
| James Brown | 115.0 | 302.0 |
| Angel | 417.0 | 293.0 |
| Hamlet | 44.0 | 289.0 |
| Thomas Smith | 3.0 | 289.0 |
| Merelstraat | 7.0 | 288.0 |
| Live | 129.0 | 287.0 |
| David Williams | 5.0 | 286.0 |
| Creuse | 309.0 | 284.0 |
| Georgia | 1788.0 | 283.0 |
| John Murray | 42.0 | 281.0 |
| James Wilson | 10.0 | 281.0 |
| John Scott | 2.0 | 278.0 |
| John Davis | 58.0 | 277.0 |
| Yvelines | 283.0 | 277.0 |
| George Brown | 2.0 | 276.0 |
| John Davies | 1.0 | 275.0 |
| Germany | 205497.0 | 272.0 |
| Koningin Julianastraat | 3.0 | 270.0 |
| John Harris | 7.0 | 268.0 |
| Resurrection | 24.0 | 267.0 |
| James Smith | 5.0 | 265.0 |
| Province of Cuneo | 259.0 | 263.0 |
| Province of Bergamo | 256.0 | 260.0 |
| John Walker | 10.0 | 260.0 |
| Robert Williams | 16.0 | 260.0 |
| The Annunciation | 4.0 | 260.0 |
| John White | 6.0 | 259.0 |
| The Three Graces | 4.0 | 259.0 |
| David Jones | 11.0 | 259.0 |
| Madonna with child | 3.0 | 256.0 |
| Paris | 39791.0 | 255.0 |
| Love | 101.0 | 255.0 |
| Thomas Williams | 2.0 | 249.0 |
| Greatest Hits | 68.0 | 246.0 |
| Paul Smith | 6.0 | 245.0 |
| Portrait of a woman | 13.0 | 245.0 |
| The Adoration of the Magi | 7.0 | 245.0 |
| John Bell | 3.0 | 241.0 |
| John Hill | 4.0 | 240.0 |
| Haute-Corse | 245.0 | 239.0 |
| Loire-Atlantique | 301.0 | 238.0 |
| William Williams | 3.0 | 238.0 |
| Victoria | 1169.0 | 237.0 |
| Alice in Wonderland | 3.0 | 236.0 |
| Bone morphogenetic protein 4 | 2.0 | 233.0 |
| Brazil | 21923.0 | 231.0 |
| Trentino | 228.0 | 230.0 |
| Destiny | 27.0 | 230.0 |
| John Carter | 11.0 | 229.0 |
| Michael Smith | 6.0 | 227.0 |
| John Young | 13.0 | 227.0 |
| Sint-Martinuskerk | 5.0 | 227.0 |
| Italy | 99514.0 | 226.0 |
| John Evans | 4.0 | 225.0 |
| John Gray | 12.0 | 223.0 |
| Twilight | 35.0 | 223.0 |
| Pietà | 44.0 | 223.0 |
| Li Yu | 191.0 | 222.0 |
| John Baker | 23.0 | 222.0 |
| Colin Campbell | 56.0 | 221.0 |
| Still Life | 15.0 | 221.0 |
| John Richardson | 23.0 | 221.0 |
| Province of Brescia | 218.0 | 219.0 |
| John Fraser | 18.0 | 218.0 |
| John Hall | 4.0 | 218.0 |
| Alpes-de-Haute-Provence | 217.0 | 217.0 |
| John Roberts | 5.0 | 216.0 |
| Evangelical Church | 4.0 | 216.0 |
| Robert Smith | 42.0 | 214.0 |
| Chris Smith | 8.0 | 213.0 |
| Napoléon | 48.0 | 213.0 |
| Pandora | 22.0 | 213.0 |
| Cinderella | 30.0 | 213.0 |
| Rio Grande do Sul | 241.0 | 213.0 |
| Essonne | 209.0 | 213.0 |
| George Wilson | 1.0 | 213.0 |
| Tarn-et-Garonne | 227.0 | 212.0 |
| David Wilson | 3.0 | 211.0 |
| John Jackson | 3.0 | 211.0 |
| Life | 47.0 | 210.0 |
| Adam and Eve | 11.0 | 210.0 |
| Russia | 42317.0 | 210.0 |
| Leda and the Swan | 19.0 | 209.0 |
| John Rogers | 9.0 | 209.0 |
| Mike Smith | 2.0 | 209.0 |
| James Walker | 4.0 | 208.0 |
| Danaë | 28.0 | 208.0 |
| Mary Magdalene | 191.0 | 208.0 |
| Steve Smith | 4.0 | 207.0 |
| Li Yi | 175.0 | 207.0 |
| St. Martin | 10.0 | 207.0 |
| John Ward | 3.0 | 207.0 |
| James Anderson | 31.0 | 206.0 |
| John Lewis | 6.0 | 205.0 |
| Forever | 46.0 | 205.0 |
| Alice | 770.0 | 205.0 |
| Province of Pavia | 200.0 | 204.0 |
| Carmen | 464.0 | 204.0 |
| Treasure Island | 16.0 | 204.0 |
| Hervormde kerk | 1.0 | 203.0 |
| Province of Alessandria | 200.0 | 203.0 |
| Salome | 56.0 | 203.0 |
| Val-d'Oise | 206.0 | 203.0 |
| Fibroblast growth factor receptor 2 | 2.0 | 202.0 |
| Lozère | 206.0 | 200.0 |
| Venus and Cupid | 3.0 | 200.0 |
| Irenelaan | 1.0 | 200.0 |
| John Kennedy | 4.0 | 199.0 |
| The Count of Monte Cristo | 4.0 | 198.0 |
| Li Jing | 163.0 | 198.0 |
| Hervormde Kerk | 2.0 | 197.0 |
| John Murphy | 2.0 | 197.0 |
| John Robinson | 19.0 | 196.0 |
| Lucy | 497.0 | 196.0 |
| Molenlaan | 10.0 | 195.0 |
| Hero | 34.0 | 194.0 |
| Paul Williams | 20.0 | 194.0 |
| SMAD family member 3 | 2.0 | 194.0 |
| Susanna and the Elders | 34.0 | 193.0 |
| William Thompson | 3.0 | 191.0 |
| Paradise | 32.0 | 191.0 |
| Believe | 45.0 | 190.0 |
| Madonna with Child | 3.0 | 190.0 |
| California | 3083.0 | 190.0 |
| John Phillips | 21.0 | 189.0 |
| John Hughes | 55.0 | 187.0 |
| Robert Campbell | 2.0 | 186.0 |
| Hautes-Alpes | 185.0 | 186.0 |
| Friends | 71.0 | 186.0 |
| James Stewart | 92.0 | 186.0 |
| William Walker | 3.0 | 185.0 |
| William Stewart | 4.0 | 184.0 |
| Phoenix | 377.0 | 183.0 |
| Prague | 5585.0 | 183.0 |
| Richard Smith | 2.0 | 183.0 |
| Romeo and Juliet | 19.0 | 182.0 |
| Oliver Twist | 19.0 | 182.0 |
| Province of Como | 178.0 | 182.0 |
| The Hunchback of Notre Dame | 13.0 | 181.0 |
| Alpes-Maritimes | 185.0 | 181.0 |
| Aurora | 144.0 | 181.0 |
| One | 46.0 | 181.0 |
| Michael Johnson | 13.0 | 180.0 |
| Vondelstraat | 1.0 | 179.0 |
| John Simpson | 2.0 | 179.0 |
| The Kiss | 11.0 | 179.0 |
| John Ross | 2.0 | 179.0 |
| David | 11047.0 | 179.0 |
| David Davies | 3.0 | 178.0 |
| John Russell | 43.0 | 178.0 |
| Munich | 9815.0 | 177.0 |
| Richard Jones | 1.0 | 177.0 |
| Lady Godiva | 20.0 | 176.0 |
| Monster | 55.0 | 176.0 |
| New South Wales | 662.0 | 176.0 |
| John Thompson | 7.0 | 176.0 |
| Marconistraat | 1.0 | 175.0 |
| Anna Karenina | 10.0 | 175.0 |
| Jack Smith | 8.0 | 175.0 |
| Voorstraat | 547.0 | 175.0 |
| The Birth of Venus | 6.0 | 175.0 |
| United States of America | 281936.0 | 174.0 |
| Peter Brown | 11.0 | 174.0 |
| The Stranger | 15.0 | 173.0 |
| Gloria | 257.0 | 173.0 |
| John Clarke | 9.0 | 172.0 |
| John Parker | 1.0 | 172.0 |
| Janus kinase 2 | 2.0 | 171.0 |
| Julius Caesar | 30.0 | 171.0 |
| Inferno | 22.0 | 170.0 |
| John Adams | 16.0 | 170.0 |
| Bone morphogenetic protein 2 | 2.0 | 170.0 |
| Province of Salerno | 167.0 | 169.0 |
| Mark Smith | 6.0 | 169.0 |
| Poststraat | 53.0 | 168.0 |
| John Edwards | 6.0 | 168.0 |
| Schoolstraat | 89.0 | 168.0 |
| David Lewis | 30.0 | 168.0 |
| Chris Jones | 8.0 | 168.0 |
| Tom Jones | 12.0 | 167.0 |
| Australia | 29186.0 | 167.0 |
| Mark Williams | 26.0 | 166.0 |
| Shine | 42.0 | 166.0 |
| Province of Cosenza | 161.0 | 165.0 |
| Lindenstraße | 82.0 | 165.0 |
| David Johnson | 7.0 | 165.0 |
| Dawn | 101.0 | 165.0 |
| Gold | 28.0 | 164.0 |
| Free | 52.0 | 164.0 |
| Koningin Wilhelminastraat | 9.0 | 164.0 |
| Andromeda | 51.0 | 163.0 |
| Passion | 27.0 | 163.0 |
| Evolution | 32.0 | 162.0 |
| Michael Jackson | 185.0 | 162.0 |
| Li Qi | 132.0 | 162.0 |
| The Hound of the Baskervilles | 8.0 | 161.0 |
| David Thomas | 11.0 | 161.0 |
| Sonic hedgehog | 1.0 | 161.0 |
| Stationsstraat | 100.0 | 161.0 |
| Together | 33.0 | 160.0 |
| Nana | 132.0 | 160.0 |
| Ivan Ivanov | 20.0 | 160.0 |
| Shanghai | 602.0 | 160.0 |
| Rio de Janeiro | 2134.0 | 159.0 |
| Paul Martin | 29.0 | 159.0 |
| Love Story | 15.0 | 159.0 |
| Nieuwstraat | 259.0 | 159.0 |
| John Watson | 6.0 | 159.0 |
| Beautiful | 29.0 | 159.0 |
| Hans Müller | 5.0 | 158.0 |
| Macbeth | 9.0 | 158.0 |
| Go | 29.0 | 158.0 |
| Hans Schmidt | 2.0 | 158.0 |
| Li Xun | 146.0 | 158.0 |
| The Flight into Egypt | 5.0 | 158.0 |
| Hope | 97.0 | 158.0 |
| Bill Smith | 1.0 | 158.0 |
| The Baptism of Christ | 5.0 | 158.0 |
| Coronation of the Virgin | 13.0 | 157.0 |
| The Awakening | 19.0 | 157.0 |
| Walter Müller | 13.0 | 156.0 |
| Death of Cleopatra | 2.0 | 156.0 |
| Catenin (cadherin associated protein), beta 1 | 1.0 | 156.0 |
| Steve Jones | 10.0 | 156.0 |
| Chris Brown | 73.0 | 156.0 |
| John Marshall | 9.0 | 156.0 |
| David Lee | 3.0 | 156.0 |
| Province of Varese | 153.0 | 155.0 |
| The Last Days of Pompeii | 1.0 | 155.0 |
| Camille | 496.0 | 155.0 |
| Robert Brown | 27.0 | 155.0 |
| Lincoln | 360.0 | 154.0 |
| Parc naturel régional des marais du Cotentin et du Bessin | 2.0 | 154.0 |
| Hoofdweg | 168.0 | 153.0 |
| Fantômas | 5.0 | 153.0 |
| San Francisco | 2694.0 | 153.0 |
| Superman | 24.0 | 153.0 |
| First Love | 10.0 | 153.0 |
| Mike Williams | 15.0 | 153.0 |
| Michael Collins | 21.0 | 153.0 |
| Hercules | 39.0 | 153.0 |
| Robert Anderson | 18.0 | 153.0 |
| Batman | 87.0 | 152.0 |
| Fury | 19.0 | 152.0 |
| Bahia | 201.0 | 152.0 |
| Canada | 43389.0 | 152.0 |
| Madonna and child | 5.0 | 152.0 |
| Latvia | 2111.0 | 152.0 |
| Washington County | 200.0 | 151.0 |
| Adoration of the Shepherds | 6.0 | 151.0 |
| Diana | 499.0 | 151.0 |
| The Phantom of the Opera | 4.0 | 151.0 |
| province of Milan | 149.0 | 150.0 |
| Desire | 17.0 | 150.0 |
| Buenos Aires Province | 173.0 | 149.0 |
| Koningin Wilhelminalaan | 17.0 | 149.0 |
| Province of Udine | 144.0 | 149.0 |
| Blue | 42.0 | 148.0 |
| Paul Johnson | 2.0 | 148.0 |
| James Martin | 3.0 | 148.0 |
| Alive | 29.0 | 148.0 |
| The Raven | 26.0 | 148.0 |
| Amsterdam | 11290.0 | 147.0 |
| Interleukin 6 | 2.0 | 147.0 |
| Dr. Jekyll and Mr. Hyde | 1.0 | 147.0 |
| Summer | 27.0 | 147.0 |
| Scream | 27.0 | 147.0 |
| Phosphatase and tensin homolog | 2.0 | 146.0 |
| Brian Smith | 3.0 | 145.0 |
| Reclining Figure | 42.0 | 145.0 |
| A Christmas Carol | 34.0 | 144.0 |
| Li Shu | 130.0 | 144.0 |
| Rage | 33.0 | 144.0 |
| Mike Jones | 17.0 | 143.0 |
| Tom Johnson | 1.0 | 143.0 |
| Atlantis | 51.0 | 143.0 |
| John Wood | 27.0 | 143.0 |
| Venus and Mars | 4.0 | 143.0 |
| Jane Eyre | 5.0 | 143.0 |
| Nude | 16.0 | 143.0 |
| Rudolf Müller | 1.0 | 142.0 |
| Mark Jones | 9.0 | 142.0 |
| Robert Wilson | 8.0 | 141.0 |
| Time | 36.0 | 141.0 |
| Beauty and the Beast | 18.0 | 141.0 |
| William White | 7.0 | 141.0 |
| Fred Smith | 2.0 | 141.0 |
| Face to Face | 35.0 | 140.0 |
| Uganda | 613.0 | 140.0 |
| Charles Brown | 1.0 | 140.0 |
| John Thomas | 9.0 | 139.0 |
| David Campbell | 6.0 | 139.0 |
| Johannes Müller | 1.0 | 139.0 |
| Reunion | 20.0 | 139.0 |
| Heartbeat | 19.0 | 139.0 |
| I Love You | 28.0 | 139.0 |
| Interleukin 1 beta | 2.0 | 139.0 |
| Crash | 26.0 | 139.0 |
| The Game | 51.0 | 139.0 |
| Teenage Mutant Ninja Turtles | 11.0 | 138.0 |
| Sahara | 24.0 | 138.0 |
| Dracula | 18.0 | 138.0 |
| Independence Day | 16.0 | 138.0 |
| catenin beta 1 | 1.0 | 137.0 |
| Madonna | 219.0 | 137.0 |
| The Crucifixion | 4.0 | 137.0 |
| Heaven | 33.0 | 137.0 |
| Zürich | 3275.0 | 137.0 |
| Exodus | 36.0 | 137.0 |
| George Jones | 105.0 | 136.0 |
| Mother | 14.0 | 136.0 |
| Richard Wagner | 33.0 | 136.0 |
| Inside Out | 29.0 | 136.0 |
| Kidnapped | 6.0 | 136.0 |
| Spring | 17.0 | 136.0 |
| Venus | 163.0 | 135.0 |
| transforming growth factor beta 1 | 1.0 | 135.0 |
| The Fugitive | 16.0 | 135.0 |
| Alexander | 4804.0 | 134.0 |
| Joan of Arc | 44.0 | 134.0 |
| Bill Brown | 16.0 | 134.0 |
| Flashback | 16.0 | 134.0 |
| Secrets | 25.0 | 134.0 |
| Paul Miller | 3.0 | 134.0 |
| Prey | 15.0 | 134.0 |
| Titanic | 2.0 | 134.0 |
| Rush | 78.0 | 134.0 |
| The Trap | 4.0 | 134.0 |
| John James | 9.0 | 133.0 |
| Tom Brown | 17.0 | 133.0 |
| Buenos Aires | 3600.0 | 133.0 |
| Bill Miller | 9.0 | 133.0 |
| Province of Rome | 131.0 | 133.0 |
| Santa Catarina | 153.0 | 133.0 |
| Dreams | 22.0 | 132.0 |
| Province of Vicenza | 132.0 | 132.0 |
| France | 178816.0 | 132.0 |
| Noli me tangere | 16.0 | 132.0 |
| Candy | 18.0 | 132.0 |
| Bill Johnson | 2.0 | 132.0 |
| Orange | 324.0 | 132.0 |
| Stay | 38.0 | 132.0 |
| South Tyrol | 142.0 | 131.0 |
| Star Trek | 37.0 | 131.0 |
| The Merry Widow | 2.0 | 131.0 |
| David Lloyd | 3.0 | 131.0 |
| Richard Williams | 7.0 | 131.0 |
| Fire | 25.0 | 131.0 |
| Postweg | 11.0 | 131.0 |
| Werner Müller | 1.0 | 130.0 |
| Charles Smith | 1.0 | 130.0 |
| Li Zhen | 90.0 | 130.0 |
| Heroes | 118.0 | 130.0 |
| L'Arlésienne | 5.0 | 130.0 |
| Erb-b2 receptor tyrosine kinase 2 | 2.0 | 130.0 |
| Robin Hood | 36.0 | 130.0 |
| Fibroblast growth factor receptor 1 | 2.0 | 130.0 |
| Freedom | 36.0 | 130.0 |
| Hoogstraat | 214.0 | 130.0 |
| John Armstrong | 1.0 | 129.0 |
| Don Quixote | 13.0 | 129.0 |
| Province of Avellino | 125.0 | 129.0 |
| Rijksweg | 76.0 | 129.0 |
| Animal | 17.0 | 129.0 |
| Cell division cycle 42 | 2.0 | 129.0 |
| Casino Royale | 4.0 | 129.0 |
| James White | 5.0 | 129.0 |
| John Fitzgerald | 1.0 | 129.0 |
| Richard Johnson | 50.0 | 129.0 |
| Empire | 14.0 | 128.0 |
| 3 | 38.0 | 128.0 |
| Robert Taylor | 88.0 | 128.0 |
| Province of Asti | 125.0 | 128.0 |
| Tony Smith | 7.0 | 128.0 |
| Transforming growth factor, beta 1 | 1.0 | 128.0 |
| Province of Cremona | 125.0 | 128.0 |
| William Allen | 1.0 | 128.0 |
| SMAD family member 2 | 2.0 | 128.0 |
| Chris Johnson | 1.0 | 127.0 |
| Martin Luther | 67.0 | 127.0 |
| Nová Ves | 68.0 | 127.0 |
| Magic | 35.0 | 127.0 |
| Anna | 2886.0 | 127.0 |
| Paul Jones | 27.0 | 127.0 |
| Alone | 24.0 | 127.0 |
| Andrew Wilson | 3.0 | 127.0 |
| SMAD family member 4 | 2.0 | 127.0 |
| Madame Bovary | 3.0 | 127.0 |
| George Washington | 33.0 | 127.0 |
| Rain | 46.0 | 127.0 |
| Pride | 18.0 | 126.0 |
| Florida | 949.0 | 126.0 |
| Sleeping Beauty | 8.0 | 126.0 |
| Humboldt University of Berlin | 480.0 | 126.0 |
| Night | 7.0 | 126.0 |
| William Miller | 4.0 | 126.0 |
| The River | 24.0 | 126.0 |
| Ulice | 1.0 | 126.0 |
| Richard Taylor | 7.0 | 126.0 |
| B cell leukemia/lymphoma 2 | 1.0 | 126.0 |
| Denmark | 10182.0 | 126.0 |
| Li Ji | 96.0 | 126.0 |
| Paraná | 154.0 | 125.0 |
| John Grant | 11.0 | 125.0 |
| Olympia | 73.0 | 125.0 |
| Touch | 41.0 | 125.0 |
| John O'Neill | 1.0 | 125.0 |
| Smile | 27.0 | 125.0 |
| The Promise | 10.0 | 125.0 |
| Chicago | 4779.0 | 125.0 |
| The Return | 18.0 | 124.0 |
| Transformation related protein 53 | 1.0 | 124.0 |
| Around the World in 80 Days | 1.0 | 124.0 |
| The Truth | 11.0 | 124.0 |
| Stationsweg | 90.0 | 124.0 |
| Gordon Brown | 8.0 | 124.0 |
| Saint George and the Dragon | 50.0 | 124.0 |
| Michael Müller | 2.0 | 124.0 |
| Faust | 23.0 | 124.0 |
| Bone morphogenetic protein 7 | 2.0 | 124.0 |
| Mark Johnson | 43.0 | 124.0 |
| John Doyle | 6.0 | 124.0 |
| Sint-Lambertuskerk | 3.0 | 124.0 |
| The Holy Family | 3.0 | 124.0 |
| Ambachtstraat | 2.0 | 124.0 |
| Turkey | 7195.0 | 123.0 |
| James Jones | 6.0 | 123.0 |
| Jimmy Smith | 24.0 | 123.0 |
| Richard Wilson | 22.0 | 123.0 |
| St. Peter und Paul | 3.0 | 123.0 |
| Province of L'Aquila | 124.0 | 123.0 |
| Fear | 13.0 | 123.0 |
| David Bell | 1.0 | 123.0 |
| Gareth Davies | 3.0 | 123.0 |
| Black and White | 6.0 | 123.0 |
| Solo | 12.0 | 122.0 |
| Wuthering Heights | 21.0 | 122.0 |
| John Black | 6.0 | 122.0 |
| Peter Pan | 6.0 | 122.0 |
| Redemption | 19.0 | 122.0 |
| Halloween | 34.0 | 122.0 |
| Fearless | 18.0 | 122.0 |
| Crush | 21.0 | 122.0 |
| Venice | 3672.0 | 122.0 |
| Portrait of a Lady | 3.0 | 122.0 |
| The Source | 5.0 | 122.0 |
| Mitogen-activated protein kinase 14 | 2.0 | 121.0 |
| John Howard | 38.0 | 121.0 |
| Tony Brown | 1.0 | 121.0 |
| Ceará | 118.0 | 121.0 |
| Berlin | 21839.0 | 121.0 |
| The Turning Point | 3.0 | 121.0 |
| William Watson | 8.0 | 121.0 |
| No Man's Land | 12.0 | 121.0 |
| The Letter | 8.0 | 121.0 |
| Hell | 15.0 | 121.0 |
| Portrait of a Young Man | 2.0 | 121.0 |
| Vengeance | 10.0 | 121.0 |
| Holiday | 16.0 | 121.0 |
| Saint Petersburg | 9117.0 | 121.0 |
| Justice | 14.0 | 121.0 |
| Pinocchio | 6.0 | 121.0 |
| European Union | 117.0 | 120.0 |
| Koningin Julianalaan | 1.0 | 120.0 |
| Rijksstraatweg | 184.0 | 120.0 |
| Sunshine | 28.0 | 120.0 |
| Little Women | 3.0 | 120.0 |
| Everything | 26.0 | 120.0 |
| David Miller | 31.0 | 120.0 |
| Mary | 3424.0 | 120.0 |
| Sherlock Holmes | 23.0 | 120.0 |
| Underground | 17.0 | 120.0 |
| John Hunter | 1.0 | 119.0 |
| John Sullivan | 7.0 | 119.0 |
| Obsession | 13.0 | 119.0 |
| The Judgment of Paris | 2.0 | 119.0 |
| The Great Gatsby | 9.0 | 119.0 |
| Laura | 1063.0 | 119.0 |
| Sappho | 9.0 | 119.0 |
| Saint Sebastian | 34.0 | 119.0 |
| Forkhead box P3 | 2.0 | 119.0 |
| Larry Smith | 11.0 | 119.0 |
| A Tale of Two Cities | 5.0 | 119.0 |
| Winter | 67.0 | 119.0 |
| Harry Potter and the Philosopher's Stone | 4.0 | 118.0 |
| William Russell | 79.0 | 118.0 |
| Ras homolog family member A | 2.0 | 118.0 |
| Drive | 16.0 | 118.0 |
| Gravity | 27.0 | 118.0 |
| Julia | 803.0 | 118.0 |
| The Adoration of the Shepherds | 8.0 | 117.0 |
| Crime and Punishment | 3.0 | 117.0 |
| Michael Brown | 3.0 | 117.0 |
| Wanted | 7.0 | 117.0 |
| The Chase | 14.0 | 117.0 |
| Ecce Homo | 14.0 | 117.0 |
| Sanctuary | 35.0 | 117.0 |
| Marie Antoinette | 24.0 | 117.0 |
| Spellbound | 6.0 | 117.0 |
| Heat | 11.0 | 117.0 |
| Province of Messina | 115.0 | 117.0 |
| Frankenstein | 19.0 | 117.0 |
| Flora | 127.0 | 117.0 |
| Province of Caserta | 113.0 | 117.0 |
| John Ferguson | 1.0 | 116.0 |
| William Hamilton | 1.0 | 116.0 |
| Brother's Keeper | 3.0 | 116.0 |
| John King | 7.0 | 116.0 |
| John Graham | 3.0 | 116.0 |
| A Midsummer Night's Dream | 10.0 | 116.0 |
| Great Expectations | 8.0 | 115.0 |
| The Bridge | 12.0 | 115.0 |
| Chris Williams | 7.0 | 115.0 |
| Gary Smith | 2.0 | 115.0 |
| Province of Padua | 112.0 | 115.0 |
| Vladimir Smirnov | 7.0 | 115.0 |
| Province of Chieti | 113.0 | 115.0 |
| John Hamilton | 14.0 | 115.0 |
| Still life | 11.0 | 115.0 |
| Andrew Miller | 12.0 | 115.0 |
| Sonic hedgehog signaling molecule | 1.0 | 115.0 |
| John Ellis | 1.0 | 115.0 |
| Li Xian | 105.0 | 115.0 |
| Eva | 1075.0 | 115.0 |
| North Macedonia | 766.0 | 114.0 |
| Lille metropolis | 135.0 | 114.0 |
| Rac family small GTPase 1 | 2.0 | 114.0 |
| One Love | 34.0 | 114.0 |
| James Williams | 6.0 | 114.0 |
| King Kong | 4.0 | 114.0 |
| province of Potenza | 111.0 | 114.0 |
| Brian Johnson | 8.0 | 114.0 |
| The Collection | 29.0 | 114.0 |
| tumor protein p53 | 1.0 | 114.0 |
| Raadhuislaan | 6.0 | 114.0 |
| John Harvey | 8.0 | 114.0 |
| Firefly | 42.0 | 114.0 |
| Quartet | 12.0 | 114.0 |
| Otto Schmidt | 5.0 | 114.0 |
| Wonderland | 13.0 | 114.0 |
| Otto Meyer | 6.0 | 114.0 |
| Brothers | 11.0 | 114.0 |
| Godzilla | 9.0 | 114.0 |
| Nemesis | 20.0 | 114.0 |
| Uranus | 77.0 | 113.0 |
| Pygmalion and Galatea | 2.0 | 113.0 |
| Moscow | 20802.0 | 113.0 |
| Milan | 8132.0 | 113.0 |
| Taxi | 21.0 | 113.0 |
| James Hamilton | 6.0 | 113.0 |
| Enigma | 33.0 | 113.0 |
| Madrid | 3594.0 | 113.0 |
| Victory | 19.0 | 113.0 |
| Midnight | 6.0 | 113.0 |
| Sweden | 37293.0 | 113.0 |
| Calreticulin | 2.0 | 112.0 |
| Dave Brown | 2.0 | 112.0 |
| Guilty | 25.0 | 112.0 |
| Province of Verona | 107.0 | 112.0 |
| Charlie Brown | 5.0 | 112.0 |
| The Gift | 24.0 | 112.0 |
| The Ten Commandments | 1.0 | 112.0 |
| Marco Polo | 14.0 | 112.0 |
| Boomerang | 6.0 | 112.0 |
| Butterfly | 44.0 | 111.0 |
| Crossroads | 10.0 | 111.0 |
| Lost | 158.0 | 111.0 |
| X | 39.0 | 111.0 |
| Li Mou | 98.0 | 111.0 |
| Ride | 17.0 | 111.0 |
| Tony Martin | 29.0 | 111.0 |
| Bad Company | 12.0 | 111.0 |
| Out of the Blue | 13.0 | 111.0 |
| John Spencer | 19.0 | 111.0 |
| Winter Landscape | 6.0 | 111.0 |
| Princess Changshan | 91.0 | 111.0 |
| Karl Fischer | 25.0 | 111.0 |
| Kerkpad | 52.0 | 111.0 |
| The Island | 3.0 | 111.0 |
| Eclipse | 19.0 | 111.0 |
| Bob Smith | 1.0 | 111.0 |
| CD36 molecule | 2.0 | 111.0 |
| Jim Brown | 29.0 | 111.0 |
| Bliss | 9.0 | 111.0 |
| Tour de France | 1733.0 | 110.0 |
| Revolution | 25.0 | 110.0 |
| Lisbon | 1443.0 | 110.0 |
| Luxembourg | 2111.0 | 110.0 |
| George Martin | 60.0 | 110.0 |
| Tokyo | 5766.0 | 110.0 |
| William Davies | 5.0 | 110.0 |
| The Spoilers | 4.0 | 110.0 |
| Peter Schneider | 10.0 | 110.0 |
| Quo Vadis | 3.0 | 110.0 |
| John Cale | 209.0 | 110.0 |
| Orpheus | 27.0 | 110.0 |
| Paired box 6 | 2.0 | 110.0 |
| Madonna and Child with Saints | 1.0 | 110.0 |
| Dorpstraat | 84.0 | 109.0 |
| Caveolin 3 | 2.0 | 109.0 |
| John Lloyd | 11.0 | 109.0 |
| Jack White | 40.0 | 109.0 |
| Truth | 15.0 | 109.0 |
| Home Sweet Home | 6.0 | 109.0 |
| Tom Sawyer | 8.0 | 109.0 |
| The Intruder | 2.0 | 109.0 |
| Ophelia | 15.0 | 109.0 |
| Q17144373 | 1.0 | 109.0 |
| Molière | 9.0 | 109.0 |
| Robert Johnson | 7.0 | 109.0 |
| Frank Williams | 5.0 | 109.0 |
| Hermann Müller | 1.0 | 109.0 |
| David James | 6.0 | 109.0 |
| Always | 19.0 | 109.0 |
| Charles Williams | 16.0 | 108.0 |
| Jupiter | 145.0 | 108.0 |
| Franklin County | 146.0 | 108.0 |
| Seine | 304.0 | 108.0 |
| John Hayes | 6.0 | 108.0 |
| Rembrandt | 241.0 | 108.0 |
| John Ryan | 2.0 | 108.0 |
| Würzburg | 1269.0 | 108.0 |
| Richard Müller | 4.0 | 108.0 |
| The Scapegoat | 3.0 | 108.0 |
| Territoire de Belfort | 105.0 | 107.0 |
| The Last Supper | 8.0 | 107.0 |
| Torenstraat | 87.0 | 107.0 |
| Peter Jones | 7.0 | 107.0 |
| Paul | 10558.0 | 107.0 |
| Happiness | 20.0 | 107.0 |
| BCL2 apoptosis regulator | 1.0 | 107.0 |
| Henry Williams | 4.0 | 107.0 |
| James Young | 79.0 | 107.0 |
| Vendetta | 8.0 | 107.0 |
| Mike Johnson | 3.0 | 106.0 |
| Province of Treviso | 101.0 | 106.0 |
| Frozen | 16.0 | 106.0 |
| 2013 Bilderberg Conference | 2.0 | 106.0 |
| St. Peter | 17.0 | 106.0 |
| Black Widow | 6.0 | 106.0 |
| Crucifixion of Christ | 6.0 | 106.0 |
| People's Republic of China | 52849.0 | 106.0 |
| Down to Earth | 13.0 | 106.0 |
| Uncle Tom's Cabin | 4.0 | 106.0 |
| Seven | 24.0 | 106.0 |
| Sam Smith | 5.0 | 106.0 |
| Valencia | 544.0 | 106.0 |
| John Collins | 2.0 | 106.0 |
| Bill White | 1.0 | 105.0 |
| Province of Lecce | 101.0 | 105.0 |
| Jason Smith | 2.0 | 105.0 |
| Asylum | 10.0 | 105.0 |
| Waterloo | 102.0 | 105.0 |
| Broken | 17.0 | 105.0 |
| Jeff Smith | 26.0 | 105.0 |
| Province of Reggio Calabria | 100.0 | 105.0 |
| Peter Jackson | 59.0 | 105.0 |
| Prins Willem-Alexanderstraat | 1.0 | 105.0 |
| Michael Green | 5.0 | 105.0 |
| Lola | 108.0 | 105.0 |
| Captain Blood | 2.0 | 105.0 |
| Tomorrow | 23.0 | 105.0 |
| London | 13631.0 | 105.0 |
| Aquaporin 1 | 2.0 | 105.0 |
| Helen of Troy | 21.0 | 105.0 |
| Lincoln County | 90.0 | 104.0 |
| Washington | 562.0 | 104.0 |
| William Gibson | 35.0 | 104.0 |
| Jim Miller | 1.0 | 104.0 |
| George Baker | 22.0 | 104.0 |
| Memories | 15.0 | 104.0 |
| Scott Smith | 14.0 | 104.0 |
| Thor | 154.0 | 104.0 |
| The Message | 15.0 | 104.0 |
| Echo | 76.0 | 104.0 |
| The Key | 6.0 | 104.0 |
| Virus | 10.0 | 104.0 |
| William Johnson | 2.0 | 104.0 |
| Mexico | 11600.0 | 104.0 |
| Ring | 64.0 | 103.0 |
| Revenge | 24.0 | 103.0 |
| John Morris | 6.0 | 103.0 |
| Bobby Brown | 23.0 | 103.0 |
| Runaway | 16.0 | 103.0 |
| Max Weber | 14.0 | 103.0 |
| The Trial | 3.0 | 103.0 |
| Scott Brown | 1.0 | 103.0 |
| Billy Taylor | 7.0 | 103.0 |
| Molendijk | 47.0 | 103.0 |
| Stars | 19.0 | 103.0 |
| John Stewart | 14.0 | 103.0 |
| Bacchus and Ariadne | 4.0 | 103.0 |
| Breathe | 27.0 | 103.0 |
| Jealousy | 12.0 | 103.0 |
| Province of Naples | 102.0 | 103.0 |
| David Taylor | 1.0 | 103.0 |
| John Foster | 2.0 | 103.0 |
| Self portrait | 7.0 | 103.0 |
| Steve Johnson | 4.0 | 102.0 |
| Athens | 2561.0 | 102.0 |
| Michael Williams | 21.0 | 102.0 |
| Thailand | 4096.0 | 102.0 |
| Province of Frosinone | 100.0 | 102.0 |
| Reckless | 6.0 | 102.0 |
| Nativity | 5.0 | 102.0 |
| Li Tan | 89.0 | 102.0 |
| The Lost World | 8.0 | 102.0 |
| Still Life with Flowers | 6.0 | 102.0 |
| She | 16.0 | 102.0 |
| Superstar | 12.0 | 102.0 |
| Michael | 9122.0 | 102.0 |
| John Morgan | 10.0 | 102.0 |
| Richard III | 6.0 | 102.0 |
| James Miller | 5.0 | 102.0 |
| Steve Brown | 1.0 | 102.0 |
| Summertime | 16.0 | 102.0 |
| Spider-Man | 8.0 | 102.0 |
| Valentine | 103.0 | 101.0 |
| Breathless | 9.0 | 101.0 |
| Sugar | 10.0 | 101.0 |
| Head of a Woman | 1.0 | 101.0 |
| Stella | 199.0 | 101.0 |
| Province of Vercelli | 97.0 | 101.0 |
| Music | 20.0 | 101.0 |
| You | 30.0 | 101.0 |
| The Raising of Lazarus | 3.0 | 101.0 |
| Eric Johnson | 14.0 | 101.0 |
| Thomas Wilson | 1.0 | 101.0 |
| The Hole | 4.0 | 101.0 |
| Hans Weber | 1.0 | 101.0 |
| Colorado | 374.0 | 101.0 |
| Reflections | 21.0 | 101.0 |
| William Hunter | 2.0 | 101.0 |
| Roma | 32.0 | 101.0 |
| Blood Brothers | 13.0 | 101.0 |
| Liu Yan | 65.0 | 101.0 |
| Apolipoprotein B | 2.0 | 100.0 |
| Bern-Mittelland administrative district | 98.0 | 100.0 |
| The Hunter | 6.0 | 100.0 |
| Red | 249.0 | 100.0 |
| GeGeGe no Kitarō | 1.0 | 100.0 |
| Ich bin ein Star – Holt mich hier raus! | 95.0 | 100.0 |
| Province of Lecco | 97.0 | 100.0 |
| Israel | 4309.0 | 100.0 |
| The Singles | 22.0 | 100.0 |
| Azerbaijan | 1276.0 | 100.0 |
| Demons | 19.0 | 100.0 |
| Faith | 67.0 | 100.0 |
| St. Laurentius | 2.0 | 100.0 |
| Florence | 4854.0 | 100.0 |
| Ben Jones | 1.0 | 100.0 |
| Unity | 145.0 | 100.0 |
| Change | 34.0 | 100.0 |
| Li Sui | 90.0 | 100.0 |
| Michael Wilson | 11.0 | 100.0 |
| Madonna of Humility | 3.0 | 100.0 |
| Huntingtin | 2.0 | 100.0 |
| Wings | 52.0 | 99.0 |
| Casablanca | 396.0 | 99.0 |
| Li Xuan | 90.0 | 99.0 |
| Alfred Schmidt | 16.0 | 99.0 |
| Antony and Cleopatra | 1.0 | 99.0 |
| Li Wan | 87.0 | 99.0 |
| The Good Life | 12.0 | 99.0 |
| Bruno | 1963.0 | 99.0 |
| Province of Novara | 95.0 | 99.0 |
| Insomnia | 15.0 | 99.0 |
| Charles Martin | 2.0 | 99.0 |
| Adenosine A1 receptor | 2.0 | 99.0 |
| The Scarlet Letter | 2.0 | 99.0 |
| Fanny | 201.0 | 99.0 |
| The Visitor | 10.0 | 99.0 |
| David Hall | 2.0 | 99.0 |
| Li County | 92.0 | 99.0 |
| Ken Jones | 2.0 | 99.0 |
| Romance | 35.0 | 99.0 |
| Q4611255 | 98.0 | 99.0 |
| Lucky | 23.0 | 99.0 |
| Paul Robinson | 1.0 | 99.0 |
| Conan the Barbarian | 105.0 | 99.0 |
| Once Upon a Time | 79.0 | 98.0 |
| Richard Martin | 16.0 | 98.0 |
| equestrian statue of Joan of Arc | 9.0 | 98.0 |
| The Prince and the Pauper | 7.0 | 98.0 |
| Underworld | 42.0 | 98.0 |
| Max Müller | 1.0 | 98.0 |
| Mitogen-activated protein kinase 9 | 2.0 | 98.0 |
| San Antonio | 490.0 | 98.0 |
| Gerhard Fischer | 2.0 | 98.0 |
| Slatina | 71.0 | 98.0 |
| Istanbul | 1842.0 | 98.0 |
| Netherlands | 115585.0 | 98.0 |
| Remember Me | 19.0 | 98.0 |
| Adam Smith | 4.0 | 98.0 |
| Princess Yanguo | 84.0 | 98.0 |
| Girl | 191.0 | 98.0 |
| Carrie | 103.0 | 98.0 |
| Child's Play | 3.0 | 98.0 |
| Gone | 17.0 | 98.0 |
| Province of Oristano | 93.0 | 98.0 |
| M | 10.0 | 98.0 |
| John Thomson | 4.0 | 98.0 |
| Princess Shouchun | 86.0 | 98.0 |
| The Wall | 11.0 | 98.0 |
| Ali | 58.0 | 98.0 |
| Trinity | 165.0 | 98.0 |
| Arizona | 337.0 | 98.0 |
| Taken | 6.0 | 98.0 |
| Cyrano de Bergerac | 3.0 | 98.0 |
| Pygmalion | 16.0 | 97.0 |
| Spartacus | 13.0 | 97.0 |
| Hans Meyer | 31.0 | 97.0 |
| Hello | 28.0 | 97.0 |
| Tonight | 26.0 | 97.0 |
| Endgame | 8.0 | 97.0 |
| Forkhead box C2 | 2.0 | 97.0 |
| Legend | 10.0 | 97.0 |
| John Henderson | 7.0 | 97.0 |
| Coming Home | 14.0 | 97.0 |
| Andrew Brown | 1.0 | 97.0 |
| The Circle | 14.0 | 97.0 |
| Freital | 87.0 | 97.0 |
| Penitent Magdalene | 64.0 | 97.0 |
| Matt Smith | 11.0 | 97.0 |
| Santiago | 1616.0 | 97.0 |
| Georg Müller | 3.0 | 97.0 |
| Winston Churchill | 30.0 | 97.0 |
| Napoleon | 133.0 | 97.0 |
| Fame | 6.0 | 97.0 |
| Alan Smith | 1.0 | 97.0 |
| Mission: Impossible | 16.0 | 97.0 |
| William Robertson | 1.0 | 97.0 |
| John O'Brien | 2.0 | 97.0 |
| Bloodline | 13.0 | 96.0 |
| Maria | 3690.0 | 96.0 |
| Autumn | 19.0 | 96.0 |
| Dangerous | 13.0 | 96.0 |
| David Watson | 1.0 | 96.0 |
| Caravaggio | 139.0 | 96.0 |
| Ernst Meyer | 7.0 | 96.0 |
| Tattoo | 16.0 | 96.0 |
| Accident | 5.0 | 96.0 |
| Kevin Smith | 51.0 | 96.0 |
| Brink | 67.0 | 96.0 |
| Eve | 183.0 | 96.0 |
| Restless | 15.0 | 96.0 |
| John Kerr | 13.0 | 96.0 |
| province of Campobasso | 91.0 | 96.0 |
| Vladimir Popov | 2.0 | 96.0 |
| Samson and Delilah | 1.0 | 96.0 |
| Journey to the Center of the Earth | 1.0 | 95.0 |
| The Storm | 3.0 | 95.0 |
| Chris Taylor | 13.0 | 95.0 |
| Someday | 28.0 | 95.0 |
| Three | 16.0 | 95.0 |
| Jim Smith | 1.0 | 95.0 |
| The True Benjamin Franklin | 17.0 | 95.0 |
| William Marshall | 44.0 | 95.0 |
| The Miracle | 7.0 | 95.0 |
| John Douglas | 15.0 | 95.0 |
| Goiás | 114.0 | 95.0 |
| Jefferson County | 144.0 | 95.0 |
| Bethlehem | 167.0 | 95.0 |
| Déjà Vu | 7.0 | 95.0 |
| Chris Martin | 12.0 | 95.0 |
| The Abduction of Europa | 4.0 | 95.0 |
| Saint Jerome | 3.0 | 95.0 |
| Doris | 373.0 | 95.0 |
| Changes | 24.0 | 95.0 |
| Earth | 72.0 | 95.0 |
| Maya | 158.0 | 95.0 |
| Fireworks | 14.0 | 95.0 |
| After Hours | 21.0 | 94.0 |
| John Lynch | 37.0 | 94.0 |
| The Last of the Mohicans | 11.0 | 94.0 |
| Miracle | 23.0 | 94.0 |
| Democratic Party | 21099.0 | 94.0 |
| Homecoming | 18.0 | 94.0 |
| Edward Jones | 2.0 | 94.0 |
| Daybreak | 12.0 | 94.0 |
| Henry Johnson | 1.0 | 94.0 |
| Masquerade | 10.0 | 94.0 |
| Brian Jones | 4.0 | 94.0 |
| Emperor Taizong of Tang | 86.0 | 94.0 |
| The Virgin and Child | 2.0 | 94.0 |
| Jacques Martin | 44.0 | 94.0 |
| Miami | 874.0 | 94.0 |
| Show Boat | 3.0 | 94.0 |
| Iron Man | 9.0 | 94.0 |
| Trouble | 23.0 | 94.0 |
| The Fall of Man | 1.0 | 94.0 |
| Metropolis | 27.0 | 94.0 |
| Sunrise | 39.0 | 94.0 |
| Another World | 11.0 | 94.0 |
| John Clark | 12.0 | 94.0 |
| The Prisoner of Zenda | 2.0 | 93.0 |
| Province of Palermo | 90.0 | 93.0 |
| Lolita | 49.0 | 93.0 |
| Trust | 14.0 | 93.0 |
| I Want You | 23.0 | 93.0 |
| Youth | 3.0 | 93.0 |
| Pure | 20.0 | 93.0 |
| Henry Jones | 23.0 | 93.0 |
| Lucretia | 24.0 | 93.0 |
| David Copperfield | 5.0 | 93.0 |
| Josef Müller | 1.0 | 93.0 |
| John McCarthy | 1.0 | 93.0 |
| George Thompson | 2.0 | 93.0 |
| John Ball | 3.0 | 93.0 |
Some closer inspection gives the explanation to the high in-degrees. The top entitites are things like "human", "male", "female" and "politican". It makes sense that many entities would satisfy relations such as "entity is human" or "entity is femmale", resulting in the high in-degrees. Interestingly, we note that the in-degree for "male" is over 5 times higher than that of "female", indicating a high gender discrepancy in terms of the people represented in the dataset. We also find the entity "United States of Amerika" very high in the list, which is likely due to many other entities being physically in or in other ways related to America. A bit further down other countries and territories can be found, such as "Germany" and "Italy".
The entities with highest out-degrees show no obvious interpretation. We find a diverse mix of streets, buildings, people, books and other things. Our hypothesis is that these are simply items that someone has chosen to add much information about in the Wikidata knowledge base, resulting in high out-degrees.
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
val inDegrees = graph.inDegrees
val outDegrees = graph.outDegrees
val degrees = inDegrees.join(outDegrees, "id").cache()
display(degrees)
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksViewb40e4bb")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksViewb40e4bb) ,min_max AS (SELECT `outDegree`,(SELECT MAX(`outDegree`) FROM q) `target_column_max`,(SELECT MIN(`outDegree`) FROM q) `target_column_min` FROM q) ,histogram_meta AS (SELECT `outDegree`,`target_column_min` `min_value`,IF(`target_column_max` = `target_column_min`,`target_column_max` + 1,`target_column_max`) `max_value`,(`target_column_max` - `target_column_min`) / 300 `step` FROM min_max) SELECT IF(ISNULL(`outDegree`),NULL,LEAST(WIDTH_BUCKET(`outDegree`,`min_value`,`max_value`,300),300)) `outDegree_BIN`,FIRST(`min_value` + ((IF(ISNULL(`outDegree`),NULL,LEAST(WIDTH_BUCKET(`outDegree`,`min_value`,`max_value`,300),300)) - 1) * `step`)) `outDegree_BIN_LOWER_BOUND`,FIRST(`step`) `outDegree_BIN_STEP`,COUNT(`outDegree`) `COUNT` FROM histogram_meta GROUP BY `outDegree_BIN`"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksViewb40e4bb")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
Edge Relations
Let us now take a look at the edges, and corresponding relations. We can count and histogram the different relations associated with edges as:
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
val inDegrees = graph.inDegrees
val outDegrees = graph.outDegrees
val degrees = inDegrees.join(outDegrees, "id").cache()
display(degrees)
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksView30dd7fb")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksView30dd7fb) ,min_max AS (SELECT `inDegree`,(SELECT MAX(`inDegree`) FROM q) `target_column_max`,(SELECT MIN(`inDegree`) FROM q) `target_column_min` FROM q) ,histogram_meta AS (SELECT `inDegree`,`target_column_min` `min_value`,IF(`target_column_max` = `target_column_min`,`target_column_max` + 1,`target_column_max`) `max_value`,(`target_column_max` - `target_column_min`) / 300 `step` FROM min_max) SELECT IF(ISNULL(`inDegree`),NULL,LEAST(WIDTH_BUCKET(`inDegree`,`min_value`,`max_value`,300),300)) `inDegree_BIN`,FIRST(`min_value` + ((IF(ISNULL(`inDegree`),NULL,LEAST(WIDTH_BUCKET(`inDegree`,`min_value`,`max_value`,300),300)) - 1) * `step`)) `inDegree_BIN_LOWER_BOUND`,FIRST(`step`) `inDegree_BIN_STEP`,COUNT(`inDegree`) `COUNT` FROM histogram_meta GROUP BY `inDegree_BIN`"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksView30dd7fb")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
val topTenTypes = typeCounts.limit(10)
//val typeFiltered = joinedTypes.join(topTenTypes, joinedTypes.col("type") === topTenTypes.col("type"), "inner")
val typeFiltered = joinedTypes.join(topTenTypes, List("type"), "inner")
display(typeFiltered)
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksView6239619")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksView6239619) SELECT `inDegree`,`outDegree`,`type` FROM q"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksView6239619")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
val relCounts = graph.edges.groupBy("rel").count().cache() // Cahce to reuse later
display(relCounts)
| rel | count |
|---|---|
| godparent | 22.0 |
| interaction | 49.0 |
| part of | 61969.0 |
| molecular function | 9159.0 |
| disease transmission process | 1.0 |
| place served by transport hub | 64.0 |
| playing hand | 2263.0 |
| IUCN protected areas category | 1453.0 |
| topic's main Wikimedia portal | 620.0 |
| family name | 94463.0 |
| parent astronomical body | 498.0 |
| general manager | 4.0 |
| end cause | 2.0 |
| shares border with | 204230.0 |
| mushroom cap shape | 793.0 |
| based on | 5856.0 |
| present in work | 4068.0 |
| public holiday | 258.0 |
| separated from | 23.0 |
| filmography | 47.0 |
| standards body | 22.0 |
| represented by | 13.0 |
| honorific suffix | 4.0 |
| track gauge | 856.0 |
| guest of honor | 2.0 |
| topic's main category | 1397.0 |
| writing system | 481.0 |
| sexual orientation | 3020.0 |
| industry | 2765.0 |
| father | 43248.0 |
| given name version for other gender | 254.0 |
| academic minor | 1.0 |
| applies to jurisdiction | 912.0 |
| worshipped by | 245.0 |
| crystal system | 369.0 |
| performer | 93406.0 |
| business division | 231.0 |
| place of burial | 31215.0 |
| influenced by | 310.0 |
| this taxon is source of | 48.0 |
| discovery method | 20.0 |
| fictional universe described in | 84.0 |
| developer | 14445.0 |
| head of state | 676.0 |
| notation | 3.0 |
| PEGI rating | 2758.0 |
| depicts | 55770.0 |
| currency | 607.0 |
| ESRB rating | 3292.0 |
| binding of software library | 1.0 |
| replaced synonym (for nom. nov.) | 9.0 |
| crystal habit | 1.0 |
| armament | 2156.0 |
| fictional or mythical analog of | 156.0 |
| basic form of government | 518.0 |
| electoral district | 6.0 |
| producer | 41151.0 |
| shape | 75.0 |
| taxon synonym | 173.0 |
| highest judicial authority | 26.0 |
| located in or next to body of water | 841.0 |
| replaced by | 717.0 |
| part of the series | 24395.0 |
| Eight Banner register | 173.0 |
| after a work by | 1.0 |
| measured physical quantity | 24.0 |
| interleaves with | 28.0 |
| participant | 9805.0 |
| narrative location | 16782.0 |
| lake on watercourse | 72.0 |
| recorded at studio or venue | 94.0 |
| place of origin (Switzerland) | 1705.0 |
| transport network | 13744.0 |
| capital of | 215.0 |
| official language | 2957.0 |
| list related to category | 124.0 |
| airline alliance | 135.0 |
| avionics | 62.0 |
| location of landing | 10.0 |
| collection | 27772.0 |
| characters | 2827.0 |
| donated by | 62.0 |
| film editor | 887.0 |
| executive producer | 152.0 |
| chivalric order | 1.0 |
| structure replaced by | 32.0 |
| owner of | 14.0 |
| presynaptic connection | 1.0 |
| has part(s) | 32359.0 |
| located in the administrative territorial entity | 404421.0 |
| employer | 79779.0 |
| hair color | 382.0 |
| sponsor | 52.0 |
| chairperson | 2389.0 |
| cathedral | 24.0 |
| place of birth | 680780.0 |
| lyrics by | 3742.0 |
| has seal, badge, or sigil | 2.0 |
| located on street | 40345.0 |
| instance of | 2558406.0 |
| subclass of | 47185.0 |
| structural engineer | 361.0 |
| exclave of | 90.0 |
| points/goal scored by | 69.0 |
| named after | 21854.0 |
| mother house | 220.0 |
| maintained by | 12932.0 |
| officially opened by | 135.0 |
| rector | 205.0 |
| country of origin | 70182.0 |
| medical condition | 3463.0 |
| carries scientific instrument | 4.0 |
| original combination | 205.0 |
| CPU | 440.0 |
| airline hub | 484.0 |
| has facet polytope | 849.0 |
| site of astronomical discovery | 38087.0 |
| licensed to broadcast to | 32.0 |
| consecrator | 91.0 |
| instrumentation | 79.0 |
| prosecutor | 4.0 |
| category related to list | 124.0 |
| has vertex figure | 3.0 |
| handedness | 686.0 |
| medical examination | 29.0 |
| Code of nomenclature | 750.0 |
| list of characters | 2.0 |
| composer | 4556.0 |
| encoded by | 1903.0 |
| allegiance | 232.0 |
| main building contractor | 422.0 |
| organizer | 273.0 |
| translator | 330.0 |
| occupant | 5114.0 |
| represents | 4.0 |
| contributing factor of | 1.0 |
| place of death | 324923.0 |
| political alignment | 43.0 |
| programmer | 87.0 |
| solved by | 2.0 |
| relative | 1800.0 |
| legislated by | 66.0 |
| physically interacts with | 4.0 |
| member of sports team | 339865.0 |
| director | 79861.0 |
| category's main topic | 1009.0 |
| category of associated people | 842.0 |
| introduced feature | 2.0 |
| spouse | 31456.0 |
| author | 31882.0 |
| basin country | 148.0 |
| sex or gender | 1512569.0 |
| position played on team / speciality | 13048.0 |
| codomain | 6.0 |
| located in/on physical feature | 4245.0 |
| foundational text | 37.0 |
| choreographer | 3.0 |
| director of photography | 26666.0 |
| powered by | 1108.0 |
| language of work or name | 8963.0 |
| patron saint | 1186.0 |
| record label | 89527.0 |
| pendant of | 168.0 |
| list of works | 8.0 |
| from narrative universe | 3615.0 |
| proved by | 7.0 |
| position held | 221291.0 |
| diocese | 2560.0 |
| ortholog | 1850.0 |
| home port | 96.0 |
| endemic to | 289.0 |
| lifestyle | 510.0 |
| docking port | 1.0 |
| category combines topics | 22906.0 |
| day in year for periodic occurrence | 204.0 |
| conferred by | 347.0 |
| postsynaptic connection | 1.0 |
| cover art by | 174.0 |
| has pet | 8.0 |
| archives at | 437.0 |
| game mode | 22321.0 |
| diplomatic relation | 582.0 |
| found in taxon | 3891.0 |
| office held by head of government | 1904.0 |
| IUCN conservation status | 3532.0 |
| sport | 57137.0 |
| Wikimedia portal's main topic | 622.0 |
| native language | 4158.0 |
| has contributing factor | 9.0 |
| family | 6276.0 |
| child astronomical body | 426.0 |
| country of citizenship | 1260348.0 |
| mother | 17271.0 |
| adjacent station | 25394.0 |
| location of creation | 1005.0 |
| companion of | 24.0 |
| central bank/issuer | 31.0 |
| imported from Wikimedia project | 700.0 |
| noble title | 3966.0 |
| replaces | 528.0 |
| has facility | 84.0 |
| Unknown | 101919.0 |
| inflows | 300.0 |
| cause of death | 22861.0 |
| inspired by | 325.0 |
| designed by | 2862.0 |
| student of | 2231.0 |
| location of formation | 463.0 |
| readable file format | 23.0 |
| commissioned by | 475.0 |
| has natural reservoir | 2.0 |
| overlies | 77.0 |
| religion or worldview | 26968.0 |
| has immediate cause | 26.0 |
| target | 14.0 |
| coat of arms | 56.0 |
| ethnic group | 8278.0 |
| programmed in | 676.0 |
| input device | 4549.0 |
| statement is subject of | 156.0 |
| country for sport | 18.0 |
| origin of the watercourse | 151.0 |
| captain | 17.0 |
| used by | 18.0 |
| list of episodes | 3.0 |
| voice actor | 1124.0 |
| main regulatory text | 156.0 |
| capital | 14397.0 |
| academic degree | 20284.0 |
| source of energy | 122.0 |
| minor planet group | 41241.0 |
| contains settlement | 5981.0 |
| founded by | 3641.0 |
| surface played on | 362.0 |
| member of | 59565.0 |
| librettist | 246.0 |
| tracklist | 663.0 |
| activating neurotransmitter | 2.0 |
| instruction set | 15.0 |
| review score by | 1.0 |
| notable work | 15863.0 |
| lake outflow | 291.0 |
| has quality | 40.0 |
| taxon rank | 118537.0 |
| tributary | 358.0 |
| official residence | 183.0 |
| constellation | 357.0 |
| continent | 4025.0 |
| follows | 173742.0 |
| valid in period | 18.0 |
| script directionality | 1.0 |
| feast day | 631.0 |
| stipe character | 628.0 |
| terminus location | 664.0 |
| discoverer or inventor | 50122.0 |
| head coach | 2150.0 |
| distribution format | 10388.0 |
| movement | 8837.0 |
| said to be the same as | 15180.0 |
| applies to part | 11.0 |
| office contested | 23.0 |
| terminus | 2557.0 |
| owned by | 22260.0 |
| manifestation of | 11.0 |
| charge | 3.0 |
| edition or translation of | 673.0 |
| speaker | 7.0 |
| definition domain | 5.0 |
| mouth of the watercourse | 4643.0 |
| field of work | 7997.0 |
| launch contractor | 4.0 |
| student | 583.0 |
| architectural style | 6956.0 |
| Fach | 241.0 |
| central bank | 7.0 |
| proxy | 10.0 |
| plaintiff | 2.0 |
| addressee | 5.0 |
| scheduled service destination | 65.0 |
| possible treatment | 4.0 |
| cast member | 554674.0 |
| educated at | 249966.0 |
| described by source | 27746.0 |
| chief operating officer | 4.0 |
| immediate cause of | 1.0 |
| curator | 3.0 |
| ancestral home | 117.0 |
| determination method | 3.0 |
| workshop of | 1.0 |
| decays to | 4180.0 |
| home venue | 3688.0 |
| item operated | 2692.0 |
| connecting line | 6901.0 |
| space tug | 7.0 |
| category for people who died here | 8029.0 |
| natural reservoir of | 1.0 |
| software engine | 1986.0 |
| candidate | 161.0 |
| approved by | 14.0 |
| soundtrack release | 32.0 |
| voice type | 8814.0 |
| fuel system | 1.0 |
| copyright license | 2673.0 |
| commemorates | 63.0 |
| depicted by | 49.0 |
| illustrator | 1544.0 |
| catalog | 97.0 |
| kinship to subject | 2.0 |
| has use | 3254.0 |
| architect | 7108.0 |
| enclave within | 140.0 |
| parent taxon | 113406.0 |
| residence | 3718.0 |
| has subsidiary | 295.0 |
| honorific prefix | 113.0 |
| defendant | 2.0 |
| crosses | 1392.0 |
| country | 412626.0 |
| brand | 25.0 |
| IMA status and/or rank | 590.0 |
| shooting handedness | 7.0 |
| headquarters location | 6228.0 |
| editor | 512.0 |
| torch lit by | 47.0 |
| distributed by | 1294.0 |
| has edition or translation | 1106.0 |
| CERO rating | 1301.0 |
| home world | 16.0 |
| category for films shot at this location | 1184.0 |
| league | 6067.0 |
| color | 526.0 |
| encodes | 1902.0 |
| original language of film or TV show | 84080.0 |
| doctoral advisor | 632.0 |
| spore print color | 688.0 |
| top-level Internet domain | 23.0 |
| located on linear feature | 5.0 |
| mascot | 18.0 |
| discography | 28.0 |
| defender | 1.0 |
| dedicated to | 290.0 |
| USK rating | 1637.0 |
| award received | 257410.0 |
| penalty | 174.0 |
| GSRR rating | 3.0 |
| opposite of | 1418.0 |
| topic's main template | 2.0 |
| NATO code for grade | 19.0 |
| radio format | 3.0 |
| unveiled by | 5.0 |
| filming location | 12856.0 |
| port of registry | 39.0 |
| afflicts | 21.0 |
| template has topic | 3.0 |
| military rank | 4735.0 |
| territory claimed by | 34.0 |
| partially coincident with | 571.0 |
| flag | 83.0 |
| point group | 79.0 |
| winner | 954.0 |
| destination point | 99.0 |
| stock exchange | 944.0 |
| child | 60412.0 |
| engine configuration | 273.0 |
| parent of this hybrid, breed, or cultivar | 6.0 |
| convicted of | 1611.0 |
| space group | 395.0 |
| category for people born here | 296.0 |
| stated in | 35.0 |
| space launch vehicle | 312.0 |
| tonality | 183.0 |
| oath made by | 67.0 |
| diplomatic mission sent | 184.0 |
| referee | 87.0 |
| location | 76268.0 |
| is pollinated by | 1.0 |
| eye color | 728.0 |
| foods traditionally associated | 4.0 |
| of | 4.0 |
| is pollinator of | 1.0 |
| guidance system | 156.0 |
| vice-county | 1.0 |
| production company | 23046.0 |
| natural product of taxon | 48.0 |
| family name identical to this given name | 349.0 |
| GUI toolkit or framework | 92.0 |
| twinning | 4.0 |
| party chief representative | 22.0 |
| motto | 3.0 |
| military casualty classification | 7.0 |
| member of political party | 168715.0 |
| hymenium attachment | 851.0 |
| connecting service | 1156.0 |
| tempo marking | 10.0 |
| symptoms and signs | 53.0 |
| vessel class | 1486.0 |
| ammunition | 383.0 |
| language regulatory body | 29.0 |
| depends on software | 7.0 |
| undercarriage | 72.0 |
| input set | 3.0 |
| taxonomic type | 302.0 |
| military branch | 25210.0 |
| judge | 1.0 |
| primary destinations | 68.0 |
| including | 10.0 |
| head of government | 7886.0 |
| type of orbit | 281.0 |
| given name | 1301107.0 |
| located in time zone | 34076.0 |
| structure replaces | 9.0 |
| officeholder | 143.0 |
| cause of destruction | 52.0 |
| blood type | 20.0 |
| participant in | 135521.0 |
| list of monuments | 3738.0 |
| work location | 41606.0 |
| vehicle | 288.0 |
| dual to | 256.0 |
| executive body | 36.0 |
| astronaut mission | 327.0 |
| legal form | 331.0 |
| religious order | 1884.0 |
| direction | 1.0 |
| located on astronomical body | 116.0 |
| name day | 1065.0 |
| mineral fracture | 5.0 |
| made from material | 49794.0 |
| doctoral student | 250.0 |
| heritage designation | 72356.0 |
| appointed by | 246.0 |
| unmarried partner | 887.0 |
| product or material produced | 341.0 |
| exhibition history | 805.0 |
| place of publication | 919.0 |
| is a list of | 16723.0 |
| professorship | 71.0 |
| operating system | 1139.0 |
| platform | 33170.0 |
| academic thesis | 15.0 |
| instrument | 24534.0 |
| languages spoken, written or signed | 56928.0 |
| type of electrification | 3.0 |
| genre | 179105.0 |
| biological process | 24780.0 |
| anthem | 287.0 |
| manner of death | 3343.0 |
| affiliation | 180.0 |
| route of administration | 53.0 |
| cleavage | 54.0 |
| significant event | 5934.0 |
| academic major | 33.0 |
| contains the administrative territorial entity | 146870.0 |
| cell component | 9811.0 |
| asteroid spectral type | 370.0 |
| parent club | 54.0 |
| highest point | 205.0 |
| temporal range start | 46.0 |
| EC enzyme classification | 1.0 |
| temporal range end | 46.0 |
| asteroid family | 81.0 |
| hymenium type | 950.0 |
| Lagrangian point | 17.0 |
| interchange station | 75.0 |
| legislative body | 478.0 |
| start point | 104.0 |
| streak color | 45.0 |
| significant drug interaction | 1747.0 |
| killed by | 306.0 |
| has effect | 20.0 |
| basionym | 203.0 |
| main subject | 14726.0 |
| political ideology | 2407.0 |
| partner in business or sport | 5.0 |
| wing configuration | 486.0 |
| screenwriter | 47927.0 |
| type of variable star | 8.0 |
| mushroom ecological type | 914.0 |
| fossil found in this unit | 5.0 |
| field of this occupation | 947.0 |
| successful candidate | 579.0 |
| measurement scale | 8.0 |
| dan/kyu rank | 5.0 |
| twinned administrative body | 38104.0 |
| occupation | 1639330.0 |
| has cause | 59.0 |
| crew member(s) | 1853.0 |
| Digital Rights Management system | 14.0 |
| edibility | 567.0 |
| bodies of water basin category | 38.0 |
| published in | 1062.0 |
| original broadcaster | 4153.0 |
| location of discovery | 182.0 |
| director / manager | 656.0 |
| presenter | 912.0 |
| theme music | 9.0 |
| GHS signal word | 1.0 |
| MPA film rating | 6.0 |
| manufacturer | 6381.0 |
| chromosome | 1915.0 |
| takes place in fictional universe | 378.0 |
| product certification | 103.0 |
| lowest point | 1.0 |
| authority | 1.0 |
| followed by | 173563.0 |
| contributor to the creative work or subject | 902.0 |
| category of people buried here | 12.0 |
| printed by | 12.0 |
| website account on | 11716.0 |
| drafted by | 62.0 |
| nominated for | 1377.0 |
| writable file format | 21.0 |
| conflict | 45775.0 |
| exemplar of | 39.0 |
| chief executive officer | 295.0 |
| coolant | 230.0 |
| publisher | 29948.0 |
| canonization status | 2611.0 |
| creator | 30054.0 |
| facet of | 1739.0 |
| commander of (DEPRECATED) | 49.0 |
| parent organization | 535.0 |
| driving side | 8.0 |
| operator | 11512.0 |
| underlies | 80.0 |
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
val relCounts = graph.edges.groupBy("rel").count().cache()
display(relCounts)
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksView66cd446")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksView66cd446) ,min_max AS (SELECT `count`,(SELECT MAX(`count`) FROM q) `target_column_max`,(SELECT MIN(`count`) FROM q) `target_column_min` FROM q) ,histogram_meta AS (SELECT `count`,`target_column_min` `min_value`,IF(`target_column_max` = `target_column_min`,`target_column_max` + 1,`target_column_max`) `max_value`,(`target_column_max` - `target_column_min`) / 100 `step` FROM min_max) SELECT IF(ISNULL(`count`),NULL,LEAST(WIDTH_BUCKET(`count`,`min_value`,`max_value`,100),100)) `count_BIN`,FIRST(`min_value` + ((IF(ISNULL(`count`),NULL,LEAST(WIDTH_BUCKET(`count`,`min_value`,`max_value`,100),100)) - 1) * `step`)) `count_BIN_LOWER_BOUND`,FIRST(`step`) `count_BIN_STEP`,COUNT(`count`) `COUNT` FROM histogram_meta GROUP BY `count_BIN`"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksView66cd446")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
Also for relations we can see that some occur millions of times, whereas some only a few times. The most commmon relation is unsurprisingly "instance of", which really can be applied to every single entity. Next we find a set of relations that are applicable to most humans, including things like "occupation", "sex or gender" and "country of citizenship". From this exploration it has become clear that a large portion of the knowledge graph is concerned with people. Another large portion seems focused on geographic and political entities, such as countries and territories. We note that relations that relate to these, such as "located in the administrative territorial entity" and "shares border with" are also prevalent in the dataset. Relations that occur only once or a few times are as expected highly specific. This includes things like "GHS signal word" and "is pollinated by".
We can also check if the graph contains any self-loops, i.e. edges where the source and destination nodes are the same.
val selfLoops = graph.edges.filter("src == dst").cache()
val selfLoopRels = selfLoops.groupBy("rel").count()
display(selfLoopRels)
| rel | count |
|---|---|
| part of | 232.0 |
| based on | 1602.0 |
| father | 476.0 |
| performer | 1208.0 |
| depicts | 511.0 |
| has part(s) | 605.0 |
| located in the administrative territorial entity | 4118.0 |
| subclass of | 115.0 |
| instance of | 265.0 |
| named after | 516.0 |
| capital | 3236.0 |
| contains settlement | 3087.0 |
| follows | 117.0 |
| said to be the same as | 86.0 |
| edition or translation of | 66.0 |
| connecting line | 10.0 |
| country | 265.0 |
| encodes | 8.0 |
| child | 468.0 |
| family name identical to this given name | 287.0 |
| given name | 714.0 |
| contains the administrative territorial entity | 251.0 |
| main subject | 162.0 |
| twinned administrative body | 166.0 |
| followed by | 123.0 |
| shares border with | 59.0 |
| topic's main category | 1.0 |
| part of the series | 767.0 |
| narrative location | 40.0 |
| characters | 32.0 |
| located on street | 24.0 |
| category's main topic | 1.0 |
| located in/on physical feature | 29.0 |
| pendant of | 6.0 |
| imported from Wikimedia project | 32.0 |
| soundtrack release | 11.0 |
| has edition or translation | 66.0 |
| color | 6.0 |
| made from material | 1.0 |
| ortholog | 21.0 |
| headquarters location | 17.0 |
| territory claimed by | 2.0 |
| family name | 25.0 |
| inspired by | 23.0 |
| tracklist | 34.0 |
| producer | 16.0 |
| capital of | 14.0 |
| cast member | 16.0 |
| catalog | 1.0 |
| replaced by | 8.0 |
| author | 9.0 |
| dual to | 42.0 |
| record label | 5.0 |
| Unknown | 69.0 |
| interchange station | 3.0 |
| relative | 56.0 |
| location | 57.0 |
| place of burial | 9.0 |
| place of birth | 11.0 |
| statement is subject of | 4.0 |
| movement | 3.0 |
| parent taxon | 19.0 |
| developer | 17.0 |
| occupant | 5.0 |
| director | 2.0 |
| described by source | 14.0 |
| present in work | 26.0 |
| mouth of the watercourse | 2.0 |
| publisher | 11.0 |
| participant | 2.0 |
| dedicated to | 7.0 |
| opposite of | 23.0 |
| creator | 7.0 |
| stock exchange | 2.0 |
| screenwriter | 5.0 |
| mother house | 1.0 |
| presenter | 4.0 |
| writing system | 9.0 |
| encoded by | 7.0 |
| is a list of | 5.0 |
| genre | 297.0 |
| Wikimedia portal's main topic | 1.0 |
| employer | 4.0 |
| killed by | 4.0 |
| spouse | 4.0 |
| mother | 12.0 |
| notable work | 7.0 |
| contributor to the creative work or subject | 1.0 |
| lyrics by | 4.0 |
| replaces | 7.0 |
| published in | 4.0 |
| owned by | 9.0 |
| parent astronomical body | 2.0 |
| from narrative universe | 1.0 |
| conflict | 1.0 |
| located in or next to body of water | 8.0 |
| has facility | 1.0 |
| point group | 1.0 |
| software engine | 6.0 |
| readable file format | 1.0 |
| student of | 2.0 |
| member of | 2.0 |
| founded by | 7.0 |
| location of creation | 4.0 |
| parent organization | 1.0 |
| child astronomical body | 2.0 |
| fictional or mythical analog of | 4.0 |
| filming location | 9.0 |
| home venue | 1.0 |
| owner of | 1.0 |
| website account on | 1.0 |
| operator | 2.0 |
| underlies | 1.0 |
| feast day | 1.0 |
| topic's main Wikimedia portal | 1.0 |
| place of death | 2.0 |
| structure replaced by | 1.0 |
| depicted by | 8.0 |
| commissioned by | 1.0 |
| taxon synonym | 1.0 |
| political ideology | 3.0 |
| has use | 2.0 |
| shape | 1.0 |
| sport | 1.0 |
| partially coincident with | 2.0 |
| programmed in | 1.0 |
| country of origin | 2.0 |
| home world | 1.0 |
| composer | 3.0 |
| manufacturer | 2.0 |
| decays to | 2.0 |
| facet of | 2.0 |
| conferred by | 1.0 |
| terminus | 2.0 |
| copyright license | 1.0 |
| collection | 1.0 |
display(selfLoops)
| src | rel | dst |
|---|---|---|
| Adam Strzembosz | father | Adam Strzembosz |
| Ancient Carthage | country | Ancient Carthage |
| Bangkalan | located in the administrative territorial entity | Bangkalan |
| Barnim | named after | Barnim |
| Bereni | capital | Bereni |
| Bereni | contains settlement | Bereni |
| Birchiș | contains settlement | Birchiș |
| Birchiș | capital | Birchiș |
| Blăjani | capital | Blăjani |
| Blăjani | contains settlement | Blăjani |
| Buck | family name identical to this given name | Buck |
| Copălău | located in the administrative territorial entity | Copălău |
| Cozieni | located in the administrative territorial entity | Cozieni |
| Curtișoara | located in the administrative territorial entity | Curtișoara |
| Cârligele | located in the administrative territorial entity | Cârligele |
| D'Arcy Power | father | D'Arcy Power |
| Dance or Die | performer | Dance or Die |
| Dongen | located in the administrative territorial entity | Dongen |
| Dăești | located in the administrative territorial entity | Dăești |
| Evanescence | performer | Evanescence |
| Francis Windebank | child | Francis Windebank |
| Giroc | contains settlement | Giroc |
| Giroc | capital | Giroc |
| Gottfried Kinkel | child | Gottfried Kinkel |
| Gura Caliței | capital | Gura Caliței |
| Gura Caliței | contains settlement | Gura Caliței |
| Hama | located in the administrative territorial entity | Hama |
| Heerenveen | contains settlement | Heerenveen |
| Heerenveen | named after | Heerenveen |
| Holes | based on | Holes |
| How I Live Now | based on | How I Live Now |
| James Watt | father | James Watt |
| Jilava | located in the administrative territorial entity | Jilava |
| Kapong | located in the administrative territorial entity | Kapong |
| Lucie | performer | Lucie |
| Maj | given name | Maj |
| Mathilukal | based on | Mathilukal |
| Michael Rose | performer | Michael Rose |
| Mitreni | located in the administrative territorial entity | Mitreni |
| Morărești | capital | Morărești |
| Morărești | contains settlement | Morărești |
| Măceșu de Sus | contains settlement | Măceșu de Sus |
| Măceșu de Sus | capital | Măceșu de Sus |
| Măgești | located in the administrative territorial entity | Măgești |
| Măieruș | capital | Măieruș |
| Măieruș | contains settlement | Măieruș |
| Mărgăritești | contains settlement | Mărgăritești |
| Mărgăritești | capital | Mărgăritești |
| Ocna de Fier | located in the administrative territorial entity | Ocna de Fier |
| Paradiso | contains the administrative territorial entity | Paradiso |
| Piet Aalberse | child | Piet Aalberse |
| Pitești | located in the administrative territorial entity | Pitești |
| Poiana Mare | located in the administrative territorial entity | Poiana Mare |
| Pointed Torso | part of the series | Pointed Torso |
| Pointed Torso | part of the series | Pointed Torso |
| Reclining Figure: Hand | has part(s) | Reclining Figure: Hand |
| Rodrigo y Gabriela | notable work | Rodrigo y Gabriela |
| Runcu | contains settlement | Runcu |
| Runcu | capital | Runcu |
| Răchitova | contains settlement | Răchitova |
| Răchitova | capital | Răchitova |
| Saraiu | contains settlement | Saraiu |
| Saraiu | capital | Saraiu |
| Seine | named after | Seine |
| Slobozia | capital | Slobozia |
| Slobozia | contains settlement | Slobozia |
| Spytkowice, Nowy Targ County | located in the administrative territorial entity | Spytkowice, Nowy Targ County |
| Sânmihaiu Român | located in the administrative territorial entity | Sânmihaiu Român |
| Săucești | located in the administrative territorial entity | Săucești |
| The Front Page | based on | The Front Page |
| The Host | based on | The Host |
| The Youngbloods | performer | The Youngbloods |
| Two Piece Reclining Figure No. 4 | has part(s) | Two Piece Reclining Figure No. 4 |
| Uncle Tom's Cabin | based on | Uncle Tom's Cabin |
| Uncle Tom's Cabin | based on | Uncle Tom's Cabin |
| Urmeniș | capital | Urmeniș |
| Urmeniș | contains settlement | Urmeniș |
| Vetiș | capital | Vetiș |
| Vetiș | contains settlement | Vetiș |
| Vurpăr | contains settlement | Vurpăr |
| Vurpăr | capital | Vurpăr |
| Vădăstrița | located in the administrative territorial entity | Vădăstrița |
| Wilhelm Solheim | father | Wilhelm Solheim |
| Xiong Yan | father | Xiong Yan |
| regular tridecagon | dual to | regular tridecagon |
| After 7 | performer | After 7 |
| Ambroise Paré | depicts | Ambroise Paré |
| Apeldoorn | capital | Apeldoorn |
| Apeldoorn | contains settlement | Apeldoorn |
| Arsène Lupin | characters | Arsène Lupin |
| Arsène Lupin | characters | Arsène Lupin |
| Arsène Lupin | characters | Arsène Lupin |
| Banjar | located in the administrative territorial entity | Banjar |
| Barbie | part of the series | Barbie |
| Barbie | part of the series | Barbie |
| Bee Season | based on | Bee Season |
| Black Alien | part of | Black Alien |
| Black Christmas | based on | Black Christmas |
| Boroaia | capital | Boroaia |
| Boroaia | contains settlement | Boroaia |
| Boston | narrative location | Boston |
| Boston | twinned administrative body | Boston |
| Brebu Nou | located in the administrative territorial entity | Brebu Nou |
| Buești | contains settlement | Buești |
| Buești | capital | Buești |
| Bârsa | capital | Bârsa |
| Bârsa | contains settlement | Bârsa |
| Carl Cederström | father | Carl Cederström |
| Chicago | narrative location | Chicago |
| Chicago | narrative location | Chicago |
| Chicago | located in the administrative territorial entity | Chicago |
| Chicago | located in the administrative territorial entity | Chicago |
| Chicago | narrative location | Chicago |
| Chicago | located in the administrative territorial entity | Chicago |
| Cloppenburg | capital | Cloppenburg |
| Cloppenburg | contains the administrative territorial entity | Cloppenburg |
| Cordun | contains settlement | Cordun |
| Cordun | capital | Cordun |
| Coroisânmărtin | located in the administrative territorial entity | Coroisânmărtin |
| Darth Plagueis | characters | Darth Plagueis |
| Days of the New | performer | Days of the New |
| Days of the New | followed by | Days of the New |
| Days of the New | performer | Days of the New |
| Doom | part of the series | Doom |
| Doom | part of the series | Doom |
| Doom | based on | Doom |
| Double Oval | has part(s) | Double Oval |
| Drăgănești | contains settlement | Drăgănești |
| Drăgănești | capital | Drăgănești |
| Dumești | contains settlement | Dumești |
| Dumești | capital | Dumești |
| Durnești | capital | Durnești |
| Durnești | contains settlement | Durnești |
| Eighteen Visions | performer | Eighteen Visions |
| Ernest Tubb | performer | Ernest Tubb |
| European Champions Cup 2013 | followed by | European Champions Cup 2013 |
| Flight of the Conchords | performer | Flight of the Conchords |
| Flight of the Conchords | performer | Flight of the Conchords |
| Francesco Barberini | relative | Francesco Barberini |
| Freising | contains the administrative territorial entity | Freising |
| Fărcășești | contains settlement | Fărcășești |
| Fărcășești | capital | Fărcășești |
| Hănțești | located in the administrative territorial entity | Hănțești |
| King Lear | inspired by | King Lear |
| Large Two Forms | has part(s) | Large Two Forms |
| Lehliu Gară | located in the administrative territorial entity | Lehliu Gară |
| Leiria | located in the administrative territorial entity | Leiria |
| Loon op Zand | contains settlement | Loon op Zand |
| Louis Armand | named after | Louis Armand |
| Lozna | contains settlement | Lozna |
| Lozna | capital | Lozna |
| Luxembourg | country | Luxembourg |
| Luxembourg | country | Luxembourg |
| MAN Truck & Bus | parent organization | MAN Truck & Bus |
| Mae Sai | located in the administrative territorial entity | Mae Sai |
| Mae Sai | located in the administrative territorial entity | Mae Sai |
| Marcos | given name | Marcos |
| Min Buri | located in the administrative territorial entity | Min Buri |
| Mogoșani | located in the administrative territorial entity | Mogoșani |
| Moțăței | located in the administrative territorial entity | Moțăței |
| Naomi | given name | Naomi |
| Naughty by Nature | performer | Naughty by Nature |
| Odo | given name | Odo |
| Odorheiu Secuiesc | located in the administrative territorial entity | Odorheiu Secuiesc |
| Oscar | given name | Oscar |
| Peyton Place | based on | Peyton Place |
| Plăieșii de Jos | located in the administrative territorial entity | Plăieșii de Jos |
| Provița de Jos | located in the administrative territorial entity | Provița de Jos |
| Păușești | contains settlement | Păușești |
| Păușești | capital | Păușești |
| Quicksilver Messenger Service | performer | Quicksilver Messenger Service |
| Reclining Figure: Angles | has part(s) | Reclining Figure: Angles |
| Reclining Figure: Hand | part of the series | Reclining Figure: Hand |
| Reclining Figure: Hand | part of the series | Reclining Figure: Hand |
| Reclining Figure: Hand | part of the series | Reclining Figure: Hand |
| Reclining Figure: Hand | part of the series | Reclining Figure: Hand |
| Reclining Figure: Hand | part of the series | Reclining Figure: Hand |
| Reclining Figure: Hand | part of the series | Reclining Figure: Hand |
| Reclining Figure: Hand | part of the series | Reclining Figure: Hand |
| Red House Painters | performer | Red House Painters |
| Red House Painters | performer | Red House Painters |
| Richard Hoare | relative | Richard Hoare |
| Rowan | family name identical to this given name | Rowan |
| Rowan | has part(s) | Rowan |
| Roșiori | located in the administrative territorial entity | Roșiori |
| Rusănești | located in the administrative territorial entity | Rusănești |
| Sai Mun | contains the administrative territorial entity | Sai Mun |
| Schinnen | contains settlement | Schinnen |
| Section de recherches, season 6 | followed by | Section de recherches, season 6 |
| Sighetu Marmației | contains settlement | Sighetu Marmației |
| Sighetu Marmației | capital | Sighetu Marmației |
| Someș-Odorhei | contains settlement | Someș-Odorhei |
| Someș-Odorhei | capital | Someș-Odorhei |
| Spencer Gore | child | Spencer Gore |
| The Black Adder | follows | The Black Adder |
| The Bourne Ultimatum | based on | The Bourne Ultimatum |
| The Fifth Element | based on | The Fifth Element |
| The Hunger | based on | The Hunger |
| Toby | given name | Toby |
| Tristania | performer | Tristania |
| Ulysses | based on | Ulysses |
| Van Halen | performer | Van Halen |
| Vânători | located in the administrative territorial entity | Vânători |
| William Penn | father | William Penn |
| Zvoriștea | contains settlement | Zvoriștea |
| Zvoriștea | capital | Zvoriștea |
| Șelimbăr | contains settlement | Șelimbăr |
| Șelimbăr | capital | Șelimbăr |
| Alexia | performer | Alexia |
| American Recordings | record label | American Recordings |
| Andrew | family name identical to this given name | Andrew |
| Anina | located in the administrative territorial entity | Anina |
| Antonin | given name | Antonin |
| Ariceștii Zeletin | contains settlement | Ariceștii Zeletin |
| Ariceștii Zeletin | capital | Ariceștii Zeletin |
| Asti | named after | Asti |
| Beliu | capital | Beliu |
| Beliu | contains settlement | Beliu |
| Bodoc | located in the administrative territorial entity | Bodoc |
| Callianassa | named after | Callianassa |
| Catalina Mk. II | based on | Catalina Mk. II |
| Ceamurlia de Jos | located in the administrative territorial entity | Ceamurlia de Jos |
| Chirnogeni | located in the administrative territorial entity | Chirnogeni |
| Cleveland | twinned administrative body | Cleveland |
| Curaçao | country | Curaçao |
| Cutting the Stone | based on | Cutting the Stone |
| Death of a Salesman | based on | Death of a Salesman |
| Delești | contains settlement | Delești |
| Delești | capital | Delești |
| Demmin | located in the administrative territorial entity | Demmin |
| Dudley | located in the administrative territorial entity | Dudley |
| Eddie Kendricks | performer | Eddie Kendricks |
| Egerton | family name identical to this given name | Egerton |
| Egerton | has part(s) | Egerton |
| Emile Verhaeren | depicts | Emile Verhaeren |
| F7 | ortholog | F7 |
| Fyodor Tyutchev | child | Fyodor Tyutchev |
| Havelterberg | named after | Havelterberg |
| Izvoarele | contains settlement | Izvoarele |
| Izvoarele | capital | Izvoarele |
| Jacob Bicker | father | Jacob Bicker |
| Jean-Thomas Taschereau | child | Jean-Thomas Taschereau |
| Kantang | located in the administrative territorial entity | Kantang |
| Kantang | contains the administrative territorial entity | Kantang |
| Kari Diesen | mother | Kari Diesen |
| Leyte | located in the administrative territorial entity | Leyte |
| Lucerne | located in the administrative territorial entity | Lucerne |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mary Magdalene | depicts | Mary Magdalene |
| Mest | performer | Mest |
| Mihail Kogălniceanu | located in the administrative territorial entity | Mihail Kogălniceanu |
| Mitsuhei Obuchi | father | Mitsuhei Obuchi |
| Moldovenești | capital | Moldovenești |
| Moldovenești | contains settlement | Moldovenești |
| Negrești | contains settlement | Negrești |
| Negrești | capital | Negrești |
| Negrești-Oaș | located in the administrative territorial entity | Negrești-Oaș |
| Ngawi | capital | Ngawi |
| Osica de Sus | located in the administrative territorial entity | Osica de Sus |
| Ovidiu | contains settlement | Ovidiu |
| Ovidiu | capital | Ovidiu |
| Parța | located in the administrative territorial entity | Parța |
| Perieni | contains settlement | Perieni |
| Perieni | capital | Perieni |
| Perry Como | performer | Perry Como |
| Planet of the Apes | based on | Planet of the Apes |
| Planet of the Apes | based on | Planet of the Apes |
| Podoleni | located in the administrative territorial entity | Podoleni |
| Poienile Izei | located in the administrative territorial entity | Poienile Izei |
| Roșiori | located in the administrative territorial entity | Roșiori |
| Ryan O'Shaughnessy | performer | Ryan O'Shaughnessy |
| Râmnicelu | located in the administrative territorial entity | Râmnicelu |
| Saint-Martin-d'Oney | located in the administrative territorial entity | Saint-Martin-d'Oney |
| Salzburg | capital | Salzburg |
| Salzburg | contains the administrative territorial entity | Salzburg |
| Samuel Alken | child | Samuel Alken |
| The Anonymous Venetian | based on | The Anonymous Venetian |
| The Fox and the Hound | based on | The Fox and the Hound |
| The Spy Who Came in from the Cold | based on | The Spy Who Came in from the Cold |
| The Wannadies | performer | The Wannadies |
| The Warriors | based on | The Warriors |
| Unna | located in the administrative territorial entity | Unna |
| Valea Salciei | contains settlement | Valea Salciei |
| Valea Salciei | capital | Valea Salciei |
| Victor Cousin | depicts | Victor Cousin |
| Walter | given name | Walter |
| Walter | given name | Walter |
| Working Model for Sheep Piece | has part(s) | Working Model for Sheep Piece |
| X-COM | part of the series | X-COM |
| Știubieni | capital | Știubieni |
| Știubieni | contains settlement | Știubieni |
| Achilles | named after | Achilles |
| Aita Mare | capital | Aita Mare |
| Aita Mare | contains settlement | Aita Mare |
| Ariceștii Rahtivani | contains settlement | Ariceștii Rahtivani |
| Ariceștii Rahtivani | capital | Ariceștii Rahtivani |
| Basilan | located in/on physical feature | Basilan |
| Blandt kløver og sopp | followed by | Blandt kløver og sopp |
| Boardwalk Empire | part of the series | Boardwalk Empire |
| Boeing Aircraft Cutaways | has edition or translation | Boeing Aircraft Cutaways |
| Bonifacio | given name | Bonifacio |
| Borșa | located in the administrative territorial entity | Borșa |
| Breakaway | part of | Breakaway |
| Brodina | located in the administrative territorial entity | Brodina |
| Cajvana | located in the administrative territorial entity | Cajvana |
| Chiojdeni | contains settlement | Chiojdeni |
| Chiojdeni | capital | Chiojdeni |
| Cicănești | located in the administrative territorial entity | Cicănești |
| Ciohorăni | contains settlement | Ciohorăni |
| Ciohorăni | capital | Ciohorăni |
| Clannad | performer | Clannad |
| Comana | located in the administrative territorial entity | Comana |
| Crețeni | located in the administrative territorial entity | Crețeni |
| Critters | has part(s) | Critters |
| Cârța | located in the administrative territorial entity | Cârța |
| Dagmar Andrtová-Voňková | performer | Dagmar Andrtová-Voňková |
| Dan Makham Tia | located in the administrative territorial entity | Dan Makham Tia |
| De Hoogt | located on street | De Hoogt |
| Densuș | contains settlement | Densuș |
| Densuș | capital | Densuș |
| Dezna | located in the administrative territorial entity | Dezna |
| Disentis | located in the administrative territorial entity | Disentis |
| Dumitra | capital | Dumitra |
| Dumitra | contains settlement | Dumitra |
| Durnești | located in the administrative territorial entity | Durnești |
| Elton John | performer | Elton John |
| Endor | parent astronomical body | Endor |
| Footloose | based on | Footloose |
| Francis Newbery | relative | Francis Newbery |
| Gherăești | contains settlement | Gherăești |
| Gherăești | capital | Gherăești |
| Glodeni | located in the administrative territorial entity | Glodeni |
| Gran Turismo | part of the series | Gran Turismo |
| Gran Turismo | part of the series | Gran Turismo |
| Granma | named after | Granma |
| Griva | performer | Griva |
| Groningen | contains the administrative territorial entity | Groningen |
| Groningen | located in the administrative territorial entity | Groningen |
| Grumăzești | capital | Grumăzești |
| Grumăzești | contains settlement | Grumăzești |
| Gérard Audran | depicts | Gérard Audran |
| Head of a Girl | has part(s) | Head of a Girl |
| Henry | family name identical to this given name | Henry |
| Henry | has part(s) | Henry |
| Iepurești | located in the administrative territorial entity | Iepurești |
| Indiana Jones | from narrative universe | Indiana Jones |
| Jagged Alliance | part of the series | Jagged Alliance |
| James Davidson | child | James Davidson |
| John Randolph | child | John Randolph |
| K2 | performer | K2 |
| Langeoog | located in the administrative territorial entity | Langeoog |
| Laos | country | Laos |
| Lightning Bolt | performer | Lightning Bolt |
| Margina | capital | Margina |
| Margina | contains settlement | Margina |
| Midori | given name | Midori |
| Mihăileni | contains settlement | Mihăileni |
| Mihăileni | capital | Mihăileni |
| Mogoșani | contains settlement | Mogoșani |
| Mogoșani | capital | Mogoșani |
| Murder on the Orient Express | based on | Murder on the Orient Express |
| Naidăș | located in the administrative territorial entity | Naidăș |
| Nils Bouveng | father | Nils Bouveng |
| Ono | has part(s) | Ono |
| Phoebe | given name | Phoebe |
| Phoenix | followed by | Phoenix |
| Pomi | located in the administrative territorial entity | Pomi |
| Quidam | performer | Quidam |
| Racovița | contains settlement | Racovița |
| Racovița | capital | Racovița |
| Reclining Figure No. 4 | part of the series | Reclining Figure No. 4 |
| Reclining Figure No. 4 | part of the series | Reclining Figure No. 4 |
| Reclining Figure No. 4 | part of the series | Reclining Figure No. 4 |
| Robert Walpole | child | Robert Walpole |
| Roberto Murolo e la sua chitarra | followed by | Roberto Murolo e la sua chitarra |
| Roth | contains the administrative territorial entity | Roth |
| Roșia | contains settlement | Roșia |
| Roșia | capital | Roșia |
| Roșiori | located in the administrative territorial entity | Roșiori |
| Răchiți | contains settlement | Răchiți |
| Răchiți | capital | Răchiți |
| Sai Noi | contains the administrative territorial entity | Sai Noi |
| Samir | given name | Samir |
| Samir | given name | Samir |
| Spanțov | located in the administrative territorial entity | Spanțov |
| Star Ocean | part of the series | Star Ocean |
| Stolniceni-Prăjescu | located in the administrative territorial entity | Stolniceni-Prăjescu |
| Sărulești | located in the administrative territorial entity | Sărulești |
| Tambacounda | located in the administrative territorial entity | Tambacounda |
| The Archers | performer | The Archers |
| The Empire of Lights | part of | The Empire of Lights |
| The It Girl | part of the series | The It Girl |
| The U.S. vs. John Lennon | soundtrack release | The U.S. vs. John Lennon |
| Tulcea | contains settlement | Tulcea |
| Tulcea | capital | Tulcea |
| Type 63 | conflict | Type 63 |
| Tâmboești | capital | Tâmboești |
| Tâmboești | contains settlement | Tâmboești |
| Upright Motive No. 7 | has part(s) | Upright Motive No. 7 |
| Venlo | located in the administrative territorial entity | Venlo |
| Veracruz | located in the administrative territorial entity | Veracruz |
| Vânători | located in the administrative territorial entity | Vânători |
| Vătava | contains settlement | Vătava |
| Vătava | capital | Vătava |
| Walter Devereux | child | Walter Devereux |
| William Goforth | child | William Goforth |
| XIII | based on | XIII |
| Your Face Sounds Familiar | based on | Your Face Sounds Familiar |
| Zapovit | edition or translation of | Zapovit |
| Zapovit | edition or translation of | Zapovit |
| Zapovit | edition or translation of | Zapovit |
| Însurăței | located in the administrative territorial entity | Însurăței |
| A Simple Plan | based on | A Simple Plan |
| Battle of Grunwald | depicts | Battle of Grunwald |
| Blijdorp | named after | Blijdorp |
| Bogda | located in the administrative territorial entity | Bogda |
| Bolboși | located in the administrative territorial entity | Bolboși |
| Bonded by Blood | named after | Bonded by Blood |
| Boss Hog | performer | Boss Hog |
| Bosse | named after | Bosse |
| Bror Cederström | child | Bror Cederström |
| Brădești | located in the administrative territorial entity | Brădești |
| Bung Khla | located in the administrative territorial entity | Bung Khla |
| Béla Bartók | child | Béla Bartók |
| Béla Bartók | relative | Béla Bartók |
| Chrysomela | parent taxon | Chrysomela |
| Cosmești | contains settlement | Cosmești |
| Cosmești | capital | Cosmești |
| Cudalbi | contains settlement | Cudalbi |
| Cudalbi | capital | Cudalbi |
| Donny Hathaway | performer | Donny Hathaway |
| Dragodana | located in the administrative territorial entity | Dragodana |
| Foundiougne Department | located in the administrative territorial entity | Foundiougne Department |
| František Schneider | child | František Schneider |
| François Coppée | depicts | François Coppée |
| George Bacon Wood | described by source | George Bacon Wood |
| Gurasada | capital | Gurasada |
| Gurasada | contains settlement | Gurasada |
| Hansel and Gretel | based on | Hansel and Gretel |
| Hansel and Gretel | based on | Hansel and Gretel |
| John Denver | performer | John Denver |
| Kendal | located in the administrative territorial entity | Kendal |
| Large Standing Figure: Knife Edge | has part(s) | Large Standing Figure: Knife Edge |
| Livada | capital | Livada |
| Livada | contains settlement | Livada |
| Mariachi El Bronx | followed by | Mariachi El Bronx |
| Martina von Schwerin | child | Martina von Schwerin |
| Mauritius | country | Mauritius |
| Meiningen | twinned administrative body | Meiningen |
| Mica | located in the administrative territorial entity | Mica |
| Molly Hatchet | performer | Molly Hatchet |
| Nanning van Foreest | father | Nanning van Foreest |
| Ocnița | contains settlement | Ocnița |
| Ocnița | capital | Ocnița |
| Peregu Mare | contains settlement | Peregu Mare |
| Peregu Mare | capital | Peregu Mare |
| Podoleni | capital | Podoleni |
| Podoleni | contains settlement | Podoleni |
| Priboieni | capital | Priboieni |
| Priboieni | contains settlement | Priboieni |
| Prigor | capital | Prigor |
| Prigor | contains settlement | Prigor |
| Rembang | capital | Rembang |
| SOCOM U.S. Navy SEALs | part of the series | SOCOM U.S. Navy SEALs |
| Santa Claus | depicts | Santa Claus |
| Slatina | located in the administrative territorial entity | Slatina |
| Soest | located in the administrative territorial entity | Soest |
| The Aggrolites | performer | The Aggrolites |
| The Great Gatsby | based on | The Great Gatsby |
| The Great Gatsby | based on | The Great Gatsby |
| The Great Gatsby | based on | The Great Gatsby |
| The Great Gatsby | based on | The Great Gatsby |
| The Great Gatsby | based on | The Great Gatsby |
| The Raven | edition or translation of | The Raven |
| The Raven | edition or translation of | The Raven |
| The Raven | based on | The Raven |
| The Raven | edition or translation of | The Raven |
| The Virgin Suicides | based on | The Virgin Suicides |
| Triage | based on | Triage |
| Veghel | capital | Veghel |
| Veghel | contains settlement | Veghel |
| Vișinești | located in the administrative territorial entity | Vișinești |
| Walter Gropius | father | Walter Gropius |
| Woking | located in the administrative territorial entity | Woking |
| iPad | subclass of | iPad |
| Șendriceni | contains settlement | Șendriceni |
| Șendriceni | capital | Șendriceni |
| Adjud | located in the administrative territorial entity | Adjud |
| Aninoasa | capital | Aninoasa |
| Aninoasa | contains settlement | Aninoasa |
| Annabel | given name | Annabel |
| Annabel | family name | Annabel |
| Arnulf | given name | Arnulf |
| Arnulf | given name | Arnulf |
| Assis Chateaubriand | named after | Assis Chateaubriand |
| Batu | located in the administrative territorial entity | Batu |
| Berghin | located in the administrative territorial entity | Berghin |
| Black Stone Cherry | performer | Black Stone Cherry |
| Brădești | capital | Brădești |
| Brădești | contains settlement | Brădești |
| Bucșani | contains settlement | Bucșani |
| Bucșani | capital | Bucșani |
| Bud Spencer Blues Explosion | performer | Bud Spencer Blues Explosion |
| Bălilești | located in the administrative territorial entity | Bălilești |
| Cameroon | country | Cameroon |
| Catalina | located in the administrative territorial entity | Catalina |
| Catane | located in the administrative territorial entity | Catane |
| Catherine of Bosnia | mother | Catherine of Bosnia |
| Chișlaz | contains settlement | Chișlaz |
| Chișlaz | capital | Chișlaz |
| Ciceu-Mihăiești | located in the administrative territorial entity | Ciceu-Mihăiești |
| Colonești | located in the administrative territorial entity | Colonești |
| Coțofenii din Față | located in the administrative territorial entity | Coțofenii din Față |
| Cârjiți | located in the administrative territorial entity | Cârjiți |
| Dalton | family name identical to this given name | Dalton |
| Damage | based on | Damage |
| Day of Fire | performer | Day of Fire |
| Dobreni | located in the administrative territorial entity | Dobreni |
| Double Indemnity | based on | Double Indemnity |
| Draped Reclining Mother and Baby | has part(s) | Draped Reclining Mother and Baby |
| Eclipse of Reason | has edition or translation | Eclipse of Reason |
| Eddie Santiago | performer | Eddie Santiago |
| Ende | located in the administrative territorial entity | Ende |
| Filipeștii de Târg | located in the administrative territorial entity | Filipeștii de Târg |
| František Veselý | child | František Veselý |
| Frumoasa | contains settlement | Frumoasa |
| Frumoasa | capital | Frumoasa |
| Gura Padinii | contains settlement | Gura Padinii |
| Gura Padinii | capital | Gura Padinii |
| Gyeongjeon Line | connecting line | Gyeongjeon Line |
| Hans von Matt | child | Hans von Matt |
| Henri, Prince of Condé | child | Henri, Prince of Condé |
| Highway 1 | has part(s) | Highway 1 |
| Hopârta | capital | Hopârta |
| Hopârta | contains settlement | Hopârta |
| Hypnos | performer | Hypnos |
| Hălmăgel | capital | Hălmăgel |
| Hălmăgel | contains settlement | Hălmăgel |
| Ideciu de Jos | located in the administrative territorial entity | Ideciu de Jos |
| Jeremiasz | given name | Jeremiasz |
| Lady Chatterley's Lover | based on | Lady Chatterley's Lover |
| Lajes do Pico | located in the administrative territorial entity | Lajes do Pico |
| Land of the Lost | based on | Land of the Lost |
| Laprida | capital | Laprida |
| Large Spindle Piece | has part(s) | Large Spindle Piece |
| Laslea | located in the administrative territorial entity | Laslea |
| Like a Prayer | part of | Like a Prayer |
| Limburg | shares border with | Limburg |
| Lorenzo | family name identical to this given name | Lorenzo |
| Luci | family name identical to this given name | Luci |
| Lunca | capital | Lunca |
| Lunca | contains settlement | Lunca |
| Mayke | given name | Mayke |
| Movila Miresii | contains settlement | Movila Miresii |
| Movila Miresii | capital | Movila Miresii |
| Mârșani | located in the administrative territorial entity | Mârșani |
| Negri | located in the administrative territorial entity | Negri |
| Neighbours from Hell | part of | Neighbours from Hell |
| Oarja | capital | Oarja |
| Oarja | contains settlement | Oarja |
| Ocna Șugatag | located in the administrative territorial entity | Ocna Șugatag |
| Osasco | twinned administrative body | Osasco |
| Parava | located in the administrative territorial entity | Parava |
| Parava | contains settlement | Parava |
| Parava | capital | Parava |
| Pierre | family name identical to this given name | Pierre |
| Prundu Bârgăului | located in the administrative territorial entity | Prundu Bârgăului |
| Raging Speedhorn | performer | Raging Speedhorn |
| Republic of Texas | country | Republic of Texas |
| Scărișoara | located in the administrative territorial entity | Scărișoara |
| Singing in the Twins Wonderland | follows | Singing in the Twins Wonderland |
| Stoicănești | located in the administrative territorial entity | Stoicănești |
| Strangers on a Train | based on | Strangers on a Train |
| Sânnicolau Mare | located in the administrative territorial entity | Sânnicolau Mare |
| Tegernsee | located in the administrative territorial entity | Tegernsee |
| The Rover Boys on the Ocean | edition or translation of | The Rover Boys on the Ocean |
| The Shadows | performer | The Shadows |
| The Sum of All Fears | based on | The Sum of All Fears |
| Three Men in a Boat | based on | Three Men in a Boat |
| Three Piece Reclining Figure: Draped | has part(s) | Three Piece Reclining Figure: Draped |
| Thutmose | given name | Thutmose |
| Thutmose | given name | Thutmose |
| Tilișca | contains settlement | Tilișca |
| Tilișca | capital | Tilișca |
| Tim Finn | performer | Tim Finn |
| Tomești | located in the administrative territorial entity | Tomești |
| Tulgheș | capital | Tulgheș |
| Tulgheș | contains settlement | Tulgheș |
| Unirea | located in the administrative territorial entity | Unirea |
| Vorkuta | located in the administrative territorial entity | Vorkuta |
| Văleni | located in the administrative territorial entity | Văleni |
| Wyns | location | Wyns |
| Ziduri | capital | Ziduri |
| Ziduri | contains settlement | Ziduri |
| Șag | located in the administrative territorial entity | Șag |
| Țuglui | capital | Țuglui |
| Țuglui | contains settlement | Țuglui |
| Aleșd | located in the administrative territorial entity | Aleșd |
| Alimpești | located in the administrative territorial entity | Alimpești |
| Amaru | capital | Amaru |
| Amaru | contains settlement | Amaru |
| Armavir | twinned administrative body | Armavir |
| Augustin | located in the administrative territorial entity | Augustin |
| Bad Dürkheim | located in the administrative territorial entity | Bad Dürkheim |
| Barabbas | based on | Barabbas |
| Bengkulu | located in the administrative territorial entity | Bengkulu |
| Biliran | contains the administrative territorial entity | Biliran |
| Bucecea | contains settlement | Bucecea |
| Bucecea | capital | Bucecea |
| Bucu | located in the administrative territorial entity | Bucu |
| Bârna | capital | Bârna |
| Bârna | contains settlement | Bârna |
| Cheap Trick | performer | Cheap Trick |
| Cheap Trick | performer | Cheap Trick |
| Churwalden | located in the administrative territorial entity | Churwalden |
| Circe | depicts | Circe |
| Crișcior | contains settlement | Crișcior |
| Crișcior | capital | Crișcior |
| Cugir | located in the administrative territorial entity | Cugir |
| Căpâlnița | contains settlement | Căpâlnița |
| Căpâlnița | capital | Căpâlnița |
| Danube | named after | Danube |
| Denis | family name identical to this given name | Denis |
| Die Ärzte | performer | Die Ärzte |
| Die Ärzte | performer | Die Ärzte |
| Dobromir | capital | Dobromir |
| Dobromir | contains settlement | Dobromir |
| Ender's Game | based on | Ender's Game |
| Erstein | located in the administrative territorial entity | Erstein |
| Falkenstein | twinned administrative body | Falkenstein |
| Farébersviller | located in the administrative territorial entity | Farébersviller |
| Forlorn River | based on | Forlorn River |
| Frank Lampard | father | Frank Lampard |
| Gods and Generals | based on | Gods and Generals |
| Graveyard Shift | based on | Graveyard Shift |
| Hat Yai | contains the administrative territorial entity | Hat Yai |
| Heerde | located in the administrative territorial entity | Heerde |
| Heleșteni | contains settlement | Heleșteni |
| Heleșteni | capital | Heleșteni |
| Heleșteni | located in the administrative territorial entity | Heleșteni |
| Hoot | tracklist | Hoot |
| I Am Legend | based on | I Am Legend |
| Jake Bugg | performer | Jake Bugg |
| Jake Bugg | named after | Jake Bugg |
| James Franklin Alexander | main subject | James Franklin Alexander |
| John Taylor | child | John Taylor |
| Kingdom of Great Britain | country | Kingdom of Great Britain |
| Lam Plai Mat | located in the administrative territorial entity | Lam Plai Mat |
| Leu | located in the administrative territorial entity | Leu |
| Linda Davis | performer | Linda Davis |
| Lisa | capital | Lisa |
| Lisa | contains settlement | Lisa |
| Malnaș | contains settlement | Malnaș |
| Malnaș | capital | Malnaș |
| Manasia | located in the administrative territorial entity | Manasia |
| Maxwell | given name | Maxwell |
| Maya | given name | Maya |
| Miguel de Bragança | father | Miguel de Bragança |
| Moreno | given name | Moreno |
| Nanning van Foreest | father | Nanning van Foreest |
| Oncești | capital | Oncești |
| Oncești | contains settlement | Oncești |
| Poienarii Burchii | located in the administrative territorial entity | Poienarii Burchii |
| Postal | part of the series | Postal |
| Prem | given name | Prem |
| Pădureni | capital | Pădureni |
| Pădureni | contains settlement | Pădureni |
| Regis | family name identical to this given name | Regis |
| Repedea | contains settlement | Repedea |
| Repedea | capital | Repedea |
| Ruth | characters | Ruth |
| Răcășdia | contains settlement | Răcășdia |
| Răcășdia | capital | Răcășdia |
| Saint Barbara | depicts | Saint Barbara |
| Saint Barbara | depicts | Saint Barbara |
| Saint Barbara | depicts | Saint Barbara |
| Say Anything | performer | Say Anything |
| Sponge Cola | performer | Sponge Cola |
| Sucevița | located in the administrative territorial entity | Sucevița |
| Super Mario Bros. | based on | Super Mario Bros. |
| Super Monkey Ball | part of the series | Super Monkey Ball |
| São Paulo | capital | São Paulo |
| Săcălășeni | capital | Săcălășeni |
| Săcălășeni | contains settlement | Săcălășeni |
| The Postman Always Rings Twice | based on | The Postman Always Rings Twice |
| The Postman Always Rings Twice | based on | The Postman Always Rings Twice |
| Three Piece Reclining Figure No. 1 | part of the series | Three Piece Reclining Figure No. 1 |
| Three Piece Reclining Figure No. 1 | part of the series | Three Piece Reclining Figure No. 1 |
| Three Piece Reclining Figure No. 1 | part of the series | Three Piece Reclining Figure No. 1 |
| Three Piece Reclining Figure No. 1 | part of the series | Three Piece Reclining Figure No. 1 |
| Three Piece Reclining Figure No. 1 | part of the series | Three Piece Reclining Figure No. 1 |
| Three Piece Reclining Figure No. 1 | part of the series | Three Piece Reclining Figure No. 1 |
| Three Piece Reclining Figure: Draped | has part(s) | Three Piece Reclining Figure: Draped |
| Thung Chang | contains the administrative territorial entity | Thung Chang |
| Thung Chang | named after | Thung Chang |
| Tomești | contains settlement | Tomești |
| Tomești | capital | Tomești |
| Tone | has part(s) | Tone |
| Tracy Lawrence | performer | Tracy Lawrence |
| Two Piece Reclining Figure No. 9 | has part(s) | Two Piece Reclining Figure No. 9 |
| Vâlcelele | capital | Vâlcelele |
| Vâlcelele | contains settlement | Vâlcelele |
| Wade | family name identical to this given name | Wade |
| Wandong | located in the administrative territorial entity | Wandong |
| Winterswijk | capital | Winterswijk |
| Winterswijk | contains settlement | Winterswijk |
| tellurium | subclass of | tellurium |
| Șirna | located in the administrative territorial entity | Șirna |
| Baotou–Lanzhou railway | connecting line | Baotou–Lanzhou railway |
| Bernard Palissy | depicts | Bernard Palissy |
| Bernd Rosemeyer | child | Bernd Rosemeyer |
| Bârghiș | located in the administrative territorial entity | Bârghiș |
| CYP1A2 | encodes | CYP1A2 |
| Cartel | performer | Cartel |
| Cernavodă | located in the administrative territorial entity | Cernavodă |
| Charles | said to be the same as | Charles |
| Charles | given name | Charles |
| Chiojdeanca | located in the administrative territorial entity | Chiojdeanca |
| Călmățuiu | located in the administrative territorial entity | Călmățuiu |
| Cănești | contains settlement | Cănești |
| Cănești | capital | Cănești |
| Căpușu Mare | capital | Căpușu Mare |
| Căpușu Mare | contains settlement | Căpușu Mare |
| David Stierncrona | child | David Stierncrona |
| Djibouti | contains the administrative territorial entity | Djibouti |
| Djibouti | named after | Djibouti |
| Djibouti | capital | Djibouti |
| Dobroteasa | contains settlement | Dobroteasa |
| Dobroteasa | capital | Dobroteasa |
| Domenico Modugno | followed by | Domenico Modugno |
| Donald Tusk | father | Donald Tusk |
| Family Group | has part(s) | Family Group |
| Family Group | has part(s) | Family Group |
| Gura Ocniței | located in the administrative territorial entity | Gura Ocniței |
| Gustaf Petrén | father | Gustaf Petrén |
| Gârbovi | located in the administrative territorial entity | Gârbovi |
| Henry | subclass of | Henry |
| Henry | part of | Henry |
| Ina | part of | Ina |
| James Dickson | child | James Dickson |
| Jordan | country | Jordan |
| Karanganyar | capital | Karanganyar |
| Kendal | capital | Kendal |
| Kiyevskaya | has part(s) | Kiyevskaya |
| Livezi | located in the administrative territorial entity | Livezi |
| Luna Sea | performer | Luna Sea |
| Madagascar | country | Madagascar |
| Mao: A Life | edition or translation of | Mao: A Life |
| Martin Luther | main subject | Martin Luther |
| Maxime Lalanne | depicts | Maxime Lalanne |
| Meseșenii de Jos | contains settlement | Meseșenii de Jos |
| Meseșenii de Jos | capital | Meseșenii de Jos |
| Montesquieu | depicts | Montesquieu |
| Montesquieu | depicts | Montesquieu |
| Nojorid | capital | Nojorid |
| Nojorid | contains settlement | Nojorid |
| Ohaba Lungă | contains settlement | Ohaba Lungă |
| Ohaba Lungă | capital | Ohaba Lungă |
| Out | based on | Out |
| Oval with Points | has part(s) | Oval with Points |
| Phoenix | follows | Phoenix |
| Pietroasa | located in the administrative territorial entity | Pietroasa |
| Prejmer | capital | Prejmer |
| Prejmer | contains settlement | Prejmer |
| Prince Henry of Prussia | child | Prince Henry of Prussia |
| Psamathe | named after | Psamathe |
| Pungești | capital | Pungești |
| Pungești | contains settlement | Pungești |
| Reghin | located in the administrative territorial entity | Reghin |
| Rochdale | located in the administrative territorial entity | Rochdale |
| Rolando | given name | Rolando |
| Rosalie | given name | Rosalie |
| Sandy | given name | Sandy |
| Sillery | twinned administrative body | Sillery |
| The Flight of the Phoenix | based on | The Flight of the Phoenix |
| The Prisoner of Chillon | based on | The Prisoner of Chillon |
| The Sound of Music | part of | The Sound of Music |
| Three Standing Figures | has part(s) | Three Standing Figures |
| Tulca | contains settlement | Tulca |
| Tulca | capital | Tulca |
| Târgu Secuiesc | contains settlement | Târgu Secuiesc |
| Târgu Secuiesc | capital | Târgu Secuiesc |
| Valea Argovei | capital | Valea Argovei |
| Valea Argovei | contains settlement | Valea Argovei |
| Vărădia de Mureș | capital | Vărădia de Mureș |
| Vărădia de Mureș | contains settlement | Vărădia de Mureș |
| Wille | family name identical to this given name | Wille |
| William Barrowby | father | William Barrowby |
| region of Malta | instance of | region of Malta |
| Șieu-Măgheruș | capital | Șieu-Măgheruș |
| Șieu-Măgheruș | contains settlement | Șieu-Măgheruș |
| Agostino | family name identical to this given name | Agostino |
| Aristide Briand | depicts | Aristide Briand |
| Bantul | located in the administrative territorial entity | Bantul |
| Battlestar Galactica | based on | Battlestar Galactica |
| Boghicea | capital | Boghicea |
| Boghicea | contains settlement | Boghicea |
| Brett Dennen | performer | Brett Dennen |
| Bridget Jones: The Edge of Reason | based on | Bridget Jones: The Edge of Reason |
| Bucecea | located in the administrative territorial entity | Bucecea |
| Bulbucata | located in the administrative territorial entity | Bulbucata |
| Butea | located in the administrative territorial entity | Butea |
| Băbana | capital | Băbana |
| Băbana | contains settlement | Băbana |
| Capitoline Wolf | based on | Capitoline Wolf |
| Charles De Geer | father | Charles De Geer |
| Chatuchak | located in the administrative territorial entity | Chatuchak |
| Cireșu | located in the administrative territorial entity | Cireșu |
| Club Bangaz | follows | Club Bangaz |
| Council of Orange | followed by | Council of Orange |
| Cuca | contains settlement | Cuca |
| Cuca | capital | Cuca |
| Ditrău | contains settlement | Ditrău |
| Ditrău | capital | Ditrău |
| Doctor Zhivago | based on | Doctor Zhivago |
| Dragomirești | contains settlement | Dragomirești |
| Dragomirești | capital | Dragomirești |
| Edvard Petrén | father | Edvard Petrén |
| Emmi | given name | Emmi |
| Farah | located in the administrative territorial entity | Farah |
| Fuyuan | contains the administrative territorial entity | Fuyuan |
| Gers | named after | Gers |
| Greg Lake | performer | Greg Lake |
| Grădinari | located in the administrative territorial entity | Grădinari |
| Guglielmo | given name | Guglielmo |
| Huedin | located in the administrative territorial entity | Huedin |
| Jan van Goyen | named after | Jan van Goyen |
| Jawbox | performer | Jawbox |
| Jiří Kvita | child | Jiří Kvita |
| Johan | participant | Johan |
| Johan Willem Frisokazerne | part of | Johan Willem Frisokazerne |
| Johannes Olearius | child | Johannes Olearius |
| John Walter | father | John Walter |
| József Kautzky | child | József Kautzky |
| Lesquin | located in the administrative territorial entity | Lesquin |
| Love Story | follows | Love Story |
| Lunca de Jos | located in the administrative territorial entity | Lunca de Jos |
| Lycus | father | Lycus |
| Manfred Mann's Earth Band | performer | Manfred Mann's Earth Band |
| Mehadia | capital | Mehadia |
| Mehadia | contains settlement | Mehadia |
| Money in the Bank | followed by | Money in the Bank |
| Money in the Bank | follows | Money in the Bank |
| Moquegua | capital | Moquegua |
| Negrești | contains settlement | Negrești |
| Negrești | capital | Negrești |
| Nenciulești | capital | Nenciulești |
| Nenciulești | contains settlement | Nenciulești |
| Noemi | given name | Noemi |
| Ocoliș | capital | Ocoliș |
| Ocoliș | contains settlement | Ocoliș |
| Putineiu | located in the administrative territorial entity | Putineiu |
| Pyu city-states | capital | Pyu city-states |
| Pătârlagele | contains settlement | Pătârlagele |
| Pătârlagele | capital | Pătârlagele |
| Ready | based on | Ready |
| Samuel Lewis | father | Samuel Lewis |
| Satchinez | located in the administrative territorial entity | Satchinez |
| Schitu | contains settlement | Schitu |
| Schitu | capital | Schitu |
| Seaca | located in the administrative territorial entity | Seaca |
| Shadowrun | part of the series | Shadowrun |
| Shadowrun | based on | Shadowrun |
| Shadowrun | part of the series | Shadowrun |
| Shadowrun | part of the series | Shadowrun |
| Shadowrun | part of the series | Shadowrun |
| Stejaru | located in the administrative territorial entity | Stejaru |
| Stăuceni | located in the administrative territorial entity | Stăuceni |
| Susan | given name | Susan |
| Săcălaz | located in the administrative territorial entity | Săcălaz |
| Telciu | located in the administrative territorial entity | Telciu |
| The Cross and the Switchblade | based on | The Cross and the Switchblade |
| The Last Dictatorship in Europe. Belarus under Lukashenko | edition or translation of | The Last Dictatorship in Europe. Belarus under Lukashenko |
| The Moon and Sixpence | based on | The Moon and Sixpence |
| The Three Stooges | based on | The Three Stooges |
| Tuttlingen | located in the administrative territorial entity | Tuttlingen |
| Vârciorog | located in the administrative territorial entity | Vârciorog |
| Vârfu Câmpului | located in the administrative territorial entity | Vârfu Câmpului |
| Vărădia de Mureș | located in the administrative territorial entity | Vărădia de Mureș |
| William Watson | child | William Watson |
| Wonosobo | capital | Wonosobo |
| Abraham des Amorie van der Hoeven | child | Abraham des Amorie van der Hoeven |
| Altenglan | headquarters location | Altenglan |
| Amara | contains settlement | Amara |
| Amara | capital | Amara |
| Angélica | followed by | Angélica |
| Angélica | follows | Angélica |
| Atlantis | narrative location | Atlantis |
| Aurich | located in the administrative territorial entity | Aurich |
| Band | capital | Band |
| Band | contains settlement | Band |
| Bechet | contains settlement | Bechet |
| Bechet | capital | Bechet |
| Beclean | located in the administrative territorial entity | Beclean |
| Besa | given name | Besa |
| Bozovici | located in the administrative territorial entity | Bozovici |
| Brusturoasa | capital | Brusturoasa |
| Brusturoasa | contains settlement | Brusturoasa |
| Bujoreni | capital | Bujoreni |
| Bujoreni | contains settlement | Bujoreni |
| Băilești | contains settlement | Băilești |
| Băilești | capital | Băilești |
| Cașin | located in the administrative territorial entity | Cașin |
| Chilia Veche | contains settlement | Chilia Veche |
| Chilia Veche | capital | Chilia Veche |
| Coesfeld | located in the administrative territorial entity | Coesfeld |
| Corbasca | contains settlement | Corbasca |
| Corbasca | capital | Corbasca |
| Coșereni | contains settlement | Coșereni |
| Coșereni | capital | Coșereni |
| Cut | capital | Cut |
| Cut | contains settlement | Cut |
| Căpleni | located in the administrative territorial entity | Căpleni |
| Daredevil | present in work | Daredevil |
| Dobrin | located in the administrative territorial entity | Dobrin |
| Dudești | located in the administrative territorial entity | Dudești |
| Dumbrăvești | contains settlement | Dumbrăvești |
| Dumbrăvești | capital | Dumbrăvești |
| Ednita Nazario | performer | Ednita Nazario |
| Edward Henry Bernhard | owned by | Edward Henry Bernhard |
| Facing New York | performer | Facing New York |
| Fatherland | based on | Fatherland |
| Feldru | contains settlement | Feldru |
| Feldru | capital | Feldru |
| Filipeștii de Pădure | located in the administrative territorial entity | Filipeștii de Pădure |
| Francis Scott | child | Francis Scott |
| Garmisch-Partenkirchen | located in the administrative territorial entity | Garmisch-Partenkirchen |
| Gemenele | located in the administrative territorial entity | Gemenele |
| Goes | capital | Goes |
| Grover Cleveland | depicts | Grover Cleveland |
| Gura Râului | contains settlement | Gura Râului |
| Gura Râului | capital | Gura Râului |
| Hangu | contains settlement | Hangu |
| Hangu | capital | Hangu |
| John of Brienne | father | John of Brienne |
| Kaiserslautern | shares border with | Kaiserslautern |
| Kandahar | capital | Kandahar |
| Kleinpolderplein | partially coincident with | Kleinpolderplein |
| Kumphawapi | contains the administrative territorial entity | Kumphawapi |
| Lat Krabang | contains the administrative territorial entity | Lat Krabang |
| Le Bourget | depicts | Le Bourget |
| Lovrin | located in the administrative territorial entity | Lovrin |
| Mardi Gras | depicts | Mardi Gras |
| Mario Losada | father | Mario Losada |
| Martin | has part(s) | Martin |
| Mongolian | writing system | Mongolian |
| Moțăței | contains settlement | Moțăței |
| Moțăței | capital | Moțăței |
| Mălini | capital | Mălini |
| Mălini | contains settlement | Mălini |
| Nana | based on | Nana |
| Nămoloasa | contains settlement | Nămoloasa |
| Nămoloasa | capital | Nămoloasa |
| Otto Hess | father | Otto Hess |
| Plesoiu | located in the administrative territorial entity | Plesoiu |
| Podeni | located in the administrative territorial entity | Podeni |
| Poienile de sub Munte | located in the administrative territorial entity | Poienile de sub Munte |
| Pringsewu | capital | Pringsewu |
| Pucioasa | located in the administrative territorial entity | Pucioasa |
| Raffaele | family name identical to this given name | Raffaele |
| Rădăuți-Prut | located in the administrative territorial entity | Rădăuți-Prut |
| Războieni | contains settlement | Războieni |
| Sangkha | capital | Sangkha |
| Sergio | family name identical to this given name | Sergio |
| Sfântu Gheorghe | contains settlement | Sfântu Gheorghe |
| Sfântu Gheorghe | capital | Sfântu Gheorghe |
| Slobozia Mândra | located in the administrative territorial entity | Slobozia Mândra |
| Stolnici | contains settlement | Stolnici |
| Stolnici | capital | Stolnici |
| Stâlpeni | located in the administrative territorial entity | Stâlpeni |
| Sânsimion | located in the administrative territorial entity | Sânsimion |
| Săcădat | contains settlement | Săcădat |
| Săcădat | capital | Săcădat |
| Tanacu | capital | Tanacu |
| Tanacu | contains settlement | Tanacu |
| The Wayward Bus | based on | The Wayward Bus |
| Third Day | performer | Third Day |
| Third Day | performer | Third Day |
| Tormac | located in the administrative territorial entity | Tormac |
| Verena | given name | Verena |
| William Hazlitt | father | William Hazlitt |
| Zuidhorn | named after | Zuidhorn |
| Șimian | contains settlement | Șimian |
| Șimian | capital | Șimian |
| Abra | given name | Abra |
| All Quiet on the Western Front | based on | All Quiet on the Western Front |
| Anna Pavlova | depicts | Anna Pavlova |
| Avrămeni | contains settlement | Avrămeni |
| Avrămeni | capital | Avrămeni |
| Avrămești | capital | Avrămești |
| Avrămești | contains settlement | Avrămești |
Indeed it does, but not many. Most of these are for a few specific relations and many seem to come from errors in the knowledge graph. Some of these are legitimmate, for example it is valid to say that country A is "located in the administrative territorial entity" of country A. Others are clearly erroneous, like the edges denoting people as their own father. A few are not wrong but add no real information, for example the self-loops with the relation "said to be the same as".
Instance Type of Nodes
The most common relation "instance of" is of particular interest. It denotes the basic type each node/entity belongs to. The cell below will count how many nodes exist of each instance type sort these to find the most common types in the data.
// Count the number of times each node type occurs
val instanceRels = graph.edges.filter("rel == 'instance of'")
val joinedTypes = degrees.join(instanceRels, degrees("id") === instanceRels("src"), "leftouter").select($"id", $"inDegree", $"outDegree", ($"dst").as("type")).cache()
val typeCounts = joinedTypes.groupBy("type").count().filter($"type" =!= "null").sort($"count".desc)
display(typeCounts)
| type | count |
|---|---|
| human | 251915.0 |
| album | 49063.0 |
| asteroid | 40573.0 |
| single | 38372.0 |
| commune of France | 32436.0 |
| township in China | 19553.0 |
| street | 16483.0 |
| Wikimedia category | 14602.0 |
| taxon | 14226.0 |
| film | 13894.0 |
| Book | 13403.0 |
| township of the People's Republic of China | 12472.0 |
| natural number | 9994.0 |
| railway station | 9798.0 |
| city | 9451.0 |
| painting | 9373.0 |
| village | 8205.0 |
| comune of Italy | 8098.0 |
| municipality of Germany | 8005.0 |
| episode | 7374.0 |
| Q12808966 | 7066.0 |
| association football club | 7049.0 |
| municipality of the Czech Republic | 6192.0 |
| Wikimedia list article | 5847.0 |
| musical group | 5011.0 |
| odd number | 4999.0 |
| even number | 4999.0 |
| human settlement | 4885.0 |
| male given name | 4475.0 |
| television series season | 4398.0 |
| town | 4352.0 |
| municipality of Spain | 4117.0 |
| canton of France (until 2015) | 4023.0 |
| company | 3972.0 |
| municipality seat | 3213.0 |
| Q12809484 | 3168.0 |
| video game | 3133.0 |
| Metro station | 3123.0 |
| year | 3079.0 |
| song | 2898.0 |
| commune of Romania | 2861.0 |
| given name | 2860.0 |
| Q19622166 | 2643.0 |
| Wikimedia disambiguation page | 2613.0 |
| female given name | 2602.0 |
| subdivision of Russia | 2513.0 |
| municipality of Switzerland | 2436.0 |
| sports season of a sports club | 2415.0 |
| university | 2352.0 |
| cadastral populated place in the Netherlands | 2348.0 |
| municipality of Austria | 2348.0 |
| river | 2274.0 |
| political party | 2247.0 |
| sculpture | 2179.0 |
| church | 2177.0 |
| family name | 2091.0 |
| Ortsteil | 2062.0 |
| record label | 2036.0 |
| live album | 2000.0 |
| municipality of Brazil | 1735.0 |
| rural municipality of Poland | 1568.0 |
| television series | 1566.0 |
| municipality of the Philippines | 1488.0 |
| comic strip | 1472.0 |
| compilation album | 1454.0 |
| county of China | 1447.0 |
| award | 1425.0 |
| rural municipality of Austria | 1378.0 |
| municipal district | 1376.0 |
| civil parish | 1339.0 |
| comic book album | 1319.0 |
| profession | 1308.0 |
| association football venue | 1255.0 |
| position | 1238.0 |
| prime number | 1230.0 |
| fictional character | 1187.0 |
| town in the United States | 1180.0 |
| television program | 1155.0 |
| stadium | 1143.0 |
| video game developer | 1078.0 |
| ethnic township | 1077.0 |
| ship class | 1038.0 |
| silent film | 1010.0 |
| tambon | 959.0 |
| fictional human | 929.0 |
| district of China | 872.0 |
| market municipality | 851.0 |
| railway line | 845.0 |
| island | 828.0 |
| basketball team | 816.0 |
| monotypic taxon | 815.0 |
| organization | 812.0 |
| amphoe | 787.0 |
| house | 782.0 |
| short film | 758.0 |
| extended play | 756.0 |
| district of Iran | 755.0 |
| rural settlement of Russia | 720.0 |
| Barn | 696.0 |
| building | 682.0 |
| Q2590631 | 679.0 |
| city of Japan | 648.0 |
| Road | 637.0 |
| version, edition, or translation | 635.0 |
| neighbourhood | 632.0 |
| art museum | 629.0 |
| rock group | 628.0 |
| Wikipedia:Portal | 604.0 |
| urban-rural municipality of Poland | 602.0 |
| municipality with town privileges in the Czech Republic | 602.0 |
| square | 593.0 |
| museum | 587.0 |
| local municipality of Quebec | 581.0 |
| municipality of Belgium | 575.0 |
| bridge | 567.0 |
| television film | 563.0 |
| cemetery | 558.0 |
| car model | 557.0 |
| buurtschap | 553.0 |
| twin | 539.0 |
| Q15410431 | 526.0 |
| census-designated place in the United States | 525.0 |
| aircraft model | 519.0 |
| UNESCO World Heritage Site | 513.0 |
| events in a specific year or time period | 513.0 |
| municipality | 512.0 |
| poem | 500.0 |
| Gemarkung | 496.0 |
| association football league | 493.0 |
| district of India | 491.0 |
| aircraft family | 478.0 |
| Dutch municipality | 466.0 |
| historical country | 455.0 |
| castle | 445.0 |
| manga | 440.0 |
| place with town rights and privileges | 436.0 |
| municipality section | 434.0 |
| municipality of Norway | 429.0 |
| villa | 421.0 |
| battle | 417.0 |
| mountain | 415.0 |
| film production company | 410.0 |
| former municipality of Switzerland | 407.0 |
| raion of Ukraine | 405.0 |
| kecamatan | 392.0 |
| Pokémon species | 391.0 |
| Ortsbezirk of Germany | 389.0 |
| hamlet | 384.0 |
| municipality of Finland | 380.0 |
| urban area in Sweden | 376.0 |
| carriage house | 373.0 |
| Counties of Iran | 372.0 |
| language | 371.0 |
| frazione | 371.0 |
| municipality of Sweden | 369.0 |
| point in time with respect to recurrent timeframe | 366.0 |
| farmhouse | 366.0 |
| video game series | 366.0 |
| regency of Indonesia | 365.0 |
| county-level city | 364.0 |
| sports venue | 359.0 |
| local government area of Australia | 354.0 |
| garden | 352.0 |
| publisher | 344.0 |
| quarter | 344.0 |
| arrondissement of France | 344.0 |
| work | 342.0 |
| district of Germany | 341.0 |
| sports club | 340.0 |
| château | 340.0 |
| service apartment | 338.0 |
| cathedral | 332.0 |
| department of Argentina | 331.0 |
| avenue | 329.0 |
| capital city | 329.0 |
| constituent locality | 326.0 |
| ice hockey team | 323.0 |
| Architectural structure | 322.0 |
| fence | 321.0 |
| Q17143521 | 321.0 |
| literary work | 320.0 |
| powiat of Poland | 318.0 |
| submarine class | 313.0 |
| town of Japan | 304.0 |
| lake | 303.0 |
| Casemate | 298.0 |
| music genre | 290.0 |
| railway stop | 288.0 |
| municipality of Portugal | 285.0 |
| municipality of Slovakia | 279.0 |
| cities of Ukraine | 279.0 |
| town in Hungary | 277.0 |
| book series | 276.0 |
| prefecture-level city | 274.0 |
| walkway | 266.0 |
| Q18752456 | 265.0 |
| New England town | 264.0 |
| miasteczko | 263.0 |
| freguesia of Portugal | 260.0 |
| geographical feature | 259.0 |
| airport | 258.0 |
| urban-type settlement | 257.0 |
| noble family | 257.0 |
| suburb | 255.0 |
| fictional astronomical object in the Serenityverse | 255.0 |
| letter | 250.0 |
| county of Texas | 249.0 |
| district of Turkey | 249.0 |
| school | 249.0 |
| men in Tolkien's legendarium | 249.0 |
| mountain range | 248.0 |
| rapid transit railway line | 247.0 |
| cultural property | 245.0 |
| abbey | 245.0 |
| urban municipality of Poland | 238.0 |
| commune of Chile | 235.0 |
| borough of Pennsylvania | 233.0 |
| decade | 232.0 |
| clergy house | 232.0 |
| ship type | 230.0 |
| cabinet | 229.0 |
| county seat | 228.0 |
| local municipality | 228.0 |
| engine model | 227.0 |
| big city | 227.0 |
| sovereign state | 220.0 |
| posyolok | 219.0 |
| statue of Sacred Heart of Jesus Christ | 218.0 |
| urban settlement in Russia | 218.0 |
| town in Romania | 218.0 |
| administrative territorial entity of the People's Republic of China | 217.0 |
| Q3409027 | 213.0 |
| television station | 212.0 |
| district | 211.0 |
| anime | 211.0 |
| palace | 204.0 |
| observatory | 203.0 |
| residential building | 203.0 |
| baseball team | 202.0 |
| Q14943515 | 202.0 |
| province of Peru | 195.0 |
| Q15916867 | 195.0 |
| district of Afghanistan | 194.0 |
| international airport | 193.0 |
| Election | 192.0 |
| remix album | 190.0 |
| village in the United States | 187.0 |
| literary award | 187.0 |
| stream | 187.0 |
| order | 182.0 |
| geographic region | 180.0 |
| room | 179.0 |
| district of Hungary | 175.0 |
| sports team | 170.0 |
| ward of Japan | 169.0 |
| dead end street | 169.0 |
| municipality of Bulgaria | 168.0 |
| million city | 168.0 |
| Municipalities of Estonia | 168.0 |
| Dynasty | 167.0 |
| miniseries | 166.0 |
| monastery | 166.0 |
| men's singles | 166.0 |
| government agency | 164.0 |
| Q14752149 | 164.0 |
| anime television program | 164.0 |
| national association football team | 164.0 |
| protected area | 163.0 |
| airline | 163.0 |
| non-metropolitan district | 162.0 |
| skyscraper | 161.0 |
| municipality of Greece | 161.0 |
| language family | 161.0 |
| Q16423655 | 161.0 |
| Q2200223 | 161.0 |
| musical composition | 159.0 |
| county of Georgia | 159.0 |
| wizard in the Harry Potter universe | 159.0 |
| diocese | 155.0 |
| municipality of Colombia | 155.0 |
| unincorporated community in the United States | 155.0 |
| trilogy | 153.0 |
| national museum | 153.0 |
| fictional moon | 152.0 |
| animated film | 150.0 |
| district of Algeria | 150.0 |
| legislative term | 150.0 |
| windmill | 147.0 |
| national sports team | 147.0 |
| big district town | 146.0 |
| country | 145.0 |
| college | 145.0 |
| deity | 145.0 |
| duo | 141.0 |
| Opera | 140.0 |
| triangular number | 140.0 |
| barangay | 140.0 |
| Q14934048 | 140.0 |
| biographical article | 140.0 |
| aerospace manufacturer | 139.0 |
| novel | 138.0 |
| wall | 138.0 |
| currency | 137.0 |
| high school | 137.0 |
| national anthem | 136.0 |
| newspaper | 136.0 |
| partido of Buenos Aires | 135.0 |
| US Open | 134.0 |
| human spaceflight | 134.0 |
| Pokémon evolutionary line | 133.0 |
| Q13516667 | 133.0 |
| Belgian municipality with the title of city | 132.0 |
| playing card | 132.0 |
| law school | 131.0 |
| garden square | 129.0 |
| ethnic group | 129.0 |
| business | 128.0 |
| commune of Algeria | 128.0 |
| bay | 128.0 |
| fountain | 128.0 |
| Wimbledon Championships | 128.0 |
| gardener house | 127.0 |
| motorcycle | 127.0 |
| archipelago | 126.0 |
| municipality of Slovenia | 126.0 |
| cultural heritage site in Slovenia | 126.0 |
| film series | 126.0 |
| deme | 124.0 |
| studio album | 124.0 |
| Chemical element | 122.0 |
| fictional location | 120.0 |
| administrative territorial entity of Ukraine | 120.0 |
| historic house museum | 119.0 |
| magazine | 119.0 |
| district of Belarus | 119.0 |
| shed | 119.0 |
| Chemical compound | 118.0 |
| township of New Jersey | 117.0 |
| school building | 117.0 |
| sculpture series | 117.0 |
| film studio | 116.0 |
| boulevard | 116.0 |
| autonomous county | 116.0 |
| film genre | 115.0 |
| park | 115.0 |
| sumu | 114.0 |
| French Open | 114.0 |
| air force | 113.0 |
| sibling duo | 113.0 |
| Conflict | 113.0 |
| municipalities and cities of Serbia | 113.0 |
| district of Uganda | 112.0 |
| military museum | 112.0 |
| kabushiki gaisha | 112.0 |
| county of Kentucky | 111.0 |
| department of France | 111.0 |
| Nemzeti Bajnokság I | 111.0 |
| Superhero | 111.0 |
| Q3685430 | 111.0 |
| Archdiocese | 111.0 |
| Paris–Roubaix | 111.0 |
| Urban park | 110.0 |
| municipality of Latvia | 110.0 |
| sports league | 110.0 |
| engine family | 109.0 |
| province of Italy | 109.0 |
| play | 108.0 |
| Khwaeng | 107.0 |
| water deity | 107.0 |
| concept | 107.0 |
| county of Missouri | 106.0 |
| village of Wisconsin | 105.0 |
| hospital | 105.0 |
| armed forces | 103.0 |
| thoroughfare | 103.0 |
| municipiu of Romania | 103.0 |
| Tour de France | 103.0 |
| Australian Open | 103.0 |
| rural district of Iran | 102.0 |
| Q17468533 | 102.0 |
| municipality of Denmark | 102.0 |
| Besta deild karla | 102.0 |
| city of the United States | 101.0 |
| Davis Cup | 101.0 |
| war | 100.0 |
| square number | 100.0 |
| World Congress of Esperanto | 100.0 |
| Architectural style | 100.0 |
| pronic number | 99.0 |
| navy | 99.0 |
| fourth-class city | 99.0 |
| voluntary association | 99.0 |
| locality | 98.0 |
| natural satellite | 98.0 |
| London Underground station | 98.0 |
| art school | 98.0 |
| Giro d'Italia | 97.0 |
| county of North Carolina | 97.0 |
| county of Illinois | 97.0 |
| gate building | 97.0 |
| category A listed building | 97.0 |
| rugby union team | 97.0 |
| district of Austria | 95.0 |
| county of Iowa | 95.0 |
| military rank | 95.0 |
| county of Virginia | 94.0 |
| Provinces of Bolivia | 94.0 |
| activity | 93.0 |
| literary genre | 93.0 |
| unisex given name | 93.0 |
| county of Tennessee | 93.0 |
| Craft | 93.0 |
| article | 93.0 |
| bicameral legislature | 92.0 |
| noble title | 92.0 |
| county of Indiana | 92.0 |
| musical ensemble | 92.0 |
| married couple | 91.0 |
| arch bridge | 91.0 |
| subdistrict of the German Democratic Republic | 91.0 |
| Q3559083 | 91.0 |
| terrace of houses | 90.0 |
| Esperanto organisation | 90.0 |
| county of Kansas | 89.0 |
| airplane | 89.0 |
| city of the Philippines | 89.0 |
| municipality of Luxembourg | 89.0 |
| county of Ohio | 88.0 |
| arena | 88.0 |
| road bridge | 88.0 |
| art movement | 88.0 |
| canal | 88.0 |
| First Professional Football League | 88.0 |
| handball team | 87.0 |
| historic district in the United States | 87.0 |
| orangery | 86.0 |
| SAT Congress | 86.0 |
| regional county municipality | 86.0 |
| S-Bahn station | 86.0 |
| municipality of Mexico | 86.0 |
| family | 85.0 |
| Academy Awards ceremony | 85.0 |
| warehouse | 85.0 |
| city of Indonesia | 85.0 |
| Q1391143 | 84.0 |
| Q15070223 | 84.0 |
| fictional planet | 84.0 |
| academy of sciences | 83.0 |
| destroyed building or structure | 83.0 |
| fresco | 83.0 |
| municipality of North Macedonia | 83.0 |
| subdistrict administrative organization | 83.0 |
| residential community of the People's Republic of China | 83.0 |
| archaeological site | 82.0 |
| Maltese Premier League | 82.0 |
| centered triangular number | 82.0 |
| fencepost | 82.0 |
| county of Mississippi | 81.0 |
| administrative quarter of Paris | 81.0 |
| pentagonal number | 81.0 |
| octagonal number | 81.0 |
| UCI Road World Championships | 81.0 |
| province of Turkey | 81.0 |
| port settlement | 81.0 |
| province of Thailand | 80.0 |
| video game genre | 80.0 |
| province of the Philippines | 80.0 |
| transport route | 80.0 |
| municipality of Croatia | 80.0 |
| Q16054233 | 80.0 |
| office building | 79.0 |
| English country house | 79.0 |
| communist party | 79.0 |
| district of Slovakia | 79.0 |
| operating system | 79.0 |
| county of Nebraska | 78.0 |
| chapel | 78.0 |
| aspect of history | 78.0 |
| city gate | 78.0 |
| formation | 78.0 |
| locomotive class | 78.0 |
| county of Oklahoma | 77.0 |
| county of Minnesota | 77.0 |
| architectural heritage monument in North Rhine-Westphalia | 77.0 |
| public university | 77.0 |
| political party in Spain | 77.0 |
| work of art | 77.0 |
| religious text | 76.0 |
| Q15632166 | 76.0 |
| Genre | 76.0 |
| station square | 76.0 |
| tram stop | 76.0 |
| tournament | 76.0 |
| Liga Portugal | 76.0 |
| districts of the Czech Republic | 76.0 |
| governorate | 76.0 |
| commune of Benin | 75.0 |
| reservoir | 75.0 |
| FA Cup Final | 75.0 |
| county of Arkansas | 75.0 |
| drama television series | 75.0 |
| tennis tournament | 75.0 |
| Parish church | 74.0 |
| Short story | 74.0 |
| community college | 73.0 |
| grave | 73.0 |
| Q18756633 | 73.0 |
| bastion | 72.0 |
| township of Pennsylvania | 72.0 |
| Wikimedia template | 72.0 |
| county of Wisconsin | 72.0 |
| Century | 72.0 |
| Q13460939 | 71.0 |
| centered square number | 71.0 |
| drawing | 71.0 |
| Argentine Primera División | 71.0 |
| sundial | 70.0 |
| Q19311591 | 70.0 |
| statue | 70.0 |
| Middle-earth elf | 70.0 |
| International Youth Congress of Esperanto | 70.0 |
| hexagonal number | 70.0 |
| rathaus | 70.0 |
| Danish Superliga | 69.0 |
| Czechoslovak First League | 69.0 |
| animation studio | 69.0 |
| constellation | 69.0 |
| army | 69.0 |
| Q17143371 | 68.0 |
| sled dog racing | 68.0 |
| Public company | 68.0 |
| basilica | 68.0 |
| American football team | 68.0 |
| architectural firm | 68.0 |
| Q15974311 | 68.0 |
| free software | 68.0 |
| county of Michigan | 67.0 |
| television channel | 67.0 |
| girl group | 67.0 |
| gracht | 67.0 |
| county of Alabama | 67.0 |
| county of Pennsylvania | 67.0 |
| website | 67.0 |
| county of Florida | 67.0 |
| shipyard | 67.0 |
| gazebo | 67.0 |
| Q1092563 | 66.0 |
| Venice Film Festival | 66.0 |
| Cartridge | 66.0 |
| city with powiat rights | 66.0 |
| county of South Dakota | 66.0 |
| Vuelta a España | 66.0 |
| Cannes Film Festival | 66.0 |
| rural municipality of Sweden and Finland | 66.0 |
| Avenue | 66.0 |
| painting series | 65.0 |
| Valley | 65.0 |
| discipline | 65.0 |
| Olympic sporting event | 65.0 |
| district of Japan | 65.0 |
| chapter | 65.0 |
| fictional universe | 65.0 |
| Q19689753 | 65.0 |
| Q15966903 | 65.0 |
| District of Bangladesh | 64.0 |
| media franchise | 64.0 |
| single entity of population | 64.0 |
| heptagonal number | 63.0 |
| academic degree | 63.0 |
| theatre | 63.0 |
| Eastern Orthodox church | 63.0 |
| art collection | 63.0 |
| centered pentagonal number | 63.0 |
| island group | 63.0 |
| county of Colorado | 63.0 |
| Q15099348 | 63.0 |
| Q3666499 | 62.0 |
| group of fictional characters | 62.0 |
| Berlin International Film Festival | 62.0 |
| group of humans | 62.0 |
| National heritage site | 62.0 |
| peninsula | 61.0 |
| village-level division in China | 61.0 |
| research institute | 61.0 |
| Q10868922 | 61.0 |
| district of Azerbaijan | 61.0 |
| seminary | 61.0 |
| curtain wall | 61.0 |
| ship | 61.0 |
| council of Asturies | 60.0 |
| World Allround Speed Skating Championships for Men | 60.0 |
| fictional country | 60.0 |
| animated series | 60.0 |
| identical twins | 60.0 |
| Eurovision Song Contest | 60.0 |
| military academy | 60.0 |
| subdistrict municipality | 60.0 |
| Q3201814 | 60.0 |
| civil town of Wisconsin | 60.0 |
| public holiday | 60.0 |
| radio station | 60.0 |
| Wightman Cup | 59.0 |
| Memphis Open | 59.0 |
| province of Vietnam | 59.0 |
| holding company | 59.0 |
| Sea | 59.0 |
| county of New York | 59.0 |
| municipality of Cuba | 59.0 |
| controlled-access highway | 59.0 |
| Districts of Kazakhstan | 59.0 |
| decentralized municipal entity | 59.0 |
| municipality of Puerto Rico | 58.0 |
| local council of Malta | 58.0 |
| tower | 58.0 |
| Japan Open Tennis Championships | 58.0 |
| centered hexagonal number | 58.0 |
| programming language | 58.0 |
| David di Donatello | 58.0 |
| Nereids | 58.0 |
| flag | 57.0 |
| county of California | 57.0 |
| state | 57.0 |
| factory | 57.0 |
| unicameralism | 57.0 |
| Eredivisie | 57.0 |
| pier | 57.0 |
| Comics | 57.0 |
| district of Peru | 56.0 |
| Norse mythical character | 56.0 |
| Vice-county | 56.0 |
| architectural heritage monument | 55.0 |
| star | 55.0 |
| polder | 55.0 |
| parliament | 55.0 |
| ministry | 55.0 |
| installation art | 54.0 |
| Hobbit | 54.0 |
| higher education institution | 54.0 |
| stanitsa | 54.0 |
| province of Chile | 54.0 |
| Q687312 | 54.0 |
| county of West Virginia | 54.0 |
| railway company | 53.0 |
| courage award | 53.0 |
| national park | 53.0 |
| women's singles | 53.0 |
| nonagonal number | 53.0 |
| Billie Jean King Cup | 53.0 |
| United Kingdom general election | 53.0 |
| Q14846918 | 53.0 |
| aircraft crash | 53.0 |
| island nation | 53.0 |
| parish of Louisiana | 53.0 |
| destroyer | 53.0 |
| road number | 52.0 |
| list of rijkswegen | 52.0 |
| banner | 52.0 |
| calendar date | 52.0 |
| Spanish provinces | 52.0 |
| Canadian Open | 52.0 |
| centered heptagonal number | 52.0 |
| music school | 52.0 |
| urban area in Norway | 51.0 |
| county of Montana | 51.0 |
| British Academy of Film and Television Arts | 51.0 |
| watercourse | 51.0 |
| Extrasolar planet | 51.0 |
| administrative territorial entity | 51.0 |
| liberal arts college in the United States | 51.0 |
| private university | 50.0 |
| centered octagonal number | 50.0 |
| grand ensemble | 50.0 |
| barracks | 50.0 |
| decagonal number | 50.0 |
| automobile manufacturer | 50.0 |
| Q3088847 | 50.0 |
| Q2559925 | 50.0 |
| Q15618652 | 50.0 |
| Fußball-Bundesliga | 50.0 |
| library | 50.0 |
| transport company | 50.0 |
| expedition to the International Space Station | 50.0 |
| Süper Lig | 50.0 |
| U.S. state | 50.0 |
| village of Japan | 49.0 |
| unorganized area of Canada | 49.0 |
| taxonomic rank | 49.0 |
| Q649434 | 49.0 |
| townhouse | 49.0 |
| women's doubles | 49.0 |
| brand | 49.0 |
| district of Pakistan | 48.0 |
| Soviet Top League | 48.0 |
| Amstel Gold Race | 48.0 |
| general election | 48.0 |
| Thesaban Mueang | 48.0 |
| legislature of a U.S. state | 48.0 |
| Prefecture of Japan | 48.0 |
| Q1852178 | 48.0 |
| Eliteserien | 48.0 |
| Q17268368 | 48.0 |
| recurring tournament | 48.0 |
| Stadtbezirk | 48.0 |
| Revolutionary section of Paris | 48.0 |
| airbase | 47.0 |
| supervillain | 47.0 |
| oblasts of Russia | 47.0 |
| designated spa town | 47.0 |
| district capital | 47.0 |
| landlocked country | 47.0 |
| centered nonagonal number | 47.0 |
| hotel | 47.0 |
| province of Algeria | 47.0 |
| Q15634531 | 47.0 |
| eingetragener Verein | 46.0 |
| business school | 46.0 |
| Cincinnati Masters | 46.0 |
| Miss France | 46.0 |
| name | 46.0 |
| dwarves in Tolkien's legendarium | 46.0 |
| mutant | 46.0 |
| Q3292203 | 46.0 |
| Monte-Carlo Masters | 46.0 |
| Italian Open | 46.0 |
| Courtyard | 46.0 |
| Military organization | 46.0 |
| county of South Carolina | 45.0 |
| province of Burkina Faso | 45.0 |
| dodecagonal number | 45.0 |
| Washington Open | 45.0 |
| district municipality | 45.0 |
| Q18759150 | 45.0 |
| Q2160811 | 45.0 |
| district of Prussia | 45.0 |
| college town | 45.0 |
| list of municipalities of Albania | 45.0 |
| centered decagonal number | 45.0 |
| Q1687964 | 44.0 |
| art gallery | 44.0 |
| Q17299692 | 44.0 |
| district of Moscow | 44.0 |
| scientific journal | 44.0 |
| district of Uzbekistan | 44.0 |
| Q1192195 | 44.0 |
| workshop | 44.0 |
| color | 44.0 |
| Drama school | 44.0 |
| fictional river | 43.0 |
| nonprofit organization | 43.0 |
| county of North Dakota | 43.0 |
| Q17301072 | 43.0 |
| strait | 43.0 |
| comarca of Catalonia | 43.0 |
| Barcelona Open | 43.0 |
| retaining wall | 43.0 |
| water pump | 43.0 |
| station building | 43.0 |
| Iditarod Trail Sled Dog Race | 43.0 |
| dzielnica | 43.0 |
| international organization | 43.0 |
| județ | 42.0 |
| May | 42.0 |
| university press | 42.0 |
| WTA Tour | 42.0 |
| core city of Japan | 42.0 |
| César Award | 42.0 |
| Paris Masters | 42.0 |
| ancient city | 42.0 |
| aircraft class | 42.0 |
| Indian Wells Masters | 42.0 |
| lower house | 41.0 |
| star number | 41.0 |
| proprietary software | 41.0 |
| January | 41.0 |
| squadron | 41.0 |
| main stream | 41.0 |
| borough | 41.0 |
| Q18779194 | 41.0 |
| Q18762207 | 40.0 |
| recurring sporting event | 40.0 |
| neighbourhood of Buenos Aires | 40.0 |
| foundation | 40.0 |
| term of the Canadian federal parliament | 40.0 |
| medical specialty | 40.0 |
| March | 40.0 |
| Han surname | 40.0 |
| building complex | 40.0 |
| February | 40.0 |
| independent city | 40.0 |
| special city of Japan | 40.0 |
| stock exchange | 40.0 |
| smartphone model | 40.0 |
| fort | 40.0 |
| medical school | 39.0 |
| Milan – San Remo | 39.0 |
| historical motorcycle manufacturer | 39.0 |
| millennium | 39.0 |
| hill | 39.0 |
| April | 39.0 |
| department of Cameroon | 39.0 |
| industrial sector | 39.0 |
| political organization | 39.0 |
| subdistrict of the canton of Graubünden | 39.0 |
| video game console | 39.0 |
| city district | 39.0 |
| June | 39.0 |
| Erste Bank Open | 39.0 |
| Q16522751 | 39.0 |
| Low German house | 39.0 |
| rural municipality of Canada | 39.0 |
| World Athletics Cross Country Championships | 39.0 |
| county of Washington | 39.0 |
| old town | 39.0 |
| Wheel arrangement | 39.0 |
| county of Idaho | 38.0 |
| county | 38.0 |
| December | 38.0 |
| subdistrict of the canton of Ticino | 38.0 |
| private mansion | 38.0 |
| hay barrack | 38.0 |
| goddess | 38.0 |
| tetrahedral number | 38.0 |
| fictional pony | 38.0 |
| November | 38.0 |
| FIBA EuroBasket | 38.0 |
| upper house | 38.0 |
| Buddhist text | 37.0 |
| United Nations Security Council resolution | 37.0 |
| museum ship | 37.0 |
| clade | 37.0 |
| republic | 37.0 |
| Q18752578 | 37.0 |
| July | 37.0 |
| Municipalities of Venezuela | 37.0 |
| October | 37.0 |
| dissolved municipality of Japan | 37.0 |
| district of Albania | 37.0 |
| World Military Cup | 37.0 |
| isotope of tellurium | 37.0 |
| light cruiser | 37.0 |
| university building | 37.0 |
| title of honor | 37.0 |
| August | 36.0 |
| note | 36.0 |
| state of Nigeria | 36.0 |
| comic book series | 36.0 |
| mansion | 36.0 |
| college of the University of Oxford | 36.0 |
| municipality of Iceland | 36.0 |
| woodcut print | 36.0 |
| Advocacy group | 36.0 |
| pastel | 36.0 |
| Stuttgart Open | 36.0 |
| territorial authority of New Zealand | 36.0 |
| natural landscape | 35.0 |
| district of Nepal | 35.0 |
| wine producing locality | 35.0 |
| autocannon | 35.0 |
| county of Oregon | 35.0 |
| archaeological culture | 35.0 |
| Q17518866 | 35.0 |
| game engine | 35.0 |
| primary area | 35.0 |
| political coalition | 35.0 |
| September | 35.0 |
| racing automobile | 35.0 |
| high-rise building | 35.0 |
| monument | 35.0 |
| Q2555200 | 35.0 |
| Papal conclave | 34.0 |
| Q2280652 | 34.0 |
| Ice Hockey World Championships | 34.0 |
| residential area | 34.0 |
| medieval battle | 34.0 |
| sibling group | 34.0 |
| Q2316398 | 34.0 |
| Foreign Office | 34.0 |
| manuscript | 34.0 |
| province of Indonesia | 34.0 |
| series of creative works | 34.0 |
| Q17372500 | 34.0 |
| World Allround Speed Skating Championships | 34.0 |
| Q766277 | 34.0 |
| statistical neighborhood of Zürich | 34.0 |
| sutra | 34.0 |
| province of Afghanistan | 34.0 |
| Moscow International Film Festival | 34.0 |
| educational institution | 34.0 |
| Q18779123 | 34.0 |
| administrative territorial entity of Germany | 34.0 |
| London borough | 34.0 |
| lycée | 34.0 |
| stone bridge | 34.0 |
| rugby league team | 34.0 |
| Tour of Flanders | 34.0 |
| Soyuz-TM | 33.0 |
| Faroe Islands Premier League | 33.0 |
| bakehouse | 33.0 |
| twinning | 33.0 |
| presidential election | 33.0 |
| fictional organization | 33.0 |
| Okeanid | 33.0 |
| lower-tier municipality | 33.0 |
| space probe | 33.0 |
| rapid transit | 33.0 |
| television pilot | 33.0 |
| quarter of Hamburg | 33.0 |
| Q15623573 | 33.0 |
| Q18523902 | 33.0 |
| Summer Olympic Games | 33.0 |
| Q1394653 | 33.0 |
| railway bridge | 33.0 |
| fictional taxon | 33.0 |
| state of Mexico | 32.0 |
| war memorial | 32.0 |
| event | 32.0 |
| list of districts and neighborhoods of Los Angeles | 32.0 |
| Yukon Quest | 32.0 |
| major regional center | 32.0 |
| province of Iran | 32.0 |
| encyclopaedia | 32.0 |
| Q606986 | 32.0 |
| water tower | 32.0 |
| military cemetery | 32.0 |
| Boston Society of Film Critics | 32.0 |
| prison | 32.0 |
| Mexican Open | 32.0 |
| isotope of silver | 32.0 |
| concert hall | 32.0 |
| government | 32.0 |
| artist collective | 32.0 |
| Scottish council area | 32.0 |
| Q15068450 | 32.0 |
| Q1321542 | 32.0 |
| baseball venue | 32.0 |
| Q17272482 | 32.0 |
| Centralbahnhof | 32.0 |
| provincial city | 32.0 |
| Category:December 2010 events | 31.0 |
| Miami Open | 31.0 |
| Category:March 2008 events | 31.0 |
| Category:May 2011 events | 31.0 |
| liberal arts college | 31.0 |
| province of the Dominican Republic | 31.0 |
| Category:August 2006 events | 31.0 |
| Q9615454 | 31.0 |
| social networking service | 31.0 |
| Q15272960 | 31.0 |
| Category:August 2008 events | 31.0 |
| fictional battle | 31.0 |
| Q9420592 | 31.0 |
| Category:January 2006 events | 31.0 |
| Category:January 2011 events | 31.0 |
| Q9676942 | 31.0 |
| Category:August 2005 events | 31.0 |
| Category:May 2005 events | 31.0 |
| Q9512666 | 31.0 |
| Category:July 2010 events | 31.0 |
| home computer | 31.0 |
| Q9676937 | 31.0 |
| fortress | 31.0 |
| government organization | 31.0 |
| Q9569288 | 31.0 |
| amusement park | 31.0 |
| Christian minister | 31.0 |
| Category:August 2010 events | 31.0 |
| Category:July 2011 events | 31.0 |
| Category:January 2015 events | 31.0 |
| isotope of caesium | 31.0 |
| Category:January 2008 events | 31.0 |
| Q9676945 | 31.0 |
| Q9617364 | 31.0 |
| Category:March 2010 events | 31.0 |
| Golden Raspberry Awards | 31.0 |
| Q9615446 | 31.0 |
| city in the state of New York | 31.0 |
| Category:October 2010 events | 31.0 |
| Q9617371 | 31.0 |
| Q1377841 | 31.0 |
| Vikings | 31.0 |
| county of New Mexico | 31.0 |
| Category:May 2010 events | 31.0 |
| fortified town | 31.0 |
| Category:July 2005 events | 31.0 |
| Political parties in Russia | 31.0 |
| Category:May 2008 events | 31.0 |
| aviation museum | 31.0 |
| human-made geographic feature | 31.0 |
| Departments of Colombia | 31.0 |
| Q9569278 | 31.0 |
| kinship | 31.0 |
| Q9512652 | 31.0 |
| Q9512656 | 31.0 |
| Q736812 | 31.0 |
| Category:March 2011 events | 31.0 |
| Category:July 2008 events | 31.0 |
| isotope of thallium | 30.0 |
| Q1208453 | 30.0 |
| television network | 30.0 |
| Category:September 2005 events | 30.0 |
As expected we find some large, general categories at the top. In particular "human", which supports our hypothesis that much of the knowledge graph is concerned with people. Interestingly we also find two music-related types in the top: "album" and "single".
We can use these types to investigate trends in in- and out-degree for specific node types. The cell below finds the top 10 types and filters the dataframe with node-degrees to only contain these. We then create a scatter-plot for a subset of the data.
val topTenTypes = typeCounts.limit(10)
val typeFiltered = joinedTypes.join(topTenTypes, List("type"), "inner")
display(typeFiltered)
| type | id | inDegree | outDegree | count |
|---|---|---|---|---|
| album | & Yet & Yet | 2.0 | 5.0 | 49063.0 |
| asteroid | (10499) 1986 RN5 | 2.0 | 6.0 | 40573.0 |
| asteroid | (11058) 1991 PN10 | 2.0 | 6.0 | 40573.0 |
| asteroid | (117404) 2005 AC9 | 1.0 | 5.0 | 40573.0 |
| asteroid | (13020) 1988 PW2 | 1.0 | 5.0 | 40573.0 |
| asteroid | (136198) 2003 UJ296 | 1.0 | 5.0 | 40573.0 |
| asteroid | (15141) 2000 EP106 | 2.0 | 4.0 | 40573.0 |
| asteroid | (15683) 1981 EX25 | 2.0 | 6.0 | 40573.0 |
| asteroid | (16307) 7569 P-L | 2.0 | 8.0 | 40573.0 |
| asteroid | (16467) 1990 FD3 | 2.0 | 6.0 | 40573.0 |
| asteroid | (17383) 1981 EE12 | 2.0 | 6.0 | 40573.0 |
| asteroid | (20188) 1997 AC18 | 2.0 | 6.0 | 40573.0 |
| asteroid | (20671) 1999 UX48 | 2.0 | 5.0 | 40573.0 |
| asteroid | (20927) 1126 T-1 | 2.0 | 8.0 | 40573.0 |
| asteroid | (21340) 1997 CS19 | 2.0 | 6.0 | 40573.0 |
| asteroid | (21944) 1999 VA118 | 2.0 | 6.0 | 40573.0 |
| asteroid | (21996) 1999 XP31 | 2.0 | 6.0 | 40573.0 |
| asteroid | (22133) 2000 UO56 | 2.0 | 6.0 | 40573.0 |
| asteroid | (22288) 1988 TR2 | 2.0 | 6.0 | 40573.0 |
| asteroid | (22313) 1991 GP3 | 2.0 | 6.0 | 40573.0 |
| asteroid | (22511) 1997 YC10 | 2.0 | 6.0 | 40573.0 |
| asteroid | (22726) 1998 SZ72 | 2.0 | 6.0 | 40573.0 |
| asteroid | (22755) 1998 WO9 | 2.0 | 6.0 | 40573.0 |
| asteroid | (23299) 2001 AP9 | 2.0 | 6.0 | 40573.0 |
| asteroid | (23723) 1998 HG40 | 2.0 | 6.0 | 40573.0 |
| asteroid | (24205) 1999 XC48 | 2.0 | 6.0 | 40573.0 |
| asteroid | (24831) 1995 SX4 | 2.0 | 6.0 | 40573.0 |
| asteroid | (25571) 1999 XP195 | 2.0 | 6.0 | 40573.0 |
| asteroid | (257203) 2008 RW122 | 2.0 | 6.0 | 40573.0 |
| asteroid | (25831) 2000 DH111 | 2.0 | 6.0 | 40573.0 |
| asteroid | (26030) 6004 P-L | 2.0 | 8.0 | 40573.0 |
| asteroid | (26094) 1988 NU | 2.0 | 6.0 | 40573.0 |
| asteroid | (26476) 2000 AK185 | 2.0 | 6.0 | 40573.0 |
| asteroid | (27142) 1998 XG61 | 2.0 | 6.0 | 40573.0 |
| asteroid | (27242) 1999 TN219 | 2.0 | 5.0 | 40573.0 |
| asteroid | (27484) 2000 GN94 | 2.0 | 6.0 | 40573.0 |
| asteroid | (28247) 1999 BP3 | 2.0 | 6.0 | 40573.0 |
| asteroid | (28404) 1999 TQ5 | 2.0 | 7.0 | 40573.0 |
| asteroid | (28463) 2000 AG168 | 2.0 | 5.0 | 40573.0 |
| asteroid | (28580) 2000 EJ104 | 2.0 | 6.0 | 40573.0 |
| asteroid | (28804) 2000 HC81 | 2.0 | 6.0 | 40573.0 |
| asteroid | (28960) 2001 DZ81 | 2.0 | 6.0 | 40573.0 |
| asteroid | (29000) 2607 P-L | 2.0 | 8.0 | 40573.0 |
| asteroid | (29022) 6630 P-L | 2.0 | 8.0 | 40573.0 |
| asteroid | (29387) 1996 JC6 | 2.0 | 6.0 | 40573.0 |
| asteroid | (29505) 1997 WV44 | 2.0 | 6.0 | 40573.0 |
| asteroid | (30466) 2000 OP14 | 2.0 | 6.0 | 40573.0 |
| asteroid | (30611) 2627 P-L | 2.0 | 8.0 | 40573.0 |
| asteroid | (30644) 6601 P-L | 2.0 | 8.0 | 40573.0 |
| asteroid | (30689) 4318 T-2 | 2.0 | 8.0 | 40573.0 |
| asteroid | (30845) 1991 PQ3 | 2.0 | 6.0 | 40573.0 |
| asteroid | (30896) 1993 FX26 | 2.0 | 6.0 | 40573.0 |
| asteroid | (31148) 1997 UO8 | 2.0 | 6.0 | 40573.0 |
| asteroid | (31259) 1998 EB3 | 2.0 | 6.0 | 40573.0 |
| asteroid | (31394) 1998 YX9 | 2.0 | 7.0 | 40573.0 |
| asteroid | (31497) 1999 CW61 | 2.0 | 6.0 | 40573.0 |
| asteroid | (31554) 1999 EJ2 | 2.0 | 6.0 | 40573.0 |
| asteroid | (31732) 1999 JB71 | 2.0 | 6.0 | 40573.0 |
| asteroid | (32382) 2000 QE187 | 2.0 | 6.0 | 40573.0 |
| asteroid | (32543) 2001 QL11 | 2.0 | 6.0 | 40573.0 |
| asteroid | (32791) 1989 TQ2 | 2.0 | 6.0 | 40573.0 |
| asteroid | (34242) 2000 QD100 | 2.0 | 6.0 | 40573.0 |
| asteroid | (343976) 2011 LC21 | 1.0 | 5.0 | 40573.0 |
| asteroid | (34712) 2001 ON103 | 2.0 | 6.0 | 40573.0 |
| asteroid | (34931) 6621 P-L | 2.0 | 8.0 | 40573.0 |
| asteroid | (35035) 1981 ER29 | 2.0 | 6.0 | 40573.0 |
| asteroid | (35051) 1981 ED47 | 2.0 | 6.0 | 40573.0 |
| asteroid | (35166) 1993 QD8 | 2.0 | 6.0 | 40573.0 |
| asteroid | (35182) 1993 US1 | 2.0 | 6.0 | 40573.0 |
| asteroid | (35224) 1995 BN1 | 2.0 | 6.0 | 40573.0 |
| asteroid | (35253) 1996 AB7 | 2.0 | 6.0 | 40573.0 |
| asteroid | (35480) 1998 FN5 | 2.0 | 5.0 | 40573.0 |
| asteroid | (35743) 1999 GP29 | 2.0 | 6.0 | 40573.0 |
| asteroid | (35803) 1999 JT40 | 2.0 | 6.0 | 40573.0 |
| asteroid | (36050) 1999 RE18 | 2.0 | 6.0 | 40573.0 |
| asteroid | (36481) 2000 QU30 | 2.0 | 6.0 | 40573.0 |
| asteroid | (37085) 2000 UO63 | 2.0 | 6.0 | 40573.0 |
| asteroid | (37160) 2000 WR5 | 2.0 | 6.0 | 40573.0 |
| asteroid | (37427) 2001 YJ82 | 2.0 | 6.0 | 40573.0 |
| asteroid | (37438) 2599 P-L | 2.0 | 8.0 | 40573.0 |
| asteroid | (37581) 1990 SU15 | 2.0 | 6.0 | 40573.0 |
| asteroid | (37697) 1995 YW4 | 2.0 | 6.0 | 40573.0 |
| asteroid | (38113) 1999 JB30 | 2.0 | 6.0 | 40573.0 |
| asteroid | (38403) 1999 RU197 | 2.0 | 6.0 | 40573.0 |
| asteroid | (38620) 2000 AQ186 | 2.0 | 6.0 | 40573.0 |
| asteroid | (39099) 2000 WS12 | 2.0 | 5.0 | 40573.0 |
| asteroid | (39298) 2001 FV132 | 2.0 | 5.0 | 40573.0 |
| asteroid | (39431) 5178 T-2 | 2.0 | 8.0 | 40573.0 |
| asteroid | (46556) 1991 FU3 | 1.0 | 5.0 | 40573.0 |
| asteroid | (58167) 1990 QM3 | 1.0 | 5.0 | 40573.0 |
| asteroid | (65225) 2002 EK44 | 1.0 | 5.0 | 40573.0 |
| asteroid | (6861) 1991 FA3 | 2.0 | 6.0 | 40573.0 |
| asteroid | (70304) 1999 RE133 | 1.0 | 5.0 | 40573.0 |
| asteroid | (73077) 2002 GT4 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73262) 2002 JK47 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73289) 2002 JW64 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73291) 2002 JG65 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73335) 2002 JN110 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73344) 2002 JT119 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73455) 2002 NT36 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73550) 2003 PG9 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73881) 1997 CD22 | 2.0 | 6.0 | 40573.0 |
| asteroid | (73924) 1997 MN3 | 2.0 | 6.0 | 40573.0 |
| asteroid | (74054) 1998 JT4 | 2.0 | 6.0 | 40573.0 |
| asteroid | (74411) 1999 AE5 | 2.0 | 6.0 | 40573.0 |
| asteroid | (76834) 2000 SA244 | 1.0 | 5.0 | 40573.0 |
| asteroid | (7951) 1992 WC2 | 2.0 | 6.0 | 40573.0 |
| asteroid | (82293) 2001 KJ38 | 2.0 | 6.0 | 40573.0 |
| asteroid | (82321) 2001 KE69 | 2.0 | 6.0 | 40573.0 |
| asteroid | (82945) 2001 QN117 | 2.0 | 6.0 | 40573.0 |
| asteroid | (9343) 1991 PO11 | 2.0 | 6.0 | 40573.0 |
| asteroid | (9575) 1989 BW1 | 2.0 | 6.0 | 40573.0 |
| album | ...With the Spirit of a Traffic Jam... | 1.0 | 4.0 | 49063.0 |
| album | 07 | 1.0 | 4.0 | 49063.0 |
| asteroid | 10970 de Zeeuw | 2.0 | 8.0 | 40573.0 |
| album | 10th Anniversary Album | 2.0 | 5.0 | 49063.0 |
| asteroid | 11055 Honduras | 2.0 | 6.0 | 40573.0 |
| asteroid | 11087 Yamasakimakoto | 2.0 | 7.0 | 40573.0 |
| asteroid | 1110 Jaroslawa | 2.0 | 6.0 | 40573.0 |
| asteroid | 11152 Oomine | 2.0 | 6.0 | 40573.0 |
| asteroid | 11365 NASA | 1.0 | 5.0 | 40573.0 |
| asteroid | 11581 Philipdejager | 2.0 | 6.0 | 40573.0 |
| asteroid | 11773 Schouten | 2.0 | 9.0 | 40573.0 |
| asteroid | 12161 Avienius | 2.0 | 9.0 | 40573.0 |
| asteroid | 13226 Soulié | 2.0 | 7.0 | 40573.0 |
| asteroid | 14424 Laval | 2.0 | 7.0 | 40573.0 |
| asteroid | 14499 Satotoshio | 2.0 | 7.0 | 40573.0 |
| asteroid | 15506 Preygel | 2.0 | 6.0 | 40573.0 |
| asteroid | 16077 Arayhamilton | 2.0 | 6.0 | 40573.0 |
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| asteroid | 1820 Lohmann | 2.0 | 6.0 | 40573.0 |
| asteroid | 18294 Rudenko | 2.0 | 6.0 | 40573.0 |
| asteroid | 1865 Cerberus | 2.0 | 7.0 | 40573.0 |
| asteroid | 18699 Quigley | 1.0 | 5.0 | 40573.0 |
| asteroid | 19 Fortuna | 2.0 | 8.0 | 40573.0 |
| asteroid | 19425 Nicholasrapp | 2.0 | 6.0 | 40573.0 |
| asteroid | 20536 Tracicarter | 2.0 | 6.0 | 40573.0 |
| asteroid | 210432 Dietmarhopp | 1.0 | 5.0 | 40573.0 |
| asteroid | 21856 Heathermaria | 2.0 | 6.0 | 40573.0 |
| asteroid | 23011 Petach | 2.0 | 6.0 | 40573.0 |
| asteroid | 23133 Rishinbehl | 2.0 | 6.0 | 40573.0 |
| asteroid | 23213 Ameliachang | 2.0 | 6.0 | 40573.0 |
| asteroid | 23769 Russellbabb | 2.0 | 6.0 | 40573.0 |
| asteroid | 23773 Sarugaku | 2.0 | 6.0 | 40573.0 |
| asteroid | 24249 Bobbiolson | 2.0 | 5.0 | 40573.0 |
| asteroid | 24318 Vivianlee | 2.0 | 6.0 | 40573.0 |
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| asteroid | 2545 Verbiest | 2.0 | 6.0 | 40573.0 |
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| asteroid | 26283 Oswalt | 2.0 | 6.0 | 40573.0 |
| asteroid | 2640 Hällström | 2.0 | 7.0 | 40573.0 |
| asteroid | 27277 Pattybrown | 2.0 | 6.0 | 40573.0 |
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| asteroid | 28644 Michaelzhang | 2.0 | 6.0 | 40573.0 |
| asteroid | 28800 Speth | 2.0 | 6.0 | 40573.0 |
| asteroid | 28823 Archibald | 2.0 | 6.0 | 40573.0 |
| asteroid | 29132 Bradpitt | 2.0 | 6.0 | 40573.0 |
| asteroid | 29438 Zhengjia | 2.0 | 6.0 | 40573.0 |
| asteroid | 29880 Andytran | 2.0 | 6.0 | 40573.0 |
| asteroid | 30211 Sheilah | 2.0 | 6.0 | 40573.0 |
| asteroid | 30241 Donnamower | 2.0 | 6.0 | 40573.0 |
| asteroid | 30441 Curly | 2.0 | 7.0 | 40573.0 |
| asteroid | 31491 Demessie | 2.0 | 6.0 | 40573.0 |
| asteroid | 31823 Viète | 2.0 | 7.0 | 40573.0 |
| asteroid | 32428 Peterlangley | 2.0 | 6.0 | 40573.0 |
| asteroid | 32807 Quarenghi | 2.0 | 7.0 | 40573.0 |
| asteroid | 3338 Richter | 2.0 | 7.0 | 40573.0 |
| asteroid | 3370 Kohsai | 2.0 | 7.0 | 40573.0 |
| asteroid | 34220 Pelagiamajoni | 2.0 | 6.0 | 40573.0 |
| asteroid | 34258 Pentland | 2.0 | 6.0 | 40573.0 |
| asteroid | 34273 Franklynwang | 2.0 | 6.0 | 40573.0 |
| asteroid | 34696 Risoldi | 2.0 | 7.0 | 40573.0 |
| asteroid | 34846 Vincent | 2.0 | 6.0 | 40573.0 |
| asteroid | 35403 Latimer | 2.0 | 6.0 | 40573.0 |
| asteroid | 3589 Loyola | 2.0 | 6.0 | 40573.0 |
| asteroid | 3792 Preston | 2.0 | 7.0 | 40573.0 |
| asteroid | 38 Leda | 2.0 | 7.0 | 40573.0 |
| asteroid | 3846 Hazel | 2.0 | 6.0 | 40573.0 |
| asteroid | 3862 Agekian | 2.0 | 6.0 | 40573.0 |
| album | 3rd: Love Escalation! | 2.0 | 5.0 | 49063.0 |
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| asteroid | 431 Nephele | 2.0 | 7.0 | 40573.0 |
| asteroid | 4419 Allancook | 2.0 | 6.0 | 40573.0 |
| asteroid | 4494 Marimo | 2.0 | 6.0 | 40573.0 |
| asteroid | 4578 Kurashiki | 2.0 | 7.0 | 40573.0 |
| asteroid | 4686 Maisica | 2.0 | 6.0 | 40573.0 |
| asteroid | 475 Ocllo | 2.0 | 6.0 | 40573.0 |
| asteroid | 5009 Sethos | 2.0 | 9.0 | 40573.0 |
| asteroid | 5497 Sararussell | 2.0 | 6.0 | 40573.0 |
| asteroid | 5671 Chanal | 2.0 | 5.0 | 40573.0 |
| asteroid | 5736 Sanford | 2.0 | 6.0 | 40573.0 |
| asteroid | 6056 Donatello | 2.0 | 9.0 | 40573.0 |
| asteroid | 6164 Gerhardmüller | 2.0 | 6.0 | 40573.0 |
| asteroid | 618 Elfriede | 2.0 | 6.0 | 40573.0 |
| asteroid | 6207 Bourvil | 2.0 | 7.0 | 40573.0 |
| asteroid | 7620 Willaert | 2.0 | 9.0 | 40573.0 |
| asteroid | 7796 Járacimrman | 2.0 | 7.0 | 40573.0 |
| asteroid | 78125 Salimbeni | 1.0 | 4.0 | 40573.0 |
| asteroid | 7901 Konnai | 2.0 | 6.0 | 40573.0 |
| asteroid | 7960 Condorcet | 2.0 | 7.0 | 40573.0 |
| asteroid | 8284 Cranach | 2.0 | 7.0 | 40573.0 |
| asteroid | 8579 Hieizan | 2.0 | 7.0 | 40573.0 |
| asteroid | 8599 Riparia | 2.0 | 9.0 | 40573.0 |
| asteroid | 8930 Kubota | 2.0 | 6.0 | 40573.0 |
| asteroid | 8999 Tashadunn | 2.0 | 6.0 | 40573.0 |
| asteroid | 9147 Kourakuen | 2.0 | 7.0 | 40573.0 |
| asteroid | 9225 Daiki | 2.0 | 6.0 | 40573.0 |
| asteroid | 924 Toni | 2.0 | 6.0 | 40573.0 |
| asteroid | 9346 Fernandel | 2.0 | 7.0 | 40573.0 |
| asteroid | 9945 Karinaxavier | 2.0 | 6.0 | 40573.0 |
| single | A Looking in View | 1.0 | 4.0 | 38372.0 |
| album | A New Day Yesterday | 1.0 | 5.0 | 49063.0 |
| album | A Night on the Town | 4.0 | 10.0 | 49063.0 |
| album | A Night on the Town | 4.0 | 10.0 | 49063.0 |
| album | A Nod Is As Good As a Wink... to a Blind Horse | 2.0 | 5.0 | 49063.0 |
| album | A Winter Romance | 2.0 | 3.0 | 49063.0 |
| human | A.O. Segerberg | 9.0 | 5.0 | 251915.0 |
| human | Abby Elliott | 2.0 | 6.0 | 251915.0 |
| human | Abo-shinnō | 4.0 | 6.0 | 251915.0 |
| human | Abram Room | 3.0 | 12.0 | 251915.0 |
| human | Abram van Rijckevorsel | 1.0 | 6.0 | 251915.0 |
| film | Ace Attorney | 9.0 | 19.0 | 13894.0 |
| human | Adam Williams | 13.0 | 23.0 | 251915.0 |
| human | Adam Williams | 13.0 | 23.0 | 251915.0 |
| human | Adam Williams | 13.0 | 23.0 | 251915.0 |
| human | Adele Astaire | 5.0 | 15.0 | 251915.0 |
| human | Adolf Svoboda | 1.0 | 10.0 | 251915.0 |
| human | Adolf von Blome | 1.0 | 8.0 | 251915.0 |
| human | Adrian Carmack | 1.0 | 6.0 | 251915.0 |
| human | Adriean Videanu | 1.0 | 7.0 | 251915.0 |
| human | Adèle Reinhardt | 4.0 | 4.0 | 251915.0 |
| human | Adélard Godbout | 1.0 | 11.0 | 251915.0 |
| album | Afraid of Sunlight | 1.0 | 6.0 | 49063.0 |
| album | African Cookbook | 1.0 | 4.0 | 49063.0 |
| human | Afshan Azad | 2.0 | 8.0 | 251915.0 |
| human | Agda Helin | 2.0 | 4.0 | 251915.0 |
| human | Agnes of Baden | 1.0 | 6.0 | 251915.0 |
| human | Agnes of Kuenring | 2.0 | 5.0 | 251915.0 |
| human | Agnieszka Sitek | 1.0 | 5.0 | 251915.0 |
| album | Ain't Nothin' Like Me | 2.0 | 5.0 | 49063.0 |
| album | Ainult unustamiseks | 1.0 | 3.0 | 49063.0 |
| human | Akhil Reed Amar | 1.0 | 7.0 | 251915.0 |
| human | Akimi Yoshida | 4.0 | 9.0 | 251915.0 |
| human | Akinobu Uraka | 1.0 | 7.0 | 251915.0 |
| human | Akinori Iwamura | 1.0 | 7.0 | 251915.0 |
| human | Al Santos | 3.0 | 17.0 | 251915.0 |
| human | Al Santos | 3.0 | 17.0 | 251915.0 |
| human | Al-Khayzuran | 3.0 | 5.0 | 251915.0 |
| human | Alan Garner | 5.0 | 25.0 | 251915.0 |
| human | Alan Garner | 5.0 | 25.0 | 251915.0 |
| human | Alan Garner | 5.0 | 25.0 | 251915.0 |
| human | Alan Mills | 1.0 | 19.0 | 251915.0 |
| human | Alan Mills | 1.0 | 19.0 | 251915.0 |
| human | Alan Mills | 1.0 | 19.0 | 251915.0 |
| human | Alan Morinis | 1.0 | 6.0 | 251915.0 |
| human | Alaungpaya | 6.0 | 13.0 | 251915.0 |
| human | Albert Cossery | 2.0 | 10.0 | 251915.0 |
| human | Albert Duquesne | 2.0 | 3.0 | 251915.0 |
| human | Albert Fennell | 7.0 | 3.0 | 251915.0 |
| human | Albert Lindhagen | 6.0 | 16.0 | 251915.0 |
| human | Albert Rueprecht | 16.0 | 7.0 | 251915.0 |
| human | Alejandro Goic | 3.0 | 12.0 | 251915.0 |
| human | Alejandro Goic | 3.0 | 12.0 | 251915.0 |
| human | Alejandro Matas Britos | 1.0 | 5.0 | 251915.0 |
| human | Alejandro Portero Igual | 1.0 | 6.0 | 251915.0 |
| human | Aleksandr Boyarsky | 1.0 | 8.0 | 251915.0 |
| human | Alena Procházková | 1.0 | 9.0 | 251915.0 |
| human | Alexander Fehling | 6.0 | 6.0 | 251915.0 |
| human | Alexander Moissi | 2.0 | 11.0 | 251915.0 |
| human | Alexandra Powers | 6.0 | 6.0 | 251915.0 |
| human | Alexandre Bertrand | 4.0 | 14.0 | 251915.0 |
| human | Alexandre-François Desportes | 1.0 | 8.0 | 251915.0 |
| human | Alfonso Cassini | 33.0 | 7.0 | 251915.0 |
| human | Alfonso II d'Este | 2.0 | 8.0 | 251915.0 |
| human | Alfonso XI of Castile | 12.0 | 20.0 | 251915.0 |
| human | Alfred Horatio Belo | 1.0 | 6.0 | 251915.0 |
| human | Alfred Meyer | 1.0 | 29.0 | 251915.0 |
| human | Alfred Meyer | 1.0 | 29.0 | 251915.0 |
| human | Alfred Meyer | 1.0 | 29.0 | 251915.0 |
| human | Alfred Zeisler | 14.0 | 9.0 | 251915.0 |
| human | Alfred, Hereditary Prince of Saxe-Coburg and Gotha | 4.0 | 11.0 | 251915.0 |
| human | Alice Pike Barney | 3.0 | 9.0 | 251915.0 |
| single | All of Me (Boy Oh Boy) | 1.0 | 4.0 | 38372.0 |
| commune of France | Allaire | 9.0 | 10.0 | 32436.0 |
| commune of France | Alligny-en-Morvan | 2.0 | 4.0 | 32436.0 |
| human | Allison Anders | 9.0 | 9.0 | 251915.0 |
| human | Amable | 3.0 | 8.0 | 251915.0 |
| human | Amanda Walsh | 8.0 | 6.0 | 251915.0 |
| single | Amanojaku | 1.0 | 5.0 | 38372.0 |
| single | Amaryllis | 5.0 | 20.0 | 38372.0 |
| taxon | Amaryllis | 5.0 | 20.0 | 14226.0 |
| taxon | Amaryllis | 5.0 | 20.0 | 14226.0 |
| album | Amaryllis | 5.0 | 20.0 | 49063.0 |
| album | Amaryllis | 5.0 | 20.0 | 49063.0 |
| single | Amazing | 14.0 | 45.0 | 38372.0 |
| single | Amazing | 14.0 | 45.0 | 38372.0 |
| single | Amazing | 14.0 | 45.0 | 38372.0 |
| single | Amazing | 14.0 | 45.0 | 38372.0 |
| single | Amazing | 14.0 | 45.0 | 38372.0 |
| single | Amazing | 14.0 | 45.0 | 38372.0 |
| single | Amazing | 14.0 | 45.0 | 38372.0 |
| album | Amazing | 14.0 | 45.0 | 49063.0 |
| album | Amazing | 14.0 | 45.0 | 49063.0 |
| human | Ameinias of Athens | 4.0 | 7.0 | 251915.0 |
| album | Amor y rock and roll | 2.0 | 5.0 | 49063.0 |
| single | Amulet | 3.0 | 9.0 | 38372.0 |
| human | Anastasia of Serbia | 4.0 | 9.0 | 251915.0 |
| human | Andrea Ahmann | 1.0 | 7.0 | 251915.0 |
| human | Andrea Costantini | 4.0 | 7.0 | 251915.0 |
| human | Andrew Dasburg | 1.0 | 10.0 | 251915.0 |
| human | Andrew Divoff | 24.0 | 7.0 | 251915.0 |
| human | Andriy Bandera | 4.0 | 9.0 | 251915.0 |
| human | Andronikos II of Trebizond | 4.0 | 7.0 | 251915.0 |
| human | András Fricsay | 3.0 | 6.0 | 251915.0 |
| human | André Forcier | 8.0 | 8.0 | 251915.0 |
| human | André Hazes | 4.0 | 11.0 | 251915.0 |
| human | André-Paul Antoine | 8.0 | 11.0 | 251915.0 |
| human | Andrés Cuevas González | 1.0 | 4.0 | 251915.0 |
| single | Angels & Stars | 3.0 | 8.0 | 38372.0 |
| single | Angels' Story | 1.0 | 4.0 | 38372.0 |
| single | Anima Rossa | 2.0 | 6.0 | 38372.0 |
| album | Animetal Marathon V | 2.0 | 5.0 | 49063.0 |
| taxon | Anisogammaridae | 8.0 | 3.0 | 14226.0 |
| human | Anita Gillette | 8.0 | 8.0 | 251915.0 |
| human | Anita Laurenzi | 2.0 | 5.0 | 251915.0 |
| human | Ann Rinaldi | 7.0 | 5.0 | 251915.0 |
| human | Anne Goursaud | 3.0 | 7.0 | 251915.0 |
| human | Anne of Lorraine, duchess of Aumale | 5.0 | 9.0 | 251915.0 |
| human | Annie Degroote | 2.0 | 6.0 | 251915.0 |
| human | Annie Dufresne | 3.0 | 6.0 | 251915.0 |
| human | Annie Rosar | 31.0 | 8.0 | 251915.0 |
| album | Anodyne | 1.0 | 9.0 | 49063.0 |
| human | Ans Kremer | 6.0 | 5.0 | 251915.0 |
| human | Anthonie Verstraelen | 1.0 | 8.0 | 251915.0 |
| single | Anthonio | 2.0 | 4.0 | 38372.0 |
| human | Anthony Andrews | 14.0 | 9.0 | 251915.0 |
| human | Anthony I, Count of Ligny | 4.0 | 8.0 | 251915.0 |
| human | Antipater of Tarsus | 3.0 | 6.0 | 251915.0 |
| human | Antoine Balpêtré | 40.0 | 9.0 | 251915.0 |
| human | Antonin Lovrier | 1.0 | 8.0 | 251915.0 |
| human | Antonio Rey González | 1.0 | 6.0 | 251915.0 |
| commune of France | Antrenas | 3.0 | 5.0 | 32436.0 |
| human | António Lobo Antunes | 1.0 | 11.0 | 251915.0 |
| human | Anushka Sharma | 8.0 | 9.0 | 251915.0 |
| single | Anything but Mine | 2.0 | 6.0 | 38372.0 |
| single | Anywhere Is | 2.0 | 6.0 | 38372.0 |
| film | Apart | 2.0 | 13.0 | 13894.0 |
| album | Apart | 2.0 | 13.0 | 49063.0 |
| taxon | Apinae | 18.0 | 3.0 | 14226.0 |
| human | Apphia Yu | 1.0 | 4.0 | 251915.0 |
| human | April Grace | 12.0 | 6.0 | 251915.0 |
| taxon | Arales | 2.0 | 3.0 | 14226.0 |
| commune of France | Arbourse | 1.0 | 4.0 | 32436.0 |
| commune of France | Archettes | 2.0 | 4.0 | 32436.0 |
| human | Archibald Primrose, 5th Earl of Rosebery | 2.0 | 13.0 | 251915.0 |
| human | Are Hilstad | 1.0 | 5.0 | 251915.0 |
| commune of France | Argenton | 2.0 | 3.0 | 32436.0 |
| human | Aristobulus of Chalcis | 2.0 | 4.0 | 251915.0 |
| street | Arlersteeg | 1.0 | 4.0 | 16483.0 |
| human | Arne Mattsson | 10.0 | 8.0 | 251915.0 |
| human | Arnold Pinnock | 5.0 | 6.0 | 251915.0 |
| human | Artaxerxes I of Persia | 4.0 | 7.0 | 251915.0 |
| human | Artur Olech | 1.0 | 11.0 | 251915.0 |
| human | Arturo de Córdova | 19.0 | 8.0 | 251915.0 |
| commune of France | Arzacq-Arraziguet | 3.0 | 4.0 | 32436.0 |
| album | Asian Dreamer | 2.0 | 5.0 | 49063.0 |
| human | Asiya bint Muzahim | 1.0 | 4.0 | 251915.0 |
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| human | Atiqah Hasiholan | 3.0 | 5.0 | 251915.0 |
| commune of France | Auchy-la-Montagne | 1.0 | 4.0 | 32436.0 |
| human | Audouin Dollfus | 1.0 | 7.0 | 251915.0 |
| commune of France | Audun-le-Roman | 8.0 | 13.0 | 32436.0 |
| human | Augustus the Younger, Duke of Brunswick-Lüneburg | 8.0 | 20.0 | 251915.0 |
| human | Austin M. Purves, Jr. | 2.0 | 6.0 | 251915.0 |
| commune of France | Aventignan | 2.0 | 5.0 | 32436.0 |
| commune of France | Avondance | 2.0 | 4.0 | 32436.0 |
| commune of France | Awala-Yalimapo | 2.0 | 4.0 | 32436.0 |
| taxon | Azalea | 2.0 | 3.0 | 14226.0 |
| commune of France | Azé | 2.0 | 4.0 | 32436.0 |
| single | BGM | 3.0 | 10.0 | 38372.0 |
| album | BGM | 3.0 | 10.0 | 49063.0 |
| human | Baba Saad | 2.0 | 6.0 | 251915.0 |
| single | Baby by Me | 2.0 | 9.0 | 38372.0 |
| album | Back for More | 3.0 | 9.0 | 49063.0 |
| album | Back for More | 3.0 | 9.0 | 49063.0 |
| human | Banksy | 3.0 | 13.0 | 251915.0 |
| single | Banquet | 4.0 | 10.0 | 38372.0 |
| album | Banquet | 4.0 | 10.0 | 49063.0 |
| commune of France | Bar-le-Duc | 77.0 | 9.0 | 32436.0 |
| human | Barbara Adolph | 8.0 | 6.0 | 251915.0 |
| human | Barbara London | 1.0 | 14.0 | 251915.0 |
| human | Barbara London | 1.0 | 14.0 | 251915.0 |
| commune of France | Barges | 2.0 | 8.0 | 32436.0 |
| commune of France | Barges | 2.0 | 8.0 | 32436.0 |
| commune of France | Barjac | 19.0 | 24.0 | 32436.0 |
| commune of France | Barjac | 19.0 | 24.0 | 32436.0 |
| commune of France | Barjac | 19.0 | 24.0 | 32436.0 |
| taxon | Basiliscus | 1.0 | 7.0 | 14226.0 |
| human | Basiliscus | 1.0 | 7.0 | 251915.0 |
| album | Be Ready Boys: Appalachia to Abilene | 2.0 | 5.0 | 49063.0 |
| human | Beata Schimscheiner | 1.0 | 5.0 | 251915.0 |
| human | Beatriz Michelena | 2.0 | 9.0 | 251915.0 |
| commune of France | Beaumont-de-Lomagne | 7.0 | 5.0 | 32436.0 |
| single | Beer for My Horses | 2.0 | 20.0 | 38372.0 |
| film | Beer for My Horses | 2.0 | 20.0 | 13894.0 |
| film | Before I Go to Sleep | 1.0 | 23.0 | 13894.0 |
| commune of France | Belmontet | 1.0 | 4.0 | 32436.0 |
| human | Benedikt Gollhardt | 1.0 | 5.0 | 251915.0 |
| human | Benito Sagredo | 1.0 | 4.0 | 251915.0 |
| human | Beppe Cardile | 1.0 | 7.0 | 251915.0 |
| human | Bernadette Paaßen | 2.0 | 5.0 | 251915.0 |
| human | Bernard of Świdnica | 11.0 | 13.0 | 251915.0 |
| human | Bernd Förster | 1.0 | 15.0 | 251915.0 |
| commune of France | Berville | 4.0 | 5.0 | 32436.0 |
| commune of France | Beuvillers | 8.0 | 14.0 | 32436.0 |
| commune of France | Beuvillers | 8.0 | 14.0 | 32436.0 |
| human | Beverley Callard | 1.0 | 5.0 | 251915.0 |
| human | Bhim Singh Rana | 1.0 | 5.0 | 251915.0 |
| human | Big Pokey | 1.0 | 4.0 | 251915.0 |
| film | Bill Bergson Lives Dangerously | 2.0 | 37.0 | 13894.0 |
| film | Bill Bergson Lives Dangerously | 2.0 | 37.0 | 13894.0 |
| human | Bill Chott | 3.0 | 4.0 | 251915.0 |
| human | Bill Mason | 7.0 | 12.0 | 251915.0 |
| human | Bill Mason | 7.0 | 12.0 | 251915.0 |
| human | Bill Williams | 22.0 | 46.0 | 251915.0 |
| human | Bill Williams | 22.0 | 46.0 | 251915.0 |
| human | Bill Williams | 22.0 | 46.0 | 251915.0 |
| human | Bill Williams | 22.0 | 46.0 | 251915.0 |
| human | Bill Williams | 22.0 | 46.0 | 251915.0 |
| human | Bill Williams | 22.0 | 46.0 | 251915.0 |
| album | Billy Breathes | 1.0 | 4.0 | 49063.0 |
| human | Billy Wirth | 7.0 | 6.0 | 251915.0 |
| township in China | Bingcun | 1.0 | 3.0 | 19553.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| street | Binnenweg | 23.0 | 35.0 | 16483.0 |
| commune of France | Bize | 4.0 | 9.0 | 32436.0 |
| commune of France | Bize | 4.0 | 9.0 | 32436.0 |
| human | Bjørg Tingstad | 1.0 | 4.0 | 251915.0 |
| album | Black Moses | 1.0 | 4.0 | 49063.0 |
| taxon | Blattodea | 8.0 | 4.0 | 14226.0 |
| album | Blazon Stone | 2.0 | 5.0 | 49063.0 |
| commune of France | Blincourt | 1.0 | 4.0 | 32436.0 |
| single | Blue Suede Shoes | 1.0 | 5.0 | 38372.0 |
| human | Bob Stephenson | 7.0 | 32.0 | 251915.0 |
| human | Bob Stephenson | 7.0 | 32.0 | 251915.0 |
| human | Bob Stephenson | 7.0 | 32.0 | 251915.0 |
| human | Bob Stephenson | 7.0 | 32.0 | 251915.0 |
| human | Bobby Andrews | 4.0 | 6.0 | 251915.0 |
| human | Bobby Roth | 20.0 | 7.0 | 251915.0 |
| human | Bodil Steensen-Leth | 1.0 | 5.0 | 251915.0 |
| commune of France | Bogy | 6.0 | 12.0 | 32436.0 |
| human | Bogy | 6.0 | 12.0 | 251915.0 |
| album | Book of Angels | 1.0 | 3.0 | 49063.0 |
| human | Boris Isaković | 16.0 | 5.0 | 251915.0 |
| taxon | Borsoniidae | 9.0 | 3.0 | 14226.0 |
| human | Boualem Sansal | 1.0 | 8.0 | 251915.0 |
| commune of France | Boulin | 2.0 | 5.0 | 32436.0 |
| human | Boyd Morgan | 4.0 | 10.0 | 251915.0 |
| human | Bradford Dillman | 38.0 | 8.0 | 251915.0 |
| album | Break a Dawn | 2.0 | 4.0 | 49063.0 |
| human | Brendan James | 5.0 | 12.0 | 251915.0 |
| album | Brendan James | 5.0 | 12.0 | 49063.0 |
| human | Brian Freeman | 1.0 | 13.0 | 251915.0 |
| human | Brian Freeman | 1.0 | 13.0 | 251915.0 |
| human | Brian Harold Mason | 2.0 | 13.0 | 251915.0 |
| human | Brian Michael Bendis | 1.0 | 8.0 | 251915.0 |
| human | Brian Tyler | 8.0 | 16.0 | 251915.0 |
| human | Brian Tyler | 8.0 | 16.0 | 251915.0 |
| human | Bruce Degen | 3.0 | 4.0 | 251915.0 |
| human | Bruno Hübner | 16.0 | 15.0 | 251915.0 |
| human | Bruno Hübner | 16.0 | 15.0 | 251915.0 |
| human | Bruno Wolkowitch | 13.0 | 8.0 | 251915.0 |
| human | Bryan Gregory | 1.0 | 6.0 | 251915.0 |
| album | Bud Powell's Moods | 1.0 | 4.0 | 49063.0 |
| single | Bumble Bees | 1.0 | 4.0 | 38372.0 |
| street | Burgemeester van Rijnsingel | 3.0 | 4.0 | 16483.0 |
| single | Burning Bridges | 7.0 | 25.0 | 38372.0 |
| album | Burning Bridges | 7.0 | 25.0 | 49063.0 |
| album | Burning Bridges | 7.0 | 25.0 | 49063.0 |
| album | Burning Bridges | 7.0 | 25.0 | 49063.0 |
| commune of France | By | 2.0 | 5.0 | 32436.0 |
| film | Byl jednou jeden král… | 1.0 | 7.0 | 13894.0 |
| human | Bárbara Lennie | 2.0 | 6.0 | 251915.0 |
| commune of France | Bélarga | 3.0 | 5.0 | 32436.0 |
| human | Cabral Ibacka | 1.0 | 6.0 | 251915.0 |
| taxon | Cajamarca | 28.0 | 30.0 | 14226.0 |
| street | Camminghastraat | 6.0 | 4.0 | 16483.0 |
| single | Can Can/Promise You | 2.0 | 4.0 | 38372.0 |
| single | Can It Be All So Simple | 2.0 | 5.0 | 38372.0 |
| commune of France | Cappel | 6.0 | 12.0 | 32436.0 |
| commune of France | Capvern | 3.0 | 5.0 | 32436.0 |
| taxon | Caracal | 7.0 | 13.0 | 14226.0 |
| human | Carita Holmström | 1.0 | 9.0 | 251915.0 |
| human | Carl Craig | 2.0 | 9.0 | 251915.0 |
| human | Carl Spitzweg | 7.0 | 10.0 | 251915.0 |
| human | Carl-Herbert Dieden | 2.0 | 5.0 | 251915.0 |
| human | Carla Bartheel | 1.0 | 8.0 | 251915.0 |
| human | Carmen Franco, 1st Duchess of Franco | 6.0 | 12.0 | 251915.0 |
| human | Caroline Munro | 14.0 | 8.0 | 251915.0 |
| human | Carsten Sieling | 1.0 | 11.0 | 251915.0 |
| commune of France | Casalabriva | 2.0 | 3.0 | 32436.0 |
| commune of France | Castelnau-de-Montmiral | 3.0 | 4.0 | 32436.0 |
| Wikimedia category | Category:2010s in the United Kingdom | 10.0 | 3.0 | 14602.0 |
| Wikimedia category | Category:April 29, 2010 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:August 26, 2008 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:British Islands | 1.0 | 3.0 | 14602.0 |
| Wikimedia category | Category:Brown algae | 1.0 | 2.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Bentivoglio | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Borgo Tossignano | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Cantù | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Carcare | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Castel Ritaldi | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Chiari, Lombardy | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Clusone | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Coeur d'Alene | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Dießen am Ammersee | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Don Benito | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Douai | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Framura | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Gabrovo | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Garlasco | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Governorate of Livonia | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Kirkkonummi | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Ksar el-Kebir | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Kyzylorda Province | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Königs Wusterhausen | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Lake Havasu City | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Lorenzago di Cadore | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Lyons-la-Forêt | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Manchester | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Mukacheve Raion | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Nanping | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Oristano | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Rolampont | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Sondika | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Struga | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Toano | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Deaths in Vitoria-Gasteiz | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:February 16, 2008 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:February 9, 2015 | 2.0 | 5.0 | 14602.0 |
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| Wikimedia category | Category:Films shot in Potenza | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Films shot in Rio Grande do Sul | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Films shot in San Diego | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:Films shot in South Dakota | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:Films shot in Trentino-South Tyrol | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:Jordanian people | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:July 30, 2008 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:June 29, 2010 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:March 16, 2011 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:March 28, 2006 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:May 10, 2005 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:October 18, 2005 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:People from Michalovce | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:People from Sigulda | 1.0 | 4.0 | 14602.0 |
| Wikimedia category | Category:September 20, 2010 | 2.0 | 5.0 | 14602.0 |
| Wikimedia category | Category:Two and a Half Men characters | 1.0 | 4.0 | 14602.0 |
| human | Catherine Sutherland | 2.0 | 6.0 | 251915.0 |
| album | Caught You | 2.0 | 5.0 | 49063.0 |
| human | Cayo Lara | 1.0 | 9.0 | 251915.0 |
| commune of France | Cazevieille | 2.0 | 5.0 | 32436.0 |
| commune of France | Ceillac | 9.0 | 11.0 | 32436.0 |
| human | Celeste Cid | 1.0 | 6.0 | 251915.0 |
| commune of France | Ceyssac | 1.0 | 4.0 | 32436.0 |
| commune of France | Ceyzérieu | 12.0 | 13.0 | 32436.0 |
| commune of France | Chambolle-Musigny | 3.0 | 6.0 | 32436.0 |
| commune of France | Chameyrat | 8.0 | 10.0 | 32436.0 |
| human | Chandni | 3.0 | 18.0 | 251915.0 |
| film | Chandni | 3.0 | 18.0 | 13894.0 |
| human | Chandra Wilson | 5.0 | 8.0 | 251915.0 |
| township in China | Changdong Town | 1.0 | 3.0 | 19553.0 |
| commune of France | Chantraines | 2.0 | 5.0 | 32436.0 |
| commune of France | Chapelle-Royale | 1.0 | 4.0 | 32436.0 |
| human | Charles Berkeley, 2nd Earl of Berkeley | 7.0 | 17.0 | 251915.0 |
| human | Charles Planat | 1.0 | 11.0 | 251915.0 |
| human | Charles Wellford Leavitt | 2.0 | 7.0 | 251915.0 |
| human | Charles William, Duke of Saxe-Meiningen | 4.0 | 15.0 | 251915.0 |
| human | Charles, Prince of Rochefort | 3.0 | 5.0 | 251915.0 |
| human | Charlotte Chaffanjon | 1.0 | 10.0 | 251915.0 |
| human | Charlotte Desmares | 1.0 | 11.0 | 251915.0 |
| commune of France | Charnay | 6.0 | 8.0 | 32436.0 |
| commune of France | Charnay | 6.0 | 8.0 | 32436.0 |
| commune of France | Chauvigny | 8.0 | 8.0 | 32436.0 |
| commune of France | Chauvincourt-Provemont | 2.0 | 4.0 | 32436.0 |
| album | Chelsea Girl | 2.0 | 7.0 | 49063.0 |
| human | Chen Yannian | 1.0 | 5.0 | 251915.0 |
| commune of France | Chesnois-Auboncourt | 7.0 | 11.0 | 32436.0 |
| album | Chicago VIII | 2.0 | 5.0 | 49063.0 |
| human | Chikuhei Nakajima | 1.0 | 5.0 | 251915.0 |
| album | Chimney's Afire | 1.0 | 4.0 | 49063.0 |
| human | Chintila | 1.0 | 6.0 | 251915.0 |
| commune of France | Chiry-Ourscamp | 3.0 | 4.0 | 32436.0 |
| taxon | Chitonina | 3.0 | 4.0 | 14226.0 |
| taxon | Chloranthaceae | 4.0 | 6.0 | 14226.0 |
| human | Chlothar I | 20.0 | 26.0 | 251915.0 |
| commune of France | Chouain | 6.0 | 9.0 | 32436.0 |
| human | Chris Ofili | 1.0 | 10.0 | 251915.0 |
| human | Chris Petersen | 2.0 | 17.0 | 251915.0 |
| human | Chris Petersen | 2.0 | 17.0 | 251915.0 |
| human | Chris Petersen | 2.0 | 17.0 | 251915.0 |
| human | Chris Thomas | 4.0 | 30.0 | 251915.0 |
| human | Chris Thomas | 4.0 | 30.0 | 251915.0 |
| human | Chris Thomas | 4.0 | 30.0 | 251915.0 |
| human | Chris Thomas | 4.0 | 30.0 | 251915.0 |
| human | Christiaen Jansz van Bieselingen | 1.0 | 7.0 | 251915.0 |
| human | Christian Décamps | 1.0 | 8.0 | 251915.0 |
| human | Christian Erickson | 2.0 | 6.0 | 251915.0 |
| human | Christian Lorenz | 1.0 | 8.0 | 251915.0 |
| human | Christian Pikes | 2.0 | 5.0 | 251915.0 |
| human | Christian Schramm | 1.0 | 7.0 | 251915.0 |
| human | Christian Stolte | 5.0 | 6.0 | 251915.0 |
| human | Christine Carère | 16.0 | 7.0 | 251915.0 |
| human | Christine Haas | 2.0 | 5.0 | 251915.0 |
| human | Christoph Ahlhaus | 2.0 | 10.0 | 251915.0 |
| human | Christoph Schönborn | 1.0 | 18.0 | 251915.0 |
| human | Christoph Zrenner | 1.0 | 5.0 | 251915.0 |
| human | Christopher Cornford | 4.0 | 6.0 | 251915.0 |
| human | Christopher Hewett | 4.0 | 8.0 | 251915.0 |
| human | Christopher Monger | 3.0 | 7.0 | 251915.0 |
| commune of France | Châteaubourg | 10.0 | 12.0 | 32436.0 |
| commune of France | Châteaubourg | 10.0 | 12.0 | 32436.0 |
| commune of France | Châteauneuf-Miravail | 8.0 | 11.0 | 32436.0 |
| commune of France | Châteauneuf-Val-de-Bargis | 3.0 | 5.0 | 32436.0 |
| human | Cinzia De Carolis | 10.0 | 7.0 | 251915.0 |
| album | Cirrus | 2.0 | 5.0 | 49063.0 |
| commune of France | Clansayes | 1.0 | 3.0 | 32436.0 |
| single | Clap Yo Hands | 2.0 | 5.0 | 38372.0 |
| human | Claude Lamoral, 3rd Prince of Ligne | 3.0 | 11.0 | 251915.0 |
| human | Claude Santelli | 1.0 | 7.0 | 251915.0 |
| human | Claude-Jean Philippe | 3.0 | 9.0 | 251915.0 |
| human | Claudio Pizarro | 1.0 | 12.0 | 251915.0 |
| human | Claus Friedrich von Reden | 1.0 | 7.0 | 251915.0 |
| commune of France | Claville | 2.0 | 4.0 | 32436.0 |
| human | Clement Hurd | 4.0 | 6.0 | 251915.0 |
| single | Clockwork | 1.0 | 12.0 | 38372.0 |
| single | Clown Prince | 1.0 | 4.0 | 38372.0 |
| human | Clémence Bretécher | 2.0 | 6.0 | 251915.0 |
| taxon | Coccinellidae | 36.0 | 3.0 | 14226.0 |
| taxon | Coccotremataceae | 1.0 | 3.0 | 14226.0 |
| taxon | Colubridae | 22.0 | 4.0 | 14226.0 |
| commune of France | Condette | 2.0 | 4.0 | 32436.0 |
| human | Conrad II, Count of Oldenburg | 3.0 | 8.0 | 251915.0 |
| human | Consort Qi | 5.0 | 9.0 | 251915.0 |
| album | Conspiritus | 1.0 | 3.0 | 49063.0 |
| human | Constantin Melnik | 1.0 | 10.0 | 251915.0 |
| commune of France | Corbère | 7.0 | 11.0 | 32436.0 |
| human | Cornelia Stuyvesant Vanderbilt | 4.0 | 8.0 | 251915.0 |
| human | Corrado Guarducci | 21.0 | 6.0 | 251915.0 |
| commune of France | Corre | 1.0 | 5.0 | 32436.0 |
| human | Cory Monteith | 4.0 | 11.0 | 251915.0 |
| human | Countess Claudine Rhédey von Kis-Rhéde | 2.0 | 8.0 | 251915.0 |
| human | Countess Ermesinde II, Countess of Luxembourg | 6.0 | 9.0 | 251915.0 |
| human | Craig Pearce | 4.0 | 6.0 | 251915.0 |
| single | Crimson and Clover | 2.0 | 5.0 | 38372.0 |
| commune of France | Criquetot-sur-Ouville | 2.0 | 3.0 | 32436.0 |
| single | Crisis | 7.0 | 59.0 | 38372.0 |
| film | Crisis | 7.0 | 59.0 | 13894.0 |
| film | Crisis | 7.0 | 59.0 | 13894.0 |
| album | Crisis | 7.0 | 59.0 | 49063.0 |
| single | Cross Over | 2.0 | 12.0 | 38372.0 |
| album | Crusade | 6.0 | 34.0 | 49063.0 |
| album | Cybernetic Dreams of Pi | 1.0 | 3.0 | 49063.0 |
| taxon | Cyrillaceae | 1.0 | 5.0 | 14226.0 |
| human | César Herráiz Pujol | 1.0 | 5.0 | 251915.0 |
| commune of France | Cézan | 1.0 | 4.0 | 32436.0 |
| taxon | Dactylopodida | 2.0 | 3.0 | 14226.0 |
| human | Daisy Campbell | 2.0 | 4.0 | 251915.0 |
| human | Dan Le Sac | 1.0 | 4.0 | 251915.0 |
| human | Daniel Conley | 2.0 | 8.0 | 251915.0 |
| human | Daniel Day-Lewis | 33.0 | 18.0 | 251915.0 |
| human | Daniel Isăilă | 1.0 | 6.0 | 251915.0 |
| human | Daniel Lupi | 6.0 | 3.0 | 251915.0 |
| single | Dans un autre monde | 2.0 | 7.0 | 38372.0 |
| human | Dantivarman | 2.0 | 5.0 | 251915.0 |
| human | Daphné Roulier | 2.0 | 5.0 | 251915.0 |
| human | Dario D'Ambrosio | 1.0 | 7.0 | 251915.0 |
| human | Darren Jeffries | 1.0 | 5.0 | 251915.0 |
| human | Date Muratomi | 2.0 | 5.0 | 251915.0 |
| human | Dava Sobel | 1.0 | 11.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | Dave Brown | 2.0 | 112.0 | 251915.0 |
| human | David Mills | 3.0 | 55.0 | 251915.0 |
| human | David Mills | 3.0 | 55.0 | 251915.0 |
| human | David Mills | 3.0 | 55.0 | 251915.0 |
| human | David Mills | 3.0 | 55.0 | 251915.0 |
| human | David Mills | 3.0 | 55.0 | 251915.0 |
| human | David Mills | 3.0 | 55.0 | 251915.0 |
| human | David Valcin | 1.0 | 4.0 | 251915.0 |
| human | Davyd Sviatoslavich | 5.0 | 7.0 | 251915.0 |
| album | De Gregori | 2.0 | 5.0 | 49063.0 |
| album | De Mi Puño y Letra | 1.0 | 4.0 | 49063.0 |
| human | Dean Edwards | 1.0 | 29.0 | 251915.0 |
| human | Dean Edwards | 1.0 | 29.0 | 251915.0 |
| human | Dean Edwards | 1.0 | 29.0 | 251915.0 |
| album | Dear Miss Lonelyhearts | 1.0 | 6.0 | 49063.0 |
| album | Decade of Decadence | 2.0 | 3.0 | 49063.0 |
| human | Delia Fiallo | 2.0 | 5.0 | 251915.0 |
| human | Denis Lazure | 1.0 | 12.0 | 251915.0 |
| human | Denise Clair | 5.0 | 6.0 | 251915.0 |
| human | Derrick O'Connor | 14.0 | 6.0 | 251915.0 |
| album | Destination Berlin | 2.0 | 4.0 | 49063.0 |
| human | Devrim Evin | 1.0 | 5.0 | 251915.0 |
| human | Diana Hardcastle | 1.0 | 4.0 | 251915.0 |
| human | Dianne Buckner | 1.0 | 5.0 | 251915.0 |
| human | Dilys Laye | 2.0 | 6.0 | 251915.0 |
| human | Dimitrios Vranopoulos | 1.0 | 9.0 | 251915.0 |
| human | Dimítris Kókkinos | 1.0 | 4.0 | 251915.0 |
| human | Dirk Oldenburg | 1.0 | 8.0 | 251915.0 |
| film | Disraeli | 1.0 | 34.0 | 13894.0 |
| human | Dmitry Vasilyevich | 1.0 | 4.0 | 251915.0 |
| single | Do It | 4.0 | 8.0 | 38372.0 |
| human | Doctor P | 1.0 | 5.0 | 251915.0 |
| film | Dogville | 1.0 | 36.0 | 13894.0 |
| human | Dominic Hawksley | 1.0 | 4.0 | 251915.0 |
| human | Dominique Lapierre | 4.0 | 11.0 | 251915.0 |
| commune of France | Doméliers | 1.0 | 4.0 | 32436.0 |
| single | Don Alfonso | 2.0 | 6.0 | 38372.0 |
| human | Don Haig | 1.0 | 7.0 | 251915.0 |
| human | Don Pardo | 1.0 | 10.0 | 251915.0 |
| single | Don't Forget to Dance | 1.0 | 3.0 | 38372.0 |
| human | Donald Calthrop | 21.0 | 8.0 | 251915.0 |
| human | Donald Dell | 1.0 | 10.0 | 251915.0 |
| township in China | Donggang | 25.0 | 29.0 | 19553.0 |
| human | Doraid Liddawi | 5.0 | 3.0 | 251915.0 |
| human | Dorothy Adams | 36.0 | 7.0 | 251915.0 |
| human | Dot Farley | 26.0 | 7.0 | 251915.0 |
| human | Doud Eisenhower | 2.0 | 6.0 | 251915.0 |
| commune of France | Doudeauville-en-Vexin | 2.0 | 4.0 | 32436.0 |
| album | Drive Like Jehu | 3.0 | 7.0 | 49063.0 |
| human | Drogo, Duke of Brittany | 4.0 | 7.0 | 251915.0 |
| street | Ds. Germsweg | 1.0 | 4.0 | 16483.0 |
| street | Dubbele Buurt | 10.0 | 6.0 | 16483.0 |
| street | Dubbele Buurt | 10.0 | 6.0 | 16483.0 |
| human | Duke Alexander of Württemberg | 20.0 | 41.0 | 251915.0 |
| human | Duke Alexander of Württemberg | 20.0 | 41.0 | 251915.0 |
| human | Duke Alexander of Württemberg | 20.0 | 41.0 | 251915.0 |
| human | Duke Xi of Lu | 6.0 | 8.0 | 251915.0 |
| album | Duo Live in Concert | 2.0 | 5.0 | 49063.0 |
| album | Duotones | 2.0 | 5.0 | 49063.0 |
| commune of France | Duras | 6.0 | 3.0 | 32436.0 |
| commune of France | Duravel | 3.0 | 4.0 | 32436.0 |
| human | Dustin Moskovitz | 2.0 | 8.0 | 251915.0 |
| human | Dylan Jones | 1.0 | 6.0 | 251915.0 |
| single | Dónde Irán | 1.0 | 4.0 | 38372.0 |
| album | E.L.E. (Extinction Level Event): The Final World Front | 2.0 | 6.0 | 49063.0 |
| single | Easter | 14.0 | 19.0 | 38372.0 |
| album | Easter | 14.0 | 19.0 | 49063.0 |
| album | Easter | 14.0 | 19.0 | 49063.0 |
| commune of France | Eaux-Puiseaux | 5.0 | 8.0 | 32436.0 |
| taxon | Echiteae | 5.0 | 4.0 | 14226.0 |
| human | Ed Marinaro | 2.0 | 10.0 | 251915.0 |
| human | Edgar Fruitier | 4.0 | 8.0 | 251915.0 |
| human | Edmund Beaufort, 2nd Duke of Somerset | 13.0 | 22.0 | 251915.0 |
| human | Edmund Burns | 27.0 | 7.0 | 251915.0 |
| human | Edred of England | 11.0 | 21.0 | 251915.0 |
| human | Edward Herbert, 3rd Baron Herbert of Chirbury | 1.0 | 4.0 | 251915.0 |
| human | Edward Warschilka | 1.0 | 8.0 | 251915.0 |
| human | Edwin H. Land | 1.0 | 14.0 | 251915.0 |
| human | Edwin Ward Moore | 1.0 | 5.0 | 251915.0 |
| street | Eemstein | 4.0 | 4.0 | 16483.0 |
| street | Eindweg | 1.0 | 4.0 | 16483.0 |
| album | El nuevo Rolando Alarcón | 2.0 | 5.0 | 49063.0 |
| human | Eldar Rønning | 1.0 | 10.0 | 251915.0 |
| taxon | Eleutherodactylus juanchoi | 1.0 | 4.0 | 14226.0 |
| taxon | Eligmodontus | 1.0 | 3.0 | 14226.0 |
| human | Elizabeth of Carinthia, Queen of Germany | 16.0 | 23.0 | 251915.0 |
| human | Ella Purnell | 1.0 | 6.0 | 251915.0 |
| street | Elzenlaan | 4.0 | 8.0 | 16483.0 |
| street | Elzenlaan | 4.0 | 8.0 | 16483.0 |
| human | Emel Sayın | 1.0 | 6.0 | 251915.0 |
| human | Emiliana Perina | 1.0 | 5.0 | 251915.0 |
| human | Emily Rutherfurd | 1.0 | 8.0 | 251915.0 |
| human | Emmanuel Gradi | 2.0 | 5.0 | 251915.0 |
| human | Empress Dowager Fujiwara no Ishi | 2.0 | 4.0 | 251915.0 |
| commune of France | Encausse | 1.0 | 4.0 | 32436.0 |
| album | EndSerenading | 1.0 | 2.0 | 49063.0 |
| human | Engelbrekt Engelbrektsson | 2.0 | 6.0 | 251915.0 |
| human | Enrico Pieranunzi | 1.0 | 10.0 | 251915.0 |
| human | Entissar Amer | 1.0 | 4.0 | 251915.0 |
| taxon | Equidae | 4.0 | 4.0 | 14226.0 |
| human | Erica Johnson Debeljak | 1.0 | 5.0 | 251915.0 |
| human | Erica Lancaster | 1.0 | 5.0 | 251915.0 |
| human | Erika Stiska | 5.0 | 6.0 | 251915.0 |
| human | Ernesta Bittanti Battisti | 2.0 | 9.0 | 251915.0 |
| human | Ernst Franz Karl von Gemmingen | 3.0 | 6.0 | 251915.0 |
| human | Ernst Marboe | 1.0 | 7.0 | 251915.0 |
| human | Ernst Stückelberg | 1.0 | 9.0 | 251915.0 |
| human | Ernst Waldow | 34.0 | 7.0 | 251915.0 |
| human | Eros Galbiati | 5.0 | 6.0 | 251915.0 |
| film | Escape from the Planet of the Apes | 2.0 | 31.0 | 13894.0 |
| commune of France | Esquièze-Sère | 2.0 | 5.0 | 32436.0 |
| commune of France | Estal | 1.0 | 4.0 | 32436.0 |
| commune of France | Estrée | 2.0 | 4.0 | 32436.0 |
| human | Ethan Phillips | 174.0 | 8.0 | 251915.0 |
| human | Ethan Vogt | 3.0 | 8.0 | 251915.0 |
| taxon | Euchaetidae | 2.0 | 3.0 | 14226.0 |
| human | Eugene Kaspersky | 2.0 | 11.0 | 251915.0 |
| human | Eugenio Lopez III | 1.0 | 10.0 | 251915.0 |
| human | Eva Cassidy | 5.0 | 12.0 | 251915.0 |
| human | Eva-Maria Hofmann | 1.0 | 5.0 | 251915.0 |
| human | Evelyn De Morgan | 3.0 | 12.0 | 251915.0 |
| street | Eversweg | 2.0 | 3.0 | 16483.0 |
| human | Ewout Genemans | 1.0 | 9.0 | 251915.0 |
| album | Extremist Makeover | 1.0 | 3.0 | 49063.0 |
| human | Ezra F. Kysor | 2.0 | 8.0 | 251915.0 |
| album | FH1 | 1.0 | 4.0 | 49063.0 |
| human | Fairfield Porter | 1.0 | 8.0 | 251915.0 |
| album | Fallen Is Babylon | 2.0 | 5.0 | 49063.0 |
| single | Fantastic future | 1.0 | 5.0 | 38372.0 |
| human | Faye | 35.0 | 6.0 | 251915.0 |
| street | Fazantstraat | 1.0 | 4.0 | 16483.0 |
| human | Federico Barón | 1.0 | 5.0 | 251915.0 |
| human | Felix Gonzalez Ares | 1.0 | 5.0 | 251915.0 |
| township in China | Fengshan | 1.0 | 3.0 | 19553.0 |
| human | Ferenc Komlóssy | 1.0 | 11.0 | 251915.0 |
| human | Ferenc Komlóssy | 1.0 | 11.0 | 251915.0 |
| album | Festival Session | 1.0 | 4.0 | 49063.0 |
| human | Fifi Young | 2.0 | 6.0 | 251915.0 |
| human | Filippo Pedrini | 1.0 | 6.0 | 251915.0 |
| album | First Under the Wire | 2.0 | 5.0 | 49063.0 |
| film | Flesh and Bone | 3.0 | 29.0 | 13894.0 |
| album | Flesh and Bone | 3.0 | 29.0 | 49063.0 |
| album | Flesh and Bone | 3.0 | 29.0 | 49063.0 |
| human | Fleur Lise Heuet | 2.0 | 5.0 | 251915.0 |
| human | Florencio Varela | 3.0 | 12.0 | 251915.0 |
| single | Flower | 25.0 | 77.0 | 38372.0 |
| single | Flower | 25.0 | 77.0 | 38372.0 |
| single | Flower | 25.0 | 77.0 | 38372.0 |
| single | Flower | 25.0 | 77.0 | 38372.0 |
| single | Flower | 25.0 | 77.0 | 38372.0 |
| single | Flower | 25.0 | 77.0 | 38372.0 |
| single | Flower | 25.0 | 77.0 | 38372.0 |
| single | Flower | 25.0 | 77.0 | 38372.0 |
| album | Flower | 25.0 | 77.0 | 49063.0 |
| album | Four in Blue | 1.0 | 4.0 | 49063.0 |
| human | Francesc Colomé Tenas | 1.0 | 5.0 | 251915.0 |
| human | Francesca Braggiotti | 4.0 | 8.0 | 251915.0 |
| human | Francesco Cabras | 3.0 | 6.0 | 251915.0 |
| human | Francesco Malcom | 5.0 | 7.0 | 251915.0 |
| human | Francesco Mandelli | 11.0 | 8.0 | 251915.0 |
| human | Francis Charles Philips | 1.0 | 8.0 | 251915.0 |
| human | Francisco Compes Martinez | 1.0 | 5.0 | 251915.0 |
| human | Francisco Jesús Hidalgo Pérez | 1.0 | 5.0 | 251915.0 |
| human | Francisco Lopez del Pozo | 1.0 | 5.0 | 251915.0 |
| human | Frankie Chan | 5.0 | 8.0 | 251915.0 |
| human | Franz Anton Schubert | 1.0 | 9.0 | 251915.0 |
| human | Franz Ernst | 1.0 | 15.0 | 251915.0 |
| human | Franz Ernst | 1.0 | 15.0 | 251915.0 |
| human | François Barberousse | 2.0 | 6.0 | 251915.0 |
| human | François-Henri Pinault | 3.0 | 13.0 | 251915.0 |
| album | Frecuencia Continental | 2.0 | 5.0 | 49063.0 |
| human | Frederick Barton Maurice | 2.0 | 19.0 | 251915.0 |
| human | Frederick, Prince of Anhalt-Bernburg-Schaumburg-Hoym | 1.0 | 7.0 | 251915.0 |
| street | Friedesemolen | 1.0 | 4.0 | 16483.0 |
| human | Friedrich Günther, Prince of Schwarzburg-Rudolstadt | 4.0 | 9.0 | 251915.0 |
| human | Friedrich Wilhelm Schnitzler | 1.0 | 11.0 | 251915.0 |
| human | Fritz Thyssen | 6.0 | 16.0 | 251915.0 |
| human | Frédéric Bazille | 14.0 | 10.0 | 251915.0 |
| album | Fuck with Fire | 1.0 | 3.0 | 49063.0 |
| human | Fujiwara no Kaneko/Kaishi | 4.0 | 8.0 | 251915.0 |
| human | Fujiwara no Kinsue | 9.0 | 13.0 | 251915.0 |
| human | Fujiwara no Sawako | 7.0 | 9.0 | 251915.0 |
| human | Fushimi-no-miya Kunisuke-shinnō | 4.0 | 8.0 | 251915.0 |
| single | Futari | 6.0 | 17.0 | 38372.0 |
| single | Futari | 6.0 | 17.0 | 38372.0 |
| single | Futari | 6.0 | 17.0 | 38372.0 |
| human | Félix Fries | 2.0 | 3.0 | 251915.0 |
| human | Gabe Sachs | 4.0 | 2.0 | 251915.0 |
| commune of France | Gabrias | 2.0 | 5.0 | 32436.0 |
| human | Gabriel Ferra Martorell | 1.0 | 5.0 | 251915.0 |
| human | Gabrielle Christian | 1.0 | 7.0 | 251915.0 |
| commune of France | Gaillon | 37.0 | 8.0 | 32436.0 |
| human | Gale Storm | 11.0 | 6.0 | 251915.0 |
| taxon | Galegeae | 10.0 | 3.0 | 14226.0 |
| taxon | Gammaherpesvirinae | 4.0 | 3.0 | 14226.0 |
| single | Gangsta Rap Made Me Do It | 1.0 | 4.0 | 38372.0 |
| township in China | Gaogezhuang | 1.0 | 3.0 | 19553.0 |
| human | Gary Chaw | 3.0 | 11.0 | 251915.0 |
| human | Gary Tarn | 4.0 | 7.0 | 251915.0 |
| human | Gebhard of Supplinburg | 2.0 | 4.0 | 251915.0 |
| street | Geert Wolter Smitweg | 1.0 | 4.0 | 16483.0 |
| human | Gene Sheldon | 9.0 | 8.0 | 251915.0 |
| single | Generation Wild | 3.0 | 9.0 | 38372.0 |
| album | Generation Wild | 3.0 | 9.0 | 49063.0 |
| human | Geoffrey James | 1.0 | 12.0 | 251915.0 |
| human | Geoffrey James | 1.0 | 12.0 | 251915.0 |
| taxon | Geohintonia mexicana | 1.0 | 4.0 | 14226.0 |
| human | Georg Abraham Schneider | 1.0 | 12.0 | 251915.0 |
| human | Georg Hackl | 1.0 | 13.0 | 251915.0 |
| human | Georg von Arco | 2.0 | 14.0 | 251915.0 |
| human | George Lamond | 4.0 | 5.0 | 251915.0 |
| human | George Osborne | 3.0 | 12.0 | 251915.0 |
| human | George, Emperor of Trebizond | 4.0 | 7.0 | 251915.0 |
| human | Georges Chamarat | 77.0 | 9.0 | 251915.0 |
| human | Georges Mathieu | 1.0 | 19.0 | 251915.0 |
| human | Georges Mathieu | 1.0 | 19.0 | 251915.0 |
| human | Georges Rouget | 1.0 | 14.0 | 251915.0 |
| human | Georgi Kadurin | 4.0 | 4.0 | 251915.0 |
| human | Georgia Groome | 4.0 | 6.0 | 251915.0 |
| human | Georgie Ripper | 3.0 | 6.0 | 251915.0 |
| human | Gerald Gladstone | 1.0 | 16.0 | 251915.0 |
| human | Gerald Gladstone | 1.0 | 16.0 | 251915.0 |
| human | Geraldine Chaplin | 101.0 | 18.0 | 251915.0 |
| human | Gerard I, Count of Guelders | 2.0 | 5.0 | 251915.0 |
| human | Gerard II, Count of Wassenberg | 3.0 | 6.0 | 251915.0 |
| human | Gerd Höfer | 1.0 | 8.0 | 251915.0 |
| album | German Engines | 1.0 | 4.0 | 49063.0 |
| human | Gerrit Kruize | 1.0 | 11.0 | 251915.0 |
| human | Gertrud Bredel | 1.0 | 6.0 | 251915.0 |
| human | Gianluca Maria Tavarelli | 7.0 | 6.0 | 251915.0 |
| human | Gianni Nanfa | 1.0 | 6.0 | 251915.0 |
| human | Gianni Rivera | 1.0 | 19.0 | 251915.0 |
| human | Gilbert Monckton, 2nd Viscount Monckton of Brenchley | 2.0 | 12.0 | 251915.0 |
| human | Gino Talamo | 7.0 | 8.0 | 251915.0 |
| human | Giorgio Vasari | 10.0 | 23.0 | 251915.0 |
| human | Giovanna Lenzi | 15.0 | 8.0 | 251915.0 |
| human | Giovanni Battista Cipriani | 3.0 | 6.0 | 251915.0 |
| human | Giovanni de Gamerra | 1.0 | 7.0 | 251915.0 |
| single | Girl Friend | 2.0 | 7.0 | 38372.0 |
| single | Girl Friend | 2.0 | 7.0 | 38372.0 |
| human | Giulia Gam | 2.0 | 5.0 | 251915.0 |
| human | Giuseppe Tartini | 1.0 | 10.0 | 251915.0 |
| taxon | Glyptopleura | 1.0 | 3.0 | 14226.0 |
| single | Gold on the Ceiling | 1.0 | 4.0 | 38372.0 |
| taxon | Goldfish | 1.0 | 4.0 | 14226.0 |
| street | Goltziusstraat | 6.0 | 8.0 | 16483.0 |
| street | Goltziusstraat | 6.0 | 8.0 | 16483.0 |
| commune of France | Gond-Pontouvre | 11.0 | 12.0 | 32436.0 |
| human | Gonzalo Sarrigoitia Oregui | 1.0 | 5.0 | 251915.0 |
| single | Good Time (Jin Akanishi song) | 1.0 | 4.0 | 38372.0 |
| single | Goodbye in Her Eyes | 1.0 | 4.0 | 38372.0 |
| album | Got It on My Mind | 1.0 | 4.0 | 49063.0 |
| street | Graaf van Burenstraat | 1.0 | 3.0 | 16483.0 |
| human | Grace Zaring Stone | 1.0 | 5.0 | 251915.0 |
| human | Graham Edwards | 4.0 | 13.0 | 251915.0 |
| human | Graham Edwards | 4.0 | 13.0 | 251915.0 |
| human | Grand Duchess Tatiana Nikolaevna of Russia | 7.0 | 19.0 | 251915.0 |
| human | Grand Duke Nicholas Constantinovich of Russia | 7.0 | 10.0 | 251915.0 |
| commune of France | Grandvaux | 3.0 | 8.0 | 32436.0 |
| album | Greatest Hits Encore | 2.0 | 5.0 | 49063.0 |
| human | Gregorio Rodriguez Lopez | 1.0 | 5.0 | 251915.0 |
| human | Grimes | 4.0 | 9.0 | 251915.0 |
| album | Grinding Stone | 1.0 | 4.0 | 49063.0 |
| single | Growing Up | 3.0 | 22.0 | 38372.0 |
| album | Growing Up | 3.0 | 22.0 | 49063.0 |
| album | Growing Up | 3.0 | 22.0 | 49063.0 |
| commune of France | Gréolières | 9.0 | 12.0 | 32436.0 |
| township in China | Guandu | 1.0 | 3.0 | 19553.0 |
| human | Guy Carbonneau | 1.0 | 10.0 | 251915.0 |
| human | Guy Fithen | 1.0 | 5.0 | 251915.0 |
| human | Guy de Binos | 2.0 | 4.0 | 251915.0 |
| human | Guè | 1.0 | 6.0 | 251915.0 |
| human | Gérard Filippelli | 16.0 | 6.0 | 251915.0 |
| human | Götz Otto | 14.0 | 7.0 | 251915.0 |
| human | H. F. Maltby | 3.0 | 5.0 | 251915.0 |
| commune of France | Hacqueville | 2.0 | 4.0 | 32436.0 |
| human | Hale Soygazi | 5.0 | 6.0 | 251915.0 |
| human | Hans Frank | 1.0 | 28.0 | 251915.0 |
| human | Hans Frank | 1.0 | 28.0 | 251915.0 |
| human | Hans Frank | 1.0 | 28.0 | 251915.0 |
| human | Hans Gerhard Creutzfeldt | 1.0 | 11.0 | 251915.0 |
| human | Hans Olof Ahnlund | 1.0 | 4.0 | 251915.0 |
| human | Hans Rausing | 5.0 | 12.0 | 251915.0 |
| human | Hans Rudolf Rahn | 6.0 | 14.0 | 251915.0 |
| human | Hans Rudolf Rahn | 6.0 | 14.0 | 251915.0 |
| single | Happy? | 6.0 | 15.0 | 38372.0 |
| album | Happy? | 6.0 | 15.0 | 49063.0 |
| album | Happy? | 6.0 | 15.0 | 49063.0 |
| human | Harriet Adams | 1.0 | 10.0 | 251915.0 |
| commune of France | Haudonville | 3.0 | 5.0 | 32436.0 |
| human | Hawise of Brittany | 3.0 | 9.0 | 251915.0 |
| human | Hayden Rorke | 25.0 | 9.0 | 251915.0 |
| human | He Xiangning | 1.0 | 6.0 | 251915.0 |
In the scatter plot we can see humans spread out widely over the two degree axis. We also see some other patterns:
- Films tend to have low in-degree, but higher out-degree. This corresponds to information being stored about the films, but not many other entities referencing the films.
- Taxons (taxonomical groups in biology) tend to have low out-degree, but higher in-degree. This is a bit harder to interpret, but is likely because many biological entities reference these as groups they belong to.
Symmetric Relations
Let's now start with a small example of basic motif mining. Here we try to find out to what extent the different edge relations are symmetric. We first find the subgraphs matching the motif "(a)-[r1]->(b); (b)-[r2]->(a)", filter for those where the edge relation is the same and count how many such 2-cycles exist for each relations. We then compute how large fraction of the total edges are part of such motifs. This indicates if a relation is symmetric in general.
val twoCycles = graph.find("(a)-[r1]->(b); (b)-[r2]->(a)")
val symCycles = twoCycles.filter("r1.rel == r2.rel")
val symCounts = symCycles.select("r1.src", "r1.rel", "r1.dst").groupBy("rel").count().cache()
display(symCounts)
| rel | count |
|---|---|
| part of | 506.0 |
| family name | 27.0 |
| parent astronomical body | 2.0 |
| topic's main Wikimedia portal | 1.0 |
| shares border with | 204711.0 |
| based on | 3932.0 |
| present in work | 30.0 |
| separated from | 2.0 |
| writing system | 9.0 |
| topic's main category | 1.0 |
| father | 926.0 |
| given name version for other gender | 258.0 |
| performer | 1492.0 |
| place of burial | 9.0 |
| influenced by | 4.0 |
| developer | 17.0 |
| depicts | 1757.0 |
| fictional or mythical analog of | 4.0 |
| producer | 18.0 |
| shape | 1.0 |
| taxon synonym | 59.0 |
| located in or next to body of water | 8.0 |
| replaced by | 10.0 |
| part of the series | 2407.0 |
| interleaves with | 28.0 |
| narrative location | 54.0 |
| participant | 10.0 |
| capital of | 14.0 |
| characters | 46.0 |
| collection | 1.0 |
| structure replaced by | 1.0 |
| owner of | 1.0 |
| has part(s) | 2095.0 |
| located in the administrative territorial entity | 5688.0 |
| employer | 4.0 |
| chairperson | 28.0 |
| lyrics by | 4.0 |
| place of birth | 11.0 |
| subclass of | 185.0 |
| instance of | 45937.0 |
| located on street | 24.0 |
| named after | 678.0 |
| mother house | 1.0 |
| country of origin | 2.0 |
| encoded by | 7.0 |
| composer | 3.0 |
| occupant | 5.0 |
| place of death | 2.0 |
| relative | 874.0 |
| director | 2.0 |
| category's main topic | 1.0 |
| spouse | 31112.0 |
| author | 9.0 |
| located in/on physical feature | 29.0 |
| pendant of | 214.0 |
| record label | 5.0 |
| from narrative universe | 1.0 |
| ortholog | 1863.0 |
| conferred by | 1.0 |
| diplomatic relation | 58.0 |
| Wikimedia portal's main topic | 1.0 |
| sport | 1.0 |
| child astronomical body | 2.0 |
| adjacent station | 24278.0 |
| mother | 18.0 |
| companion of | 24.0 |
| location of creation | 4.0 |
| imported from Wikimedia project | 32.0 |
| replaces | 7.0 |
| has facility | 1.0 |
| Unknown | 89075.0 |
| inspired by | 27.0 |
| student of | 2.0 |
| readable file format | 1.0 |
| commissioned by | 1.0 |
| statement is subject of | 8.0 |
| programmed in | 1.0 |
| origin of the watercourse | 2.0 |
| capital | 4274.0 |
| contains settlement | 4105.0 |
| founded by | 75.0 |
| member of | 34.0 |
| tracklist | 34.0 |
| notable work | 7.0 |
| tributary | 2.0 |
| lake outflow | 10.0 |
| follows | 737.0 |
| feast day | 1.0 |
| discoverer or inventor | 4.0 |
| movement | 3.0 |
| said to be the same as | 14782.0 |
| terminus | 128.0 |
| owned by | 13.0 |
| edition or translation of | 134.0 |
| mouth of the watercourse | 16.0 |
| student | 6.0 |
| scheduled service destination | 6.0 |
| cast member | 22.0 |
| described by source | 16.0 |
| connecting line | 12.0 |
| decays to | 2.0 |
| home venue | 1.0 |
| software engine | 6.0 |
| soundtrack release | 11.0 |
| depicted by | 14.0 |
| copyright license | 1.0 |
| catalog | 1.0 |
| architect | 6.0 |
| has use | 2.0 |
| parent taxon | 21.0 |
| residence | 2.0 |
| country | 283.0 |
| headquarters location | 17.0 |
| has edition or translation | 132.0 |
| home world | 1.0 |
| color | 6.0 |
| encodes | 8.0 |
| doctoral advisor | 2.0 |
| dedicated to | 7.0 |
| opposite of | 1405.0 |
| filming location | 9.0 |
| territory claimed by | 2.0 |
| partially coincident with | 482.0 |
| point group | 1.0 |
| winner | 280.0 |
| stock exchange | 2.0 |
| child | 934.0 |
| location | 63.0 |
| family name identical to this given name | 301.0 |
| given name | 1054.0 |
| dual to | 254.0 |
| made from material | 1.0 |
| unmarried partner | 650.0 |
| is a list of | 11.0 |
| genre | 50149.0 |
| affiliation | 2.0 |
| contains the administrative territorial entity | 291.0 |
| interchange station | 59.0 |
| significant drug interaction | 1404.0 |
| killed by | 10.0 |
| main subject | 4274.0 |
| partner in business or sport | 4.0 |
| political ideology | 9.0 |
| screenwriter | 5.0 |
| twinned administrative body | 38504.0 |
| crew member(s) | 2.0 |
| published in | 4.0 |
| presenter | 4.0 |
| manufacturer | 4.0 |
| followed by | 755.0 |
| contributor to the creative work or subject | 25.0 |
| website account on | 1.0 |
| conflict | 1.0 |
| chief executive officer | 4.0 |
| publisher | 11.0 |
| creator | 17.0 |
| facet of | 6.0 |
| parent organization | 1.0 |
| operator | 2.0 |
| underlies | 1.0 |
val symCountsRenamed = symCounts.select(($"rel").as("symRel"), ($"count").as("symCount"))
val relCountsRenamed = relCounts.select(($"rel").as("totRel"), ($"count").as("totalCount"))
val joinedCounts = symCountsRenamed.join(relCountsRenamed, symCountsRenamed("symRel") === relCountsRenamed("totRel"), "inner")
val symFractionDf = joinedCounts.select(($"symRel").as("rel"), ($"symCount"/$"totalCount").as("symFraction")) // Compute #relation is symmetric / #relation occurs
display(symFractionDf)
| rel | symFraction |
|---|---|
| part of | 8.165373009085188e-3 |
| topic's main Wikimedia portal | 1.6129032258064516e-3 |
| family name | 2.85826196500217e-4 |
| parent astronomical body | 4.016064257028112e-3 |
| shares border with | 1.002355187778485 |
| based on | 0.671448087431694 |
| present in work | 7.374631268436578e-3 |
| separated from | 8.695652173913043e-2 |
| topic's main category | 7.158196134574087e-4 |
| writing system | 1.8711018711018712e-2 |
| father | 2.1411394746577876e-2 |
| given name version for other gender | 1.015748031496063 |
| performer | 1.5973277947883432e-2 |
| place of burial | 2.8832292167227293e-4 |
| influenced by | 1.2903225806451613e-2 |
| developer | 1.176877812391831e-3 |
| depicts | 3.150439304285458e-2 |
| fictional or mythical analog of | 2.564102564102564e-2 |
| producer | 4.3741342859225776e-4 |
| shape | 1.3333333333333334e-2 |
| taxon synonym | 0.34104046242774566 |
| located in or next to body of water | 9.512485136741973e-3 |
| replaced by | 1.394700139470014e-2 |
| part of the series | 9.866775978684157e-2 |
| interleaves with | 1.0 |
| participant | 1.0198878123406426e-3 |
| narrative location | 3.2177332856632105e-3 |
| capital of | 6.511627906976744e-2 |
| collection | 3.600748955782803e-5 |
| characters | 1.627166607711355e-2 |
| structure replaced by | 3.125e-2 |
| owner of | 7.142857142857142e-2 |
| has part(s) | 6.474242096480114e-2 |
| located in the administrative territorial entity | 1.4064551544059285e-2 |
| employer | 5.0138507627320475e-5 |
| chairperson | 1.1720385098367517e-2 |
| place of birth | 1.6157936484620582e-5 |
| lyrics by | 1.0689470871191875e-3 |
| located on street | 5.948692526955013e-4 |
| instance of | 1.7955320617603306e-2 |
| subclass of | 3.920737522517749e-3 |
| named after | 3.1024068820353252e-2 |
| mother house | 4.545454545454545e-3 |
| country of origin | 2.8497335499130832e-5 |
| composer | 6.584723441615452e-4 |
| encoded by | 3.6784025223331584e-3 |
| occupant | 9.777082518576457e-4 |
| place of death | 6.155304487524737e-6 |
| relative | 0.4855555555555556 |
| director | 2.504351310401823e-5 |
| category's main topic | 9.910802775024777e-4 |
| spouse | 0.9890640895218719 |
| author | 2.822909478702716e-4 |
| located in/on physical feature | 6.831566548881037e-3 |
| record label | 5.584907346387124e-5 |
| pendant of | 1.2738095238095237 |
| from narrative universe | 2.7662517289073305e-4 |
| ortholog | 1.007027027027027 |
| conferred by | 2.881844380403458e-3 |
| diplomatic relation | 9.965635738831616e-2 |
| sport | 1.7501793933878222e-5 |
| Wikimedia portal's main topic | 1.607717041800643e-3 |
| child astronomical body | 4.694835680751174e-3 |
| mother | 1.0422094841063053e-3 |
| adjacent station | 0.9560526108529573 |
| location of creation | 3.980099502487562e-3 |
| companion of | 1.0 |
| imported from Wikimedia project | 4.5714285714285714e-2 |
| replaces | 1.3257575757575758e-2 |
| has facility | 1.1904761904761904e-2 |
| Unknown | 0.8739783553606295 |
| inspired by | 8.307692307692308e-2 |
| student of | 8.964589870013447e-4 |
| readable file format | 4.3478260869565216e-2 |
| commissioned by | 2.105263157894737e-3 |
| programmed in | 1.4792899408284023e-3 |
| statement is subject of | 5.128205128205128e-2 |
| origin of the watercourse | 1.3245033112582781e-2 |
| capital | 0.2968674029311662 |
| contains settlement | 0.6863400769102157 |
| founded by | 2.0598736610821202e-2 |
| member of | 5.708050029379669e-4 |
| tracklist | 5.128205128205128e-2 |
| notable work | 4.412784466998676e-4 |
| lake outflow | 3.436426116838488e-2 |
| tributary | 5.58659217877095e-3 |
| follows | 4.241921930218369e-3 |
| feast day | 1.584786053882726e-3 |
| discoverer or inventor | 7.9805275128686e-5 |
| movement | 3.394817245671608e-4 |
| said to be the same as | 0.9737812911725955 |
| terminus | 5.0058662495111456e-2 |
| owned by | 5.840071877807727e-4 |
| edition or translation of | 0.19910846953937592 |
| mouth of the watercourse | 3.4460478139134183e-3 |
| student | 1.0291595197255575e-2 |
| scheduled service destination | 9.230769230769231e-2 |
| cast member | 3.966293714866751e-5 |
| described by source | 5.766596986953074e-4 |
| decays to | 4.784688995215311e-4 |
| home venue | 2.7114967462039046e-4 |
| connecting line | 1.738878423416896e-3 |
| software engine | 3.0211480362537764e-3 |
| soundtrack release | 0.34375 |
| copyright license | 3.741114852225963e-4 |
| depicted by | 0.2857142857142857 |
| catalog | 1.0309278350515464e-2 |
| has use | 6.146281499692685e-4 |
| architect | 8.441193021947102e-4 |
| parent taxon | 1.851753875456325e-4 |
| residence | 5.379236148466917e-4 |
| country | 6.858511097216365e-4 |
| headquarters location | 2.7296082209377005e-3 |
| has edition or translation | 0.11934900542495479 |
| home world | 6.25e-2 |
| color | 1.1406844106463879e-2 |
| encodes | 4.206098843322818e-3 |
| doctoral advisor | 3.1645569620253164e-3 |
| dedicated to | 2.413793103448276e-2 |
| opposite of | 0.9908321579689704 |
| filming location | 7.000622277535781e-4 |
| territory claimed by | 5.8823529411764705e-2 |
| partially coincident with | 0.8441330998248686 |
| point group | 1.2658227848101266e-2 |
| winner | 0.29350104821802936 |
| stock exchange | 2.11864406779661e-3 |
| child | 1.5460504535522744e-2 |
| location | 8.260345098861908e-4 |
| family name identical to this given name | 0.8624641833810889 |
| given name | 8.100794169887642e-4 |
| dual to | 0.9921875 |
| made from material | 2.0082740892477006e-5 |
| unmarried partner | 0.7328072153325818 |
| is a list of | 6.577767147042994e-4 |
| genre | 0.27999776667318055 |
| affiliation | 1.1111111111111112e-2 |
| contains the administrative territorial entity | 1.9813440457547493e-3 |
| interchange station | 0.7866666666666666 |
| significant drug interaction | 0.8036634230108758 |
| killed by | 3.2679738562091505e-2 |
| main subject | 0.29023495857666715 |
| political ideology | 3.739094308267553e-3 |
| partner in business or sport | 0.8 |
| screenwriter | 1.0432532810315688e-4 |
| twinned administrative body | 1.0104975855553222 |
| crew member(s) | 1.0793308148947653e-3 |
| published in | 3.766478342749529e-3 |
| presenter | 4.3859649122807015e-3 |
| manufacturer | 6.268609935746748e-4 |
| followed by | 4.350005473516821e-3 |
| contributor to the creative work or subject | 2.771618625277162e-2 |
| website account on | 8.535336292249915e-5 |
| conflict | 2.184598580010923e-5 |
| chief executive officer | 1.3559322033898305e-2 |
| publisher | 3.6730332576465877e-4 |
| creator | 5.656484993678046e-4 |
| facet of | 3.4502587694077054e-3 |
| parent organization | 1.869158878504673e-3 |
| operator | 1.7373175816539263e-4 |
| underlies | 1.25e-2 |
Inspecting these results we can see that the relations with highest symmetry in the graph match our intuition for symmetric relations. We here find things like "opposite of", "companion of" and "unmarried partner".
Analysing the different types of relations
In this notebook we look at the relations in terms of their multiplicities. We are interested in grouping the relationships based on if they go from one or many entitites (1/M) to one or many other entitites. This gives us the four categories:
- 1-1: Which relations have a 1-1 correspondence, e.g., "married to"?
- 1-M: Which relations have 1-M, e.g., "is birthplace of"?
- M-1: Which relations have M-1, e.g., "is born in city"?
- M-M: Which relations are many-to-many, e.g., "classmates"?
To do this analysis we work with the dataframe containing the edges of the graph. The pattern (?-M), relations to many entitites, can be detected by finding some set of edge with the same source entity and relation. This means that the source entity has this relation to multipl other entities, and this relation can in general be ?-M. In a similar way the pattern (M-?) can be found by finding edges with the same relation and destination entity. To then classify each relation into the four categories it is enough to consider which of the two patterns above the relation matches. For example, if it matches none of them it is a 1-1 relation. Below we perform this computation.
./02_load_data
val srcRelGrp = graph.edges.groupBy("src","rel").count() // Count how many times each combination of source entity and relation occurs
val relSrcGrp = graph.edges.groupBy("rel", "dst").count() // Same for combinations of relation and destination entity
srcRelGrp: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
relSrcGrp: org.apache.spark.sql.DataFrame = [rel: string, dst: string ... 1 more field]
import spark.implicits._
import org.graphframes._
// Find the maximum times such combinations occur for each relation
val rel_max_srcgrp = relSrcGrp.groupBy("rel").max()
val rel_max_dstgrp = srcRelGrp.groupBy("rel").max()
rel_max_srcgrp: org.apache.spark.sql.DataFrame = [rel: string, max(count): bigint]
rel_max_dstgrp: org.apache.spark.sql.DataFrame = [rel: string, max(count): bigint]
df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
// Join into one dataframe
val jointrel = rel_max_dstgrp.join(rel_max_srcgrp,rel_max_dstgrp("rel") === rel_max_srcgrp("rel"), "inner")
val newColumns = Seq("rel","src_count","rel","dst_count")
val rel_count_src_dst = jointrel.toDF(newColumns:_*)
jointrel: org.apache.spark.sql.DataFrame = [rel: string, max(count): bigint ... 2 more fields]
newColumns: Seq[String] = List(rel, src_count, rel, dst_count)
rel_count_src_dst: org.apache.spark.sql.DataFrame = [rel: string, src_count: bigint ... 2 more fields]
df1: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
entdescdf: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
import spark.implicits._
mergedDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
list: List[String] = List(src, rel, dst, srcentid, srclabel)
mergedDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
display(rel_count_src_dst)
| rel | src_count | rel | dst_count |
|---|---|---|---|
| godparent | 2.0 | godparent | 1.0 |
| part of | 33.0 | part of | 1461.0 |
| molecular function | 47.0 | molecular function | 947.0 |
| playing hand | 3.0 | playing hand | 1318.0 |
| place served by transport hub | 2.0 | place served by transport hub | 3.0 |
| family name | 104.0 | family name | 4629.0 |
| topic's main Wikimedia portal | 2.0 | topic's main Wikimedia portal | 6.0 |
| parent astronomical body | 2.0 | parent astronomical body | 50.0 |
| shares border with | 76.0 | shares border with | 76.0 |
| based on | 27.0 | based on | 150.0 |
| present in work | 46.0 | present in work | 441.0 |
| filmography | 12.0 | filmography | 2.0 |
| public holiday | 15.0 | public holiday | 25.0 |
| topic's main category | 37.0 | topic's main category | 40.0 |
| sexual orientation | 2.0 | sexual orientation | 2902.0 |
| writing system | 4.0 | writing system | 256.0 |
| father | 13.0 | father | 69.0 |
| given name version for other gender | 3.0 | given name version for other gender | 3.0 |
| industry | 13.0 | industry | 1370.0 |
| applies to jurisdiction | 28.0 | applies to jurisdiction | 54.0 |
| worshipped by | 3.0 | worshipped by | 192.0 |
| performer | 60.0 | performer | 140.0 |
| business division | 37.0 | business division | 4.0 |
| place of burial | 5.0 | place of burial | 3043.0 |
| influenced by | 10.0 | influenced by | 7.0 |
| this taxon is source of | 2.0 | this taxon is source of | 4.0 |
| fictional universe described in | 11.0 | fictional universe described in | 4.0 |
| developer | 9.0 | developer | 499.0 |
| head of state | 23.0 | head of state | 42.0 |
| notation | 1.0 | notation | 1.0 |
| depicts | 692.0 | depicts | 3303.0 |
| currency | 23.0 | currency | 67.0 |
| ESRB rating | 3.0 | ESRB rating | 1086.0 |
| replaced synonym (for nom. nov.) | 1.0 | replaced synonym (for nom. nov.) | 1.0 |
| fictional or mythical analog of | 2.0 | fictional or mythical analog of | 2.0 |
| armament | 14.0 | armament | 162.0 |
| basic form of government | 6.0 | basic form of government | 61.0 |
| producer | 17.0 | producer | 397.0 |
| shape | 1.0 | shape | 28.0 |
| taxon synonym | 16.0 | taxon synonym | 2.0 |
| highest judicial authority | 2.0 | highest judicial authority | 2.0 |
| located in or next to body of water | 5.0 | located in or next to body of water | 38.0 |
| replaced by | 10.0 | replaced by | 9.0 |
| part of the series | 55.0 | part of the series | 577.0 |
| measured physical quantity | 2.0 | measured physical quantity | 3.0 |
| interleaves with | 3.0 | interleaves with | 3.0 |
| participant | 106.0 | participant | 36.0 |
| narrative location | 11.0 | narrative location | 1904.0 |
| recorded at studio or venue | 7.0 | recorded at studio or venue | 21.0 |
| lake on watercourse | 6.0 | lake on watercourse | 2.0 |
| place of origin (Switzerland) | 4.0 | place of origin (Switzerland) | 75.0 |
| transport network | 16.0 | transport network | 844.0 |
| list related to category | 1.0 | list related to category | 1.0 |
| official language | 24.0 | official language | 1327.0 |
| capital of | 6.0 | capital of | 2.0 |
| avionics | 5.0 | avionics | 6.0 |
| collection | 80.0 | collection | 5290.0 |
| characters | 53.0 | characters | 71.0 |
| donated by | 3.0 | donated by | 3.0 |
| film editor | 8.0 | film editor | 29.0 |
| executive producer | 10.0 | executive producer | 28.0 |
| structure replaced by | 2.0 | structure replaced by | 2.0 |
| has part(s) | 103.0 | has part(s) | 432.0 |
| located in the administrative territorial entity | 258.0 | located in the administrative territorial entity | 7237.0 |
| employer | 11.0 | employer | 3198.0 |
| sponsor | 7.0 | sponsor | 2.0 |
| chairperson | 20.0 | chairperson | 31.0 |
| place of birth | 25.0 | place of birth | 14404.0 |
| lyrics by | 6.0 | lyrics by | 174.0 |
| located on street | 47.0 | located on street | 726.0 |
| instance of | 439.0 | instance of | 1510474.0 |
| subclass of | 12.0 | subclass of | 1930.0 |
| points/goal scored by | 6.0 | points/goal scored by | 2.0 |
| structural engineer | 2.0 | structural engineer | 53.0 |
| exclave of | 3.0 | exclave of | 3.0 |
| named after | 258.0 | named after | 545.0 |
| officially opened by | 3.0 | officially opened by | 5.0 |
| mother house | 4.0 | mother house | 14.0 |
| maintained by | 10.0 | maintained by | 845.0 |
| country of origin | 19.0 | country of origin | 27099.0 |
| rector | 119.0 | rector | 2.0 |
| medical condition | 5.0 | medical condition | 918.0 |
| original combination | 1.0 | original combination | 1.0 |
| CPU | 2.0 | CPU | 119.0 |
| airline hub | 11.0 | airline hub | 10.0 |
| has facet polytope | 4.0 | has facet polytope | 208.0 |
| consecrator | 3.0 | consecrator | 6.0 |
| site of astronomical discovery | 2.0 | site of astronomical discovery | 12986.0 |
| licensed to broadcast to | 1.0 | licensed to broadcast to | 3.0 |
| category related to list | 1.0 | category related to list | 1.0 |
| has vertex figure | 1.0 | has vertex figure | 1.0 |
| encoded by | 2.0 | encoded by | 2.0 |
| composer | 6.0 | composer | 202.0 |
| main building contractor | 2.0 | main building contractor | 61.0 |
| allegiance | 3.0 | allegiance | 27.0 |
| translator | 3.0 | translator | 19.0 |
| organizer | 3.0 | organizer | 66.0 |
| occupant | 10.0 | occupant | 102.0 |
| represents | 1.0 | represents | 1.0 |
| place of death | 15.0 | place of death | 17262.0 |
| solved by | 2.0 | solved by | 1.0 |
| programmer | 2.0 | programmer | 17.0 |
| relative | 9.0 | relative | 6.0 |
| legislated by | 1.0 | legislated by | 25.0 |
| physically interacts with | 2.0 | physically interacts with | 1.0 |
| member of sports team | 55.0 | member of sports team | 1795.0 |
| director | 51.0 | director | 543.0 |
| category's main topic | 3.0 | category's main topic | 4.0 |
| category of associated people | 2.0 | category of associated people | 1.0 |
| spouse | 22.0 | spouse | 22.0 |
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| foundational text | 3.0 | foundational text | 6.0 |
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| language of work or name | 35.0 | language of work or name | 1722.0 |
| patron saint | 5.0 | patron saint | 54.0 |
| record label | 40.0 | record label | 3454.0 |
| pendant of | 8.0 | pendant of | 8.0 |
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| position held | 33.0 | position held | 13796.0 |
| diocese | 5.0 | diocese | 122.0 |
| ortholog | 2.0 | ortholog | 2.0 |
| endemic to | 4.0 | endemic to | 44.0 |
| lifestyle | 2.0 | lifestyle | 326.0 |
| home port | 2.0 | home port | 6.0 |
| category combines topics | 4.0 | category combines topics | 8018.0 |
| day in year for periodic occurrence | 14.0 | day in year for periodic occurrence | 4.0 |
| conferred by | 3.0 | conferred by | 13.0 |
| postsynaptic connection | 1.0 | postsynaptic connection | 1.0 |
| cover art by | 2.0 | cover art by | 10.0 |
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| diplomatic relation | 133.0 | diplomatic relation | 94.0 |
| office held by head of government | 12.0 | office held by head of government | 1594.0 |
| Wikimedia portal's main topic | 6.0 | Wikimedia portal's main topic | 2.0 |
| native language | 3.0 | native language | 1340.0 |
| sport | 18.0 | sport | 36409.0 |
| country of citizenship | 80.0 | country of citizenship | 215882.0 |
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| mother | 6.0 | mother | 60.0 |
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| companion of | 2.0 | companion of | 2.0 |
| imported from Wikimedia project | 16.0 | imported from Wikimedia project | 249.0 |
| central bank/issuer | 2.0 | central bank/issuer | 2.0 |
| noble title | 3.0 | noble title | 2956.0 |
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| Unknown | 269.0 | Unknown | 810.0 |
| cause of death | 5.0 | cause of death | 5153.0 |
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| readable file format | 4.0 | readable file format | 4.0 |
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| coat of arms | 2.0 | coat of arms | 8.0 |
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| programmed in | 6.0 | programmed in | 194.0 |
| origin of the watercourse | 2.0 | origin of the watercourse | 9.0 |
| country for sport | 2.0 | country for sport | 6.0 |
| voice actor | 20.0 | voice actor | 70.0 |
| main regulatory text | 3.0 | main regulatory text | 82.0 |
| capital | 7.0 | capital | 28.0 |
| academic degree | 4.0 | academic degree | 9389.0 |
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| librettist | 5.0 | librettist | 12.0 |
| tracklist | 27.0 | tracklist | 3.0 |
| activating neurotransmitter | 1.0 | activating neurotransmitter | 2.0 |
| instruction set | 2.0 | instruction set | 8.0 |
| review score by | 1.0 | review score by | 1.0 |
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| tributary | 19.0 | tributary | 2.0 |
| constellation | 5.0 | constellation | 22.0 |
| continent | 5.0 | continent | 1809.0 |
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| follows | 52.0 | follows | 50.0 |
| valid in period | 5.0 | valid in period | 2.0 |
| feast day | 3.0 | feast day | 6.0 |
| movement | 12.0 | movement | 1542.0 |
| head coach | 38.0 | head coach | 6.0 |
| discoverer or inventor | 5.0 | discoverer or inventor | 12992.0 |
| terminus location | 4.0 | terminus location | 11.0 |
| distribution format | 7.0 | distribution format | 1743.0 |
| said to be the same as | 31.0 | said to be the same as | 31.0 |
| applies to part | 2.0 | applies to part | 1.0 |
| owned by | 18.0 | owned by | 845.0 |
| terminus | 11.0 | terminus | 35.0 |
| edition or translation of | 15.0 | edition or translation of | 12.0 |
| definition domain | 1.0 | definition domain | 1.0 |
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| mouth of the watercourse | 6.0 | mouth of the watercourse | 72.0 |
| student | 14.0 | student | 5.0 |
| architectural style | 26.0 | architectural style | 706.0 |
| central bank | 1.0 | central bank | 1.0 |
| Fach | 3.0 | Fach | 111.0 |
| scheduled service destination | 51.0 | scheduled service destination | 3.0 |
| cast member | 282.0 | cast member | 347.0 |
| educated at | 18.0 | educated at | 11262.0 |
| described by source | 13.0 | described by source | 9578.0 |
| ancestral home | 2.0 | ancestral home | 6.0 |
| connecting line | 14.0 | connecting line | 144.0 |
| home venue | 6.0 | home venue | 12.0 |
| decays to | 10.0 | decays to | 12.0 |
| item operated | 51.0 | item operated | 102.0 |
| category for people who died here | 4.0 | category for people who died here | 2.0 |
| software engine | 6.0 | software engine | 251.0 |
| candidate | 11.0 | candidate | 4.0 |
| soundtrack release | 8.0 | soundtrack release | 2.0 |
| copyright license | 8.0 | copyright license | 1462.0 |
| depicted by | 3.0 | depicted by | 3.0 |
| commemorates | 2.0 | commemorates | 3.0 |
| illustrator | 5.0 | illustrator | 57.0 |
| kinship to subject | 1.0 | kinship to subject | 1.0 |
| architect | 13.0 | architect | 155.0 |
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| enclave within | 3.0 | enclave within | 3.0 |
| parent taxon | 7.0 | parent taxon | 3135.0 |
| residence | 9.0 | residence | 98.0 |
| has subsidiary | 9.0 | has subsidiary | 2.0 |
| defendant | 1.0 | defendant | 1.0 |
| country | 258.0 | country | 84551.0 |
| crosses | 3.0 | crosses | 82.0 |
| shooting handedness | 1.0 | shooting handedness | 4.0 |
| headquarters location | 13.0 | headquarters location | 171.0 |
| torch lit by | 6.0 | torch lit by | 1.0 |
| editor | 18.0 | editor | 9.0 |
| has edition or translation | 12.0 | has edition or translation | 166.0 |
| distributed by | 4.0 | distributed by | 91.0 |
| home world | 2.0 | home world | 5.0 |
| category for films shot at this location | 2.0 | category for films shot at this location | 1.0 |
| league | 8.0 | league | 107.0 |
| color | 11.0 | color | 104.0 |
| encodes | 2.0 | encodes | 2.0 |
| original language of film or TV show | 22.0 | original language of film or TV show | 53297.0 |
| doctoral advisor | 4.0 | doctoral advisor | 14.0 |
| spore print color | 1.0 | spore print color | 280.0 |
| top-level Internet domain | 3.0 | top-level Internet domain | 6.0 |
| mascot | 1.0 | mascot | 2.0 |
| discography | 1.0 | discography | 1.0 |
| dedicated to | 4.0 | dedicated to | 17.0 |
| award received | 34.0 | award received | 15971.0 |
| opposite of | 3.0 | opposite of | 3.0 |
| radio format | 1.0 | radio format | 1.0 |
| filming location | 14.0 | filming location | 1040.0 |
| military rank | 10.0 | military rank | 1834.0 |
| territory claimed by | 4.0 | territory claimed by | 6.0 |
| partially coincident with | 21.0 | partially coincident with | 24.0 |
| point group | 2.0 | point group | 12.0 |
| flag | 1.0 | flag | 8.0 |
| winner | 18.0 | winner | 26.0 |
| destination point | 2.0 | destination point | 17.0 |
| stock exchange | 5.0 | stock exchange | 407.0 |
| child | 69.0 | child | 14.0 |
| engine configuration | 1.0 | engine configuration | 79.0 |
| parent of this hybrid, breed, or cultivar | 2.0 | parent of this hybrid, breed, or cultivar | 1.0 |
| convicted of | 4.0 | convicted of | 1460.0 |
| space group | 5.0 | space group | 89.0 |
| category for people born here | 3.0 | category for people born here | 2.0 |
| tonality | 2.0 | tonality | 18.0 |
| diplomatic mission sent | 2.0 | diplomatic mission sent | 92.0 |
| oath made by | 3.0 | oath made by | 1.0 |
| referee | 7.0 | referee | 4.0 |
| location | 256.0 | location | 5283.0 |
| eye color | 2.0 | eye color | 223.0 |
| production company | 9.0 | production company | 1511.0 |
| family name identical to this given name | 3.0 | family name identical to this given name | 3.0 |
| natural product of taxon | 4.0 | natural product of taxon | 2.0 |
| member of political party | 11.0 | member of political party | 21072.0 |
| connecting service | 13.0 | connecting service | 182.0 |
| vessel class | 4.0 | vessel class | 61.0 |
| ammunition | 9.0 | ammunition | 49.0 |
| language regulatory body | 3.0 | language regulatory body | 3.0 |
| taxonomic type | 1.0 | taxonomic type | 3.0 |
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| including | 4.0 | including | 1.0 |
| primary destinations | 16.0 | primary destinations | 4.0 |
| head of government | 90.0 | head of government | 9.0 |
| given name | 106.0 | given name | 26120.0 |
| blood type | 1.0 | blood type | 9.0 |
| officeholder | 12.0 | officeholder | 2.0 |
| located in time zone | 13.0 | located in time zone | 25664.0 |
| cause of destruction | 2.0 | cause of destruction | 10.0 |
| list of monuments | 7.0 | list of monuments | 2.0 |
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| participant in | 36.0 | participant in | 9207.0 |
| dual to | 2.0 | dual to | 2.0 |
| executive body | 2.0 | executive body | 12.0 |
| astronaut mission | 8.0 | astronaut mission | 3.0 |
| legal form | 3.0 | legal form | 85.0 |
| religious order | 4.0 | religious order | 1168.0 |
| name day | 3.0 | name day | 5.0 |
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| doctoral student | 16.0 | doctoral student | 2.0 |
| heritage designation | 60.0 | heritage designation | 53050.0 |
| appointed by | 2.0 | appointed by | 29.0 |
| unmarried partner | 8.0 | unmarried partner | 8.0 |
| exhibition history | 28.0 | exhibition history | 37.0 |
| product or material produced | 21.0 | product or material produced | 17.0 |
| is a list of | 5.0 | is a list of | 8357.0 |
| professorship | 3.0 | professorship | 21.0 |
| languages spoken, written or signed | 10.0 | languages spoken, written or signed | 31379.0 |
| operating system | 13.0 | operating system | 204.0 |
| platform | 30.0 | platform | 5894.0 |
| place of publication | 9.0 | place of publication | 126.0 |
| instrument | 19.0 | instrument | 9593.0 |
| genre | 216.0 | genre | 9431.0 |
| biological process | 205.0 | biological process | 257.0 |
| manner of death | 3.0 | manner of death | 2643.0 |
| affiliation | 3.0 | affiliation | 47.0 |
| anthem | 2.0 | anthem | 52.0 |
| significant event | 11.0 | significant event | 815.0 |
| contains the administrative territorial entity | 902.0 | contains the administrative territorial entity | 26.0 |
| cell component | 38.0 | cell component | 649.0 |
| asteroid spectral type | 2.0 | asteroid spectral type | 143.0 |
| parent club | 4.0 | parent club | 6.0 |
| highest point | 2.0 | highest point | 4.0 |
| temporal range start | 1.0 | temporal range start | 4.0 |
| asteroid family | 1.0 | asteroid family | 24.0 |
| start point | 2.0 | start point | 18.0 |
| legislative body | 3.0 | legislative body | 96.0 |
| significant drug interaction | 42.0 | significant drug interaction | 44.0 |
| killed by | 5.0 | killed by | 27.0 |
| has effect | 2.0 | has effect | 1.0 |
| main subject | 51.0 | main subject | 1564.0 |
| political ideology | 19.0 | political ideology | 248.0 |
| basionym | 1.0 | basionym | 8.0 |
| partner in business or sport | 1.0 | partner in business or sport | 1.0 |
| screenwriter | 65.0 | screenwriter | 191.0 |
| successful candidate | 12.0 | successful candidate | 12.0 |
| field of this occupation | 3.0 | field of this occupation | 12.0 |
| twinned administrative body | 98.0 | twinned administrative body | 98.0 |
| occupation | 137.0 | occupation | 223411.0 |
| has cause | 5.0 | has cause | 2.0 |
| crew member(s) | 10.0 | crew member(s) | 148.0 |
| published in | 3.0 | published in | 212.0 |
| original broadcaster | 7.0 | original broadcaster | 385.0 |
| presenter | 24.0 | presenter | 11.0 |
| director / manager | 36.0 | director / manager | 4.0 |
| location of discovery | 3.0 | location of discovery | 7.0 |
| theme music | 1.0 | theme music | 1.0 |
| manufacturer | 11.0 | manufacturer | 160.0 |
| takes place in fictional universe | 5.0 | takes place in fictional universe | 83.0 |
| chromosome | 2.0 | chromosome | 98.0 |
| followed by | 50.0 | followed by | 52.0 |
| contributor to the creative work or subject | 311.0 | contributor to the creative work or subject | 8.0 |
| printed by | 2.0 | printed by | 2.0 |
| website account on | 24.0 | website account on | 8317.0 |
| conflict | 10.0 | conflict | 16039.0 |
| exemplar of | 3.0 | exemplar of | 8.0 |
| chief executive officer | 10.0 | chief executive officer | 2.0 |
| publisher | 9.0 | publisher | 757.0 |
| creator | 83.0 | creator | 908.0 |
| facet of | 2.0 | facet of | 124.0 |
| commander of (DEPRECATED) | 5.0 | commander of (DEPRECATED) | 5.0 |
| parent organization | 5.0 | parent organization | 12.0 |
| operator | 34.0 | operator | 1045.0 |
| underlies | 3.0 | underlies | 3.0 |
| interaction | 4.0 | interaction | 17.0 |
| IUCN protected areas category | 3.0 | IUCN protected areas category | 508.0 |
| standards body | 2.0 | standards body | 11.0 |
| represented by | 2.0 | represented by | 8.0 |
| crystal system | 2.0 | crystal system | 182.0 |
| discovery method | 1.0 | discovery method | 8.0 |
| Eight Banner register | 2.0 | Eight Banner register | 55.0 |
| location of landing | 1.0 | location of landing | 3.0 |
| hair color | 1.0 | hair color | 138.0 |
| cathedral | 2.0 | cathedral | 2.0 |
| prosecutor | 3.0 | prosecutor | 1.0 |
| medical examination | 5.0 | medical examination | 7.0 |
| docking port | 1.0 | docking port | 1.0 |
| game mode | 9.0 | game mode | 15220.0 |
| IUCN conservation status | 1.0 | IUCN conservation status | 1963.0 |
| found in taxon | 4.0 | found in taxon | 1985.0 |
| has contributing factor | 3.0 | has contributing factor | 1.0 |
| has facility | 7.0 | has facility | 15.0 |
| has immediate cause | 2.0 | has immediate cause | 7.0 |
| input device | 5.0 | input device | 1881.0 |
| list of episodes | 1.0 | list of episodes | 2.0 |
| office contested | 1.0 | office contested | 6.0 |
| charge | 2.0 | charge | 1.0 |
| manifestation of | 1.0 | manifestation of | 2.0 |
| chief operating officer | 1.0 | chief operating officer | 1.0 |
| space tug | 1.0 | space tug | 4.0 |
| honorific prefix | 3.0 | honorific prefix | 52.0 |
| IMA status and/or rank | 3.0 | IMA status and/or rank | 294.0 |
| brand | 1.0 | brand | 22.0 |
| CERO rating | 2.0 | CERO rating | 659.0 |
| topic's main template | 1.0 | topic's main template | 1.0 |
| port of registry | 3.0 | port of registry | 7.0 |
| afflicts | 7.0 | afflicts | 5.0 |
| space launch vehicle | 1.0 | space launch vehicle | 45.0 |
| stated in | 6.0 | stated in | 3.0 |
| of | 1.0 | of | 2.0 |
| guidance system | 2.0 | guidance system | 35.0 |
| GUI toolkit or framework | 4.0 | GUI toolkit or framework | 35.0 |
| twinning | 2.0 | twinning | 1.0 |
| party chief representative | 5.0 | party chief representative | 2.0 |
| structure replaces | 1.0 | structure replaces | 1.0 |
| vehicle | 2.0 | vehicle | 42.0 |
| academic thesis | 2.0 | academic thesis | 1.0 |
| route of administration | 4.0 | route of administration | 18.0 |
| academic major | 2.0 | academic major | 4.0 |
| temporal range end | 2.0 | temporal range end | 4.0 |
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| interchange station | 3.0 | interchange station | 3.0 |
| streak color | 1.0 | streak color | 25.0 |
| wing configuration | 2.0 | wing configuration | 252.0 |
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| MPA film rating | 1.0 | MPA film rating | 6.0 |
| category of people buried here | 1.0 | category of people buried here | 1.0 |
| drafted by | 1.0 | drafted by | 9.0 |
| writable file format | 5.0 | writable file format | 4.0 |
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| mushroom cap shape | 1.0 | mushroom cap shape | 516.0 |
| separated from | 2.0 | separated from | 2.0 |
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| academic minor | 1.0 | academic minor | 1.0 |
| instrumentation | 13.0 | instrumentation | 18.0 |
| contributing factor of | 1.0 | contributing factor of | 1.0 |
| political alignment | 2.0 | political alignment | 8.0 |
| has pet | 1.0 | has pet | 2.0 |
| minor planet group | 2.0 | minor planet group | 39042.0 |
| stipe character | 1.0 | stipe character | 450.0 |
| voice type | 4.0 | voice type | 2719.0 |
| catalog | 2.0 | catalog | 50.0 |
| USK rating | 4.0 | USK rating | 473.0 |
| penalty | 1.0 | penalty | 112.0 |
| military casualty classification | 1.0 | military casualty classification | 5.0 |
| symptoms and signs | 8.0 | symptoms and signs | 6.0 |
| depends on software | 1.0 | depends on software | 2.0 |
| located on astronomical body | 1.0 | located on astronomical body | 40.0 |
| canonization status | 4.0 | canonization status | 1925.0 |
| general manager | 2.0 | general manager | 1.0 |
| chivalric order | 1.0 | chivalric order | 1.0 |
| owner of | 4.0 | owner of | 2.0 |
| carries scientific instrument | 1.0 | carries scientific instrument | 1.0 |
| list of works | 3.0 | list of works | 1.0 |
| captain | 2.0 | captain | 3.0 |
| speaker | 2.0 | speaker | 1.0 |
| proxy | 7.0 | proxy | 1.0 |
| possible treatment | 2.0 | possible treatment | 1.0 |
| natural reservoir of | 1.0 | natural reservoir of | 1.0 |
| approved by | 3.0 | approved by | 5.0 |
| fuel system | 1.0 | fuel system | 1.0 |
| located on linear feature | 1.0 | located on linear feature | 1.0 |
| input set | 1.0 | input set | 3.0 |
| undercarriage | 1.0 | undercarriage | 27.0 |
| type of variable star | 2.0 | type of variable star | 2.0 |
| dan/kyu rank | 1.0 | dan/kyu rank | 5.0 |
| edibility | 1.0 | edibility | 185.0 |
| lowest point | 1.0 | lowest point | 1.0 |
| Code of nomenclature | 1.0 | Code of nomenclature | 737.0 |
| choreographer | 1.0 | choreographer | 1.0 |
| has natural reservoir | 1.0 | has natural reservoir | 1.0 |
| target | 3.0 | target | 2.0 |
| addressee | 2.0 | addressee | 1.0 |
| unveiled by | 2.0 | unveiled by | 1.0 |
| NATO code for grade | 2.0 | NATO code for grade | 3.0 |
| is pollinator of | 1.0 | is pollinator of | 1.0 |
| honorific suffix | 1.0 | honorific suffix | 1.0 |
| after a work by | 1.0 | after a work by | 1.0 |
| airline alliance | 2.0 | airline alliance | 59.0 |
| handedness | 3.0 | handedness | 444.0 |
| introduced feature | 1.0 | introduced feature | 1.0 |
| used by | 2.0 | used by | 2.0 |
| immediate cause of | 1.0 | immediate cause of | 1.0 |
| Digital Rights Management system | 1.0 | Digital Rights Management system | 13.0 |
| GHS signal word | 1.0 | GHS signal word | 1.0 |
| PEGI rating | 4.0 | PEGI rating | 779.0 |
| list of characters | 1.0 | list of characters | 1.0 |
| proved by | 2.0 | proved by | 2.0 |
| script directionality | 1.0 | script directionality | 1.0 |
| workshop of | 1.0 | workshop of | 1.0 |
| is pollinated by | 1.0 | is pollinated by | 1.0 |
| Lagrangian point | 1.0 | Lagrangian point | 7.0 |
| fossil found in this unit | 2.0 | fossil found in this unit | 2.0 |
| measurement scale | 3.0 | measurement scale | 4.0 |
| coolant | 1.0 | coolant | 138.0 |
| electoral district | 1.0 | electoral district | 2.0 |
| curator | 1.0 | curator | 2.0 |
| GSRR rating | 1.0 | GSRR rating | 3.0 |
| vice-county | 1.0 | vice-county | 1.0 |
| crystal habit | 1.0 | crystal habit | 1.0 |
| surface played on | 3.0 | surface played on | 188.0 |
| foods traditionally associated | 2.0 | foods traditionally associated | 1.0 |
| tempo marking | 1.0 | tempo marking | 6.0 |
| type of orbit | 1.0 | type of orbit | 269.0 |
| mushroom ecological type | 2.0 | mushroom ecological type | 587.0 |
| launch contractor | 1.0 | launch contractor | 2.0 |
| motto | 1.0 | motto | 2.0 |
| hymenium attachment | 2.0 | hymenium attachment | 322.0 |
| mineral fracture | 1.0 | mineral fracture | 5.0 |
| end cause | 1.0 | end cause | 1.0 |
| guest of honor | 2.0 | guest of honor | 1.0 |
| defender | 1.0 | defender | 1.0 |
| authority | 1.0 | authority | 1.0 |
| product certification | 2.0 | product certification | 47.0 |
| determination method | 1.0 | determination method | 3.0 |
| template has topic | 1.0 | template has topic | 1.0 |
| judge | 1.0 | judge | 1.0 |
| cleavage | 1.0 | cleavage | 50.0 |
| presynaptic connection | 1.0 | presynaptic connection | 1.0 |
| binding of software library | 1.0 | binding of software library | 1.0 |
| EC enzyme classification | 1.0 | EC enzyme classification | 1.0 |
| direction | 1.0 | direction | 1.0 |
| type of electrification | 1.0 | type of electrification | 3.0 |
| driving side | 1.0 | driving side | 4.0 |
| disease transmission process | 1.0 | disease transmission process | 1.0 |
| has seal, badge, or sigil | 1.0 | has seal, badge, or sigil | 2.0 |
| plaintiff | 1.0 | plaintiff | 1.0 |
mergedDf2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
list2: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel)
mergedDF2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
rel_name_df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list3: List[String] = List(relid, label, description)
relnamedf: org.apache.spark.sql.DataFrame = [relid: string, label: string ... 1 more field]
finalDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 7 more fields]
list4: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel, relid, rellabel)
finalDF: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
edgesDF_: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
list5: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
verticesDf: org.apache.spark.sql.DataFrame = [id: string]
graph: org.graphframes.GraphFrame = GraphFrame(v:[id: string], e:[src: string, dst: string ... 1 more field])
import org.apache.spark.sql.functions.udf
// Here we define a function to perform the final classification into our 4 groups
def decide_reltype(t1 : Long, t2 : Long): String = {
if (t1 == 1 && t2 > 1) {
return "1-M"
} else if (t1 > 1 && t2 > 1) {
return "M-M"
} else if (t1 == 1 && t2 == 1) {
return "1-1"
} else {
return "M-1"
}
}
// Apply function to dataframe
val rel_type = udf(decide_reltype _)
val class_df =rel_count_src_dst.withColumn("relationType", rel_type($"src_count", $"dst_count")).cache()
import org.apache.spark.sql.functions.udf
decide_reltype: (t1: Long, t2: Long)String
rel_type: org.apache.spark.sql.expressions.UserDefinedFunction = SparkUserDefinedFunction($Lambda$9916/1066546511@2e14664a,StringType,List(Some(class[value[0]: bigint]), Some(class[value[0]: bigint])),Some(class[value[0]: string]),None,true,true)
class_df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [rel: string, src_count: bigint ... 3 more fields]
display(class_df)
| rel | src_count | rel | dst_count | relationType |
|---|---|---|---|---|
| godparent | 2.0 | godparent | 1.0 | M-1 |
| interaction | 4.0 | interaction | 17.0 | M-M |
| disease transmission process | 1.0 | disease transmission process | 1.0 | 1-1 |
| molecular function | 47.0 | molecular function | 947.0 | M-M |
| part of | 33.0 | part of | 1461.0 | M-M |
| IUCN protected areas category | 3.0 | IUCN protected areas category | 508.0 | M-M |
| place served by transport hub | 2.0 | place served by transport hub | 3.0 | M-M |
| playing hand | 3.0 | playing hand | 1318.0 | M-M |
| family name | 104.0 | family name | 4629.0 | M-M |
| general manager | 2.0 | general manager | 1.0 | M-1 |
| parent astronomical body | 2.0 | parent astronomical body | 50.0 | M-M |
| topic's main Wikimedia portal | 2.0 | topic's main Wikimedia portal | 6.0 | M-M |
| end cause | 1.0 | end cause | 1.0 | 1-1 |
| mushroom cap shape | 1.0 | mushroom cap shape | 516.0 | 1-M |
| shares border with | 76.0 | shares border with | 76.0 | M-M |
| based on | 27.0 | based on | 150.0 | M-M |
| filmography | 12.0 | filmography | 2.0 | M-M |
| present in work | 46.0 | present in work | 441.0 | M-M |
| public holiday | 15.0 | public holiday | 25.0 | M-M |
| separated from | 2.0 | separated from | 2.0 | M-M |
| honorific suffix | 1.0 | honorific suffix | 1.0 | 1-1 |
| represented by | 2.0 | represented by | 8.0 | M-M |
| standards body | 2.0 | standards body | 11.0 | M-M |
| guest of honor | 2.0 | guest of honor | 1.0 | M-1 |
| track gauge | 3.0 | track gauge | 640.0 | M-M |
| sexual orientation | 2.0 | sexual orientation | 2902.0 | M-M |
| topic's main category | 37.0 | topic's main category | 40.0 | M-M |
| writing system | 4.0 | writing system | 256.0 | M-M |
| academic minor | 1.0 | academic minor | 1.0 | 1-1 |
| father | 13.0 | father | 69.0 | M-M |
| given name version for other gender | 3.0 | given name version for other gender | 3.0 | M-M |
| industry | 13.0 | industry | 1370.0 | M-M |
| applies to jurisdiction | 28.0 | applies to jurisdiction | 54.0 | M-M |
| crystal system | 2.0 | crystal system | 182.0 | M-M |
| worshipped by | 3.0 | worshipped by | 192.0 | M-M |
| business division | 37.0 | business division | 4.0 | M-M |
| performer | 60.0 | performer | 140.0 | M-M |
| discovery method | 1.0 | discovery method | 8.0 | 1-M |
| influenced by | 10.0 | influenced by | 7.0 | M-M |
| place of burial | 5.0 | place of burial | 3043.0 | M-M |
| this taxon is source of | 2.0 | this taxon is source of | 4.0 | M-M |
| PEGI rating | 4.0 | PEGI rating | 779.0 | M-M |
| developer | 9.0 | developer | 499.0 | M-M |
| fictional universe described in | 11.0 | fictional universe described in | 4.0 | M-M |
| head of state | 23.0 | head of state | 42.0 | M-M |
| notation | 1.0 | notation | 1.0 | 1-1 |
| ESRB rating | 3.0 | ESRB rating | 1086.0 | M-M |
| binding of software library | 1.0 | binding of software library | 1.0 | 1-1 |
| currency | 23.0 | currency | 67.0 | M-M |
| depicts | 692.0 | depicts | 3303.0 | M-M |
| crystal habit | 1.0 | crystal habit | 1.0 | 1-1 |
| replaced synonym (for nom. nov.) | 1.0 | replaced synonym (for nom. nov.) | 1.0 | 1-1 |
| armament | 14.0 | armament | 162.0 | M-M |
| basic form of government | 6.0 | basic form of government | 61.0 | M-M |
| fictional or mythical analog of | 2.0 | fictional or mythical analog of | 2.0 | M-M |
| electoral district | 1.0 | electoral district | 2.0 | 1-M |
| producer | 17.0 | producer | 397.0 | M-M |
| shape | 1.0 | shape | 28.0 | 1-M |
| highest judicial authority | 2.0 | highest judicial authority | 2.0 | M-M |
| taxon synonym | 16.0 | taxon synonym | 2.0 | M-M |
| located in or next to body of water | 5.0 | located in or next to body of water | 38.0 | M-M |
| replaced by | 10.0 | replaced by | 9.0 | M-M |
| Eight Banner register | 2.0 | Eight Banner register | 55.0 | M-M |
| after a work by | 1.0 | after a work by | 1.0 | 1-1 |
| part of the series | 55.0 | part of the series | 577.0 | M-M |
| interleaves with | 3.0 | interleaves with | 3.0 | M-M |
| measured physical quantity | 2.0 | measured physical quantity | 3.0 | M-M |
| lake on watercourse | 6.0 | lake on watercourse | 2.0 | M-M |
| narrative location | 11.0 | narrative location | 1904.0 | M-M |
| participant | 106.0 | participant | 36.0 | M-M |
| recorded at studio or venue | 7.0 | recorded at studio or venue | 21.0 | M-M |
| place of origin (Switzerland) | 4.0 | place of origin (Switzerland) | 75.0 | M-M |
| transport network | 16.0 | transport network | 844.0 | M-M |
| capital of | 6.0 | capital of | 2.0 | M-M |
| list related to category | 1.0 | list related to category | 1.0 | 1-1 |
| official language | 24.0 | official language | 1327.0 | M-M |
| airline alliance | 2.0 | airline alliance | 59.0 | M-M |
| avionics | 5.0 | avionics | 6.0 | M-M |
| location of landing | 1.0 | location of landing | 3.0 | 1-M |
| characters | 53.0 | characters | 71.0 | M-M |
| collection | 80.0 | collection | 5290.0 | M-M |
| donated by | 3.0 | donated by | 3.0 | M-M |
| chivalric order | 1.0 | chivalric order | 1.0 | 1-1 |
| executive producer | 10.0 | executive producer | 28.0 | M-M |
| film editor | 8.0 | film editor | 29.0 | M-M |
| owner of | 4.0 | owner of | 2.0 | M-M |
| presynaptic connection | 1.0 | presynaptic connection | 1.0 | 1-1 |
| structure replaced by | 2.0 | structure replaced by | 2.0 | M-M |
| has part(s) | 103.0 | has part(s) | 432.0 | M-M |
| employer | 11.0 | employer | 3198.0 | M-M |
| hair color | 1.0 | hair color | 138.0 | 1-M |
| located in the administrative territorial entity | 258.0 | located in the administrative territorial entity | 7237.0 | M-M |
| sponsor | 7.0 | sponsor | 2.0 | M-M |
| cathedral | 2.0 | cathedral | 2.0 | M-M |
| chairperson | 20.0 | chairperson | 31.0 | M-M |
| has seal, badge, or sigil | 1.0 | has seal, badge, or sigil | 2.0 | 1-M |
| lyrics by | 6.0 | lyrics by | 174.0 | M-M |
| place of birth | 25.0 | place of birth | 14404.0 | M-M |
| exclave of | 3.0 | exclave of | 3.0 | M-M |
| instance of | 439.0 | instance of | 1510474.0 | M-M |
| located on street | 47.0 | located on street | 726.0 | M-M |
| points/goal scored by | 6.0 | points/goal scored by | 2.0 | M-M |
| structural engineer | 2.0 | structural engineer | 53.0 | M-M |
| subclass of | 12.0 | subclass of | 1930.0 | M-M |
| named after | 258.0 | named after | 545.0 | M-M |
| maintained by | 10.0 | maintained by | 845.0 | M-M |
| mother house | 4.0 | mother house | 14.0 | M-M |
| officially opened by | 3.0 | officially opened by | 5.0 | M-M |
| country of origin | 19.0 | country of origin | 27099.0 | M-M |
| rector | 119.0 | rector | 2.0 | M-M |
| carries scientific instrument | 1.0 | carries scientific instrument | 1.0 | 1-1 |
| medical condition | 5.0 | medical condition | 918.0 | M-M |
| CPU | 2.0 | CPU | 119.0 | M-M |
| original combination | 1.0 | original combination | 1.0 | 1-1 |
| airline hub | 11.0 | airline hub | 10.0 | M-M |
| has facet polytope | 4.0 | has facet polytope | 208.0 | M-M |
| consecrator | 3.0 | consecrator | 6.0 | M-M |
| instrumentation | 13.0 | instrumentation | 18.0 | M-M |
| licensed to broadcast to | 1.0 | licensed to broadcast to | 3.0 | 1-M |
| prosecutor | 3.0 | prosecutor | 1.0 | M-1 |
| site of astronomical discovery | 2.0 | site of astronomical discovery | 12986.0 | M-M |
| category related to list | 1.0 | category related to list | 1.0 | 1-1 |
| handedness | 3.0 | handedness | 444.0 | M-M |
| has vertex figure | 1.0 | has vertex figure | 1.0 | 1-1 |
| Code of nomenclature | 1.0 | Code of nomenclature | 737.0 | 1-M |
| list of characters | 1.0 | list of characters | 1.0 | 1-1 |
| medical examination | 5.0 | medical examination | 7.0 | M-M |
| allegiance | 3.0 | allegiance | 27.0 | M-M |
| composer | 6.0 | composer | 202.0 | M-M |
| encoded by | 2.0 | encoded by | 2.0 | M-M |
| main building contractor | 2.0 | main building contractor | 61.0 | M-M |
| organizer | 3.0 | organizer | 66.0 | M-M |
| translator | 3.0 | translator | 19.0 | M-M |
| contributing factor of | 1.0 | contributing factor of | 1.0 | 1-1 |
| occupant | 10.0 | occupant | 102.0 | M-M |
| represents | 1.0 | represents | 1.0 | 1-1 |
| place of death | 15.0 | place of death | 17262.0 | M-M |
| political alignment | 2.0 | political alignment | 8.0 | M-M |
| programmer | 2.0 | programmer | 17.0 | M-M |
| solved by | 2.0 | solved by | 1.0 | M-1 |
| legislated by | 1.0 | legislated by | 25.0 | 1-M |
| physically interacts with | 2.0 | physically interacts with | 1.0 | M-1 |
| relative | 9.0 | relative | 6.0 | M-M |
| director | 51.0 | director | 543.0 | M-M |
| member of sports team | 55.0 | member of sports team | 1795.0 | M-M |
| category of associated people | 2.0 | category of associated people | 1.0 | M-1 |
| category's main topic | 3.0 | category's main topic | 4.0 | M-M |
| introduced feature | 1.0 | introduced feature | 1.0 | 1-1 |
| author | 85.0 | author | 1462.0 | M-M |
| basin country | 22.0 | basin country | 12.0 | M-M |
| position played on team / speciality | 7.0 | position played on team / speciality | 1682.0 | M-M |
| sex or gender | 106.0 | sex or gender | 1277035.0 | M-M |
| spouse | 22.0 | spouse | 22.0 | M-M |
| codomain | 1.0 | codomain | 4.0 | 1-M |
| choreographer | 1.0 | choreographer | 1.0 | 1-1 |
| foundational text | 3.0 | foundational text | 6.0 | M-M |
| located in/on physical feature | 11.0 | located in/on physical feature | 321.0 | M-M |
| director of photography | 23.0 | director of photography | 423.0 | M-M |
| language of work or name | 35.0 | language of work or name | 1722.0 | M-M |
| powered by | 4.0 | powered by | 184.0 | M-M |
| patron saint | 5.0 | patron saint | 54.0 | M-M |
| list of works | 3.0 | list of works | 1.0 | M-1 |
| pendant of | 8.0 | pendant of | 8.0 | M-M |
| record label | 40.0 | record label | 3454.0 | M-M |
| from narrative universe | 3.0 | from narrative universe | 982.0 | M-M |
| proved by | 2.0 | proved by | 2.0 | M-M |
| diocese | 5.0 | diocese | 122.0 | M-M |
| position held | 33.0 | position held | 13796.0 | M-M |
| docking port | 1.0 | docking port | 1.0 | 1-1 |
| endemic to | 4.0 | endemic to | 44.0 | M-M |
| home port | 2.0 | home port | 6.0 | M-M |
| lifestyle | 2.0 | lifestyle | 326.0 | M-M |
| ortholog | 2.0 | ortholog | 2.0 | M-M |
| category combines topics | 4.0 | category combines topics | 8018.0 | M-M |
| day in year for periodic occurrence | 14.0 | day in year for periodic occurrence | 4.0 | M-M |
| conferred by | 3.0 | conferred by | 13.0 | M-M |
| postsynaptic connection | 1.0 | postsynaptic connection | 1.0 | 1-1 |
| cover art by | 2.0 | cover art by | 10.0 | M-M |
| has pet | 1.0 | has pet | 2.0 | 1-M |
| archives at | 4.0 | archives at | 87.0 | M-M |
| game mode | 9.0 | game mode | 15220.0 | M-M |
| diplomatic relation | 133.0 | diplomatic relation | 94.0 | M-M |
| IUCN conservation status | 1.0 | IUCN conservation status | 1963.0 | 1-M |
| found in taxon | 4.0 | found in taxon | 1985.0 | M-M |
| office held by head of government | 12.0 | office held by head of government | 1594.0 | M-M |
| Wikimedia portal's main topic | 6.0 | Wikimedia portal's main topic | 2.0 | M-M |
| has contributing factor | 3.0 | has contributing factor | 1.0 | M-1 |
| native language | 3.0 | native language | 1340.0 | M-M |
| sport | 18.0 | sport | 36409.0 | M-M |
| child astronomical body | 49.0 | child astronomical body | 2.0 | M-M |
| country of citizenship | 80.0 | country of citizenship | 215882.0 | M-M |
| family | 10.0 | family | 168.0 | M-M |
| adjacent station | 12.0 | adjacent station | 11.0 | M-M |
| companion of | 2.0 | companion of | 2.0 | M-M |
| location of creation | 7.0 | location of creation | 116.0 | M-M |
| mother | 6.0 | mother | 60.0 | M-M |
| central bank/issuer | 2.0 | central bank/issuer | 2.0 | M-M |
| imported from Wikimedia project | 16.0 | imported from Wikimedia project | 249.0 | M-M |
| noble title | 3.0 | noble title | 2956.0 | M-M |
| has facility | 7.0 | has facility | 15.0 | M-M |
| replaces | 9.0 | replaces | 5.0 | M-M |
| Unknown | 269.0 | Unknown | 810.0 | M-M |
| cause of death | 5.0 | cause of death | 5153.0 | M-M |
| inflows | 20.0 | inflows | 6.0 | M-M |
| inspired by | 5.0 | inspired by | 10.0 | M-M |
| designed by | 6.0 | designed by | 27.0 | M-M |
| location of formation | 2.0 | location of formation | 21.0 | M-M |
| readable file format | 4.0 | readable file format | 4.0 | M-M |
| student of | 9.0 | student of | 80.0 | M-M |
| commissioned by | 3.0 | commissioned by | 14.0 | M-M |
| has natural reservoir | 1.0 | has natural reservoir | 1.0 | 1-1 |
| coat of arms | 2.0 | coat of arms | 8.0 | M-M |
| has immediate cause | 2.0 | has immediate cause | 7.0 | M-M |
| overlies | 2.0 | overlies | 3.0 | M-M |
| religion or worldview | 9.0 | religion or worldview | 16455.0 | M-M |
| target | 3.0 | target | 2.0 | M-M |
| ethnic group | 3.0 | ethnic group | 2853.0 | M-M |
| input device | 5.0 | input device | 1881.0 | M-M |
| programmed in | 6.0 | programmed in | 194.0 | M-M |
| statement is subject of | 6.0 | statement is subject of | 16.0 | M-M |
| captain | 2.0 | captain | 3.0 | M-M |
| country for sport | 2.0 | country for sport | 6.0 | M-M |
| list of episodes | 1.0 | list of episodes | 2.0 | 1-M |
| origin of the watercourse | 2.0 | origin of the watercourse | 9.0 | M-M |
| used by | 2.0 | used by | 2.0 | M-M |
| main regulatory text | 3.0 | main regulatory text | 82.0 | M-M |
| voice actor | 20.0 | voice actor | 70.0 | M-M |
| academic degree | 4.0 | academic degree | 9389.0 | M-M |
| capital | 7.0 | capital | 28.0 | M-M |
| minor planet group | 2.0 | minor planet group | 39042.0 | M-M |
| source of energy | 6.0 | source of energy | 16.0 | M-M |
| contains settlement | 52.0 | contains settlement | 7.0 | M-M |
| founded by | 16.0 | founded by | 10.0 | M-M |
| surface played on | 3.0 | surface played on | 188.0 | M-M |
| librettist | 5.0 | librettist | 12.0 | M-M |
| member of | 48.0 | member of | 5863.0 | M-M |
| activating neurotransmitter | 1.0 | activating neurotransmitter | 2.0 | 1-M |
| tracklist | 27.0 | tracklist | 3.0 | M-M |
| instruction set | 2.0 | instruction set | 8.0 | M-M |
| review score by | 1.0 | review score by | 1.0 | 1-1 |
| constellation | 5.0 | constellation | 22.0 | M-M |
| continent | 5.0 | continent | 1809.0 | M-M |
| has quality | 6.0 | has quality | 4.0 | M-M |
| lake outflow | 2.0 | lake outflow | 6.0 | M-M |
| notable work | 49.0 | notable work | 38.0 | M-M |
| official residence | 2.0 | official residence | 18.0 | M-M |
| taxon rank | 3.0 | taxon rank | 99472.0 | M-M |
| tributary | 19.0 | tributary | 2.0 | M-M |
| follows | 52.0 | follows | 50.0 | M-M |
| script directionality | 1.0 | script directionality | 1.0 | 1-1 |
| valid in period | 5.0 | valid in period | 2.0 | M-M |
| feast day | 3.0 | feast day | 6.0 | M-M |
| stipe character | 1.0 | stipe character | 450.0 | 1-M |
| discoverer or inventor | 5.0 | discoverer or inventor | 12992.0 | M-M |
| distribution format | 7.0 | distribution format | 1743.0 | M-M |
| head coach | 38.0 | head coach | 6.0 | M-M |
| movement | 12.0 | movement | 1542.0 | M-M |
| terminus location | 4.0 | terminus location | 11.0 | M-M |
| applies to part | 2.0 | applies to part | 1.0 | M-1 |
| office contested | 1.0 | office contested | 6.0 | 1-M |
| said to be the same as | 31.0 | said to be the same as | 31.0 | M-M |
| charge | 2.0 | charge | 1.0 | M-1 |
| manifestation of | 1.0 | manifestation of | 2.0 | 1-M |
| owned by | 18.0 | owned by | 845.0 | M-M |
| terminus | 11.0 | terminus | 35.0 | M-M |
| definition domain | 1.0 | definition domain | 1.0 | 1-1 |
| edition or translation of | 15.0 | edition or translation of | 12.0 | M-M |
| speaker | 2.0 | speaker | 1.0 | M-1 |
| field of work | 29.0 | field of work | 619.0 | M-M |
| launch contractor | 1.0 | launch contractor | 2.0 | 1-M |
| mouth of the watercourse | 6.0 | mouth of the watercourse | 72.0 | M-M |
| Fach | 3.0 | Fach | 111.0 | M-M |
| architectural style | 26.0 | architectural style | 706.0 | M-M |
| central bank | 1.0 | central bank | 1.0 | 1-1 |
| plaintiff | 1.0 | plaintiff | 1.0 | 1-1 |
| proxy | 7.0 | proxy | 1.0 | M-1 |
| student | 14.0 | student | 5.0 | M-M |
| addressee | 2.0 | addressee | 1.0 | M-1 |
| possible treatment | 2.0 | possible treatment | 1.0 | M-1 |
| scheduled service destination | 51.0 | scheduled service destination | 3.0 | M-M |
| cast member | 282.0 | cast member | 347.0 | M-M |
| chief operating officer | 1.0 | chief operating officer | 1.0 | 1-1 |
| described by source | 13.0 | described by source | 9578.0 | M-M |
| educated at | 18.0 | educated at | 11262.0 | M-M |
| immediate cause of | 1.0 | immediate cause of | 1.0 | 1-1 |
| ancestral home | 2.0 | ancestral home | 6.0 | M-M |
| curator | 1.0 | curator | 2.0 | 1-M |
| determination method | 1.0 | determination method | 3.0 | 1-M |
| workshop of | 1.0 | workshop of | 1.0 | 1-1 |
| connecting line | 14.0 | connecting line | 144.0 | M-M |
| decays to | 10.0 | decays to | 12.0 | M-M |
| home venue | 6.0 | home venue | 12.0 | M-M |
| item operated | 51.0 | item operated | 102.0 | M-M |
| space tug | 1.0 | space tug | 4.0 | 1-M |
| category for people who died here | 4.0 | category for people who died here | 2.0 | M-M |
| natural reservoir of | 1.0 | natural reservoir of | 1.0 | 1-1 |
| approved by | 3.0 | approved by | 5.0 | M-M |
| candidate | 11.0 | candidate | 4.0 | M-M |
| fuel system | 1.0 | fuel system | 1.0 | 1-1 |
| software engine | 6.0 | software engine | 251.0 | M-M |
| soundtrack release | 8.0 | soundtrack release | 2.0 | M-M |
| voice type | 4.0 | voice type | 2719.0 | M-M |
| commemorates | 2.0 | commemorates | 3.0 | M-M |
| copyright license | 8.0 | copyright license | 1462.0 | M-M |
| depicted by | 3.0 | depicted by | 3.0 | M-M |
| catalog | 2.0 | catalog | 50.0 | M-M |
| illustrator | 5.0 | illustrator | 57.0 | M-M |
| kinship to subject | 1.0 | kinship to subject | 1.0 | 1-1 |
| architect | 13.0 | architect | 155.0 | M-M |
| enclave within | 3.0 | enclave within | 3.0 | M-M |
| has use | 7.0 | has use | 412.0 | M-M |
| defendant | 1.0 | defendant | 1.0 | 1-1 |
| has subsidiary | 9.0 | has subsidiary | 2.0 | M-M |
| honorific prefix | 3.0 | honorific prefix | 52.0 | M-M |
| parent taxon | 7.0 | parent taxon | 3135.0 | M-M |
| residence | 9.0 | residence | 98.0 | M-M |
| IMA status and/or rank | 3.0 | IMA status and/or rank | 294.0 | M-M |
| brand | 1.0 | brand | 22.0 | 1-M |
| country | 258.0 | country | 84551.0 | M-M |
| crosses | 3.0 | crosses | 82.0 | M-M |
| shooting handedness | 1.0 | shooting handedness | 4.0 | 1-M |
| editor | 18.0 | editor | 9.0 | M-M |
| headquarters location | 13.0 | headquarters location | 171.0 | M-M |
| torch lit by | 6.0 | torch lit by | 1.0 | M-1 |
| CERO rating | 2.0 | CERO rating | 659.0 | M-M |
| distributed by | 4.0 | distributed by | 91.0 | M-M |
| has edition or translation | 12.0 | has edition or translation | 166.0 | M-M |
| home world | 2.0 | home world | 5.0 | M-M |
| category for films shot at this location | 2.0 | category for films shot at this location | 1.0 | M-1 |
| color | 11.0 | color | 104.0 | M-M |
| league | 8.0 | league | 107.0 | M-M |
| encodes | 2.0 | encodes | 2.0 | M-M |
| original language of film or TV show | 22.0 | original language of film or TV show | 53297.0 | M-M |
| doctoral advisor | 4.0 | doctoral advisor | 14.0 | M-M |
| spore print color | 1.0 | spore print color | 280.0 | 1-M |
| located on linear feature | 1.0 | located on linear feature | 1.0 | 1-1 |
| top-level Internet domain | 3.0 | top-level Internet domain | 6.0 | M-M |
| mascot | 1.0 | mascot | 2.0 | 1-M |
| defender | 1.0 | defender | 1.0 | 1-1 |
| discography | 1.0 | discography | 1.0 | 1-1 |
| USK rating | 4.0 | USK rating | 473.0 | M-M |
| dedicated to | 4.0 | dedicated to | 17.0 | M-M |
| GSRR rating | 1.0 | GSRR rating | 3.0 | 1-M |
| award received | 34.0 | award received | 15971.0 | M-M |
| penalty | 1.0 | penalty | 112.0 | 1-M |
| NATO code for grade | 2.0 | NATO code for grade | 3.0 | M-M |
| opposite of | 3.0 | opposite of | 3.0 | M-M |
| radio format | 1.0 | radio format | 1.0 | 1-1 |
| topic's main template | 1.0 | topic's main template | 1.0 | 1-1 |
| unveiled by | 2.0 | unveiled by | 1.0 | M-1 |
| afflicts | 7.0 | afflicts | 5.0 | M-M |
| filming location | 14.0 | filming location | 1040.0 | M-M |
| port of registry | 3.0 | port of registry | 7.0 | M-M |
| template has topic | 1.0 | template has topic | 1.0 | 1-1 |
| military rank | 10.0 | military rank | 1834.0 | M-M |
| territory claimed by | 4.0 | territory claimed by | 6.0 | M-M |
| flag | 1.0 | flag | 8.0 | 1-M |
| partially coincident with | 21.0 | partially coincident with | 24.0 | M-M |
| point group | 2.0 | point group | 12.0 | M-M |
| destination point | 2.0 | destination point | 17.0 | M-M |
| winner | 18.0 | winner | 26.0 | M-M |
| stock exchange | 5.0 | stock exchange | 407.0 | M-M |
| child | 69.0 | child | 14.0 | M-M |
| engine configuration | 1.0 | engine configuration | 79.0 | 1-M |
| parent of this hybrid, breed, or cultivar | 2.0 | parent of this hybrid, breed, or cultivar | 1.0 | M-1 |
| convicted of | 4.0 | convicted of | 1460.0 | M-M |
| space group | 5.0 | space group | 89.0 | M-M |
| category for people born here | 3.0 | category for people born here | 2.0 | M-M |
| space launch vehicle | 1.0 | space launch vehicle | 45.0 | 1-M |
| stated in | 6.0 | stated in | 3.0 | M-M |
| diplomatic mission sent | 2.0 | diplomatic mission sent | 92.0 | M-M |
| oath made by | 3.0 | oath made by | 1.0 | M-1 |
| referee | 7.0 | referee | 4.0 | M-M |
| tonality | 2.0 | tonality | 18.0 | M-M |
| location | 256.0 | location | 5283.0 | M-M |
| is pollinated by | 1.0 | is pollinated by | 1.0 | 1-1 |
| eye color | 2.0 | eye color | 223.0 | M-M |
| foods traditionally associated | 2.0 | foods traditionally associated | 1.0 | M-1 |
| is pollinator of | 1.0 | is pollinator of | 1.0 | 1-1 |
| of | 1.0 | of | 2.0 | 1-M |
| guidance system | 2.0 | guidance system | 35.0 | M-M |
| vice-county | 1.0 | vice-county | 1.0 | 1-1 |
| GUI toolkit or framework | 4.0 | GUI toolkit or framework | 35.0 | M-M |
| family name identical to this given name | 3.0 | family name identical to this given name | 3.0 | M-M |
| natural product of taxon | 4.0 | natural product of taxon | 2.0 | M-M |
| production company | 9.0 | production company | 1511.0 | M-M |
| twinning | 2.0 | twinning | 1.0 | M-1 |
| party chief representative | 5.0 | party chief representative | 2.0 | M-M |
| military casualty classification | 1.0 | military casualty classification | 5.0 | 1-M |
| motto | 1.0 | motto | 2.0 | 1-M |
| hymenium attachment | 2.0 | hymenium attachment | 322.0 | M-M |
| member of political party | 11.0 | member of political party | 21072.0 | M-M |
| connecting service | 13.0 | connecting service | 182.0 | M-M |
| tempo marking | 1.0 | tempo marking | 6.0 | 1-M |
| symptoms and signs | 8.0 | symptoms and signs | 6.0 | M-M |
| ammunition | 9.0 | ammunition | 49.0 | M-M |
| language regulatory body | 3.0 | language regulatory body | 3.0 | M-M |
| vessel class | 4.0 | vessel class | 61.0 | M-M |
| depends on software | 1.0 | depends on software | 2.0 | 1-M |
| input set | 1.0 | input set | 3.0 | 1-M |
| undercarriage | 1.0 | undercarriage | 27.0 | 1-M |
| judge | 1.0 | judge | 1.0 | 1-1 |
| military branch | 10.0 | military branch | 10147.0 | M-M |
| taxonomic type | 1.0 | taxonomic type | 3.0 | 1-M |
| including | 4.0 | including | 1.0 | M-1 |
| primary destinations | 16.0 | primary destinations | 4.0 | M-M |
| head of government | 90.0 | head of government | 9.0 | M-M |
| type of orbit | 1.0 | type of orbit | 269.0 | 1-M |
| blood type | 1.0 | blood type | 9.0 | 1-M |
| cause of destruction | 2.0 | cause of destruction | 10.0 | M-M |
| given name | 106.0 | given name | 26120.0 | M-M |
| located in time zone | 13.0 | located in time zone | 25664.0 | M-M |
| officeholder | 12.0 | officeholder | 2.0 | M-M |
| structure replaces | 1.0 | structure replaces | 1.0 | 1-1 |
| list of monuments | 7.0 | list of monuments | 2.0 | M-M |
| participant in | 36.0 | participant in | 9207.0 | M-M |
| work location | 14.0 | work location | 3944.0 | M-M |
| vehicle | 2.0 | vehicle | 42.0 | M-M |
| dual to | 2.0 | dual to | 2.0 | M-M |
| executive body | 2.0 | executive body | 12.0 | M-M |
| astronaut mission | 8.0 | astronaut mission | 3.0 | M-M |
| direction | 1.0 | direction | 1.0 | 1-1 |
| legal form | 3.0 | legal form | 85.0 | M-M |
| located on astronomical body | 1.0 | located on astronomical body | 40.0 | 1-M |
| religious order | 4.0 | religious order | 1168.0 | M-M |
| mineral fracture | 1.0 | mineral fracture | 5.0 | 1-M |
| name day | 3.0 | name day | 5.0 | M-M |
| doctoral student | 16.0 | doctoral student | 2.0 | M-M |
| made from material | 134.0 | made from material | 19846.0 | M-M |
| appointed by | 2.0 | appointed by | 29.0 | M-M |
| heritage designation | 60.0 | heritage designation | 53050.0 | M-M |
| unmarried partner | 8.0 | unmarried partner | 8.0 | M-M |
| exhibition history | 28.0 | exhibition history | 37.0 | M-M |
| product or material produced | 21.0 | product or material produced | 17.0 | M-M |
| academic thesis | 2.0 | academic thesis | 1.0 | M-1 |
| instrument | 19.0 | instrument | 9593.0 | M-M |
| is a list of | 5.0 | is a list of | 8357.0 | M-M |
| languages spoken, written or signed | 10.0 | languages spoken, written or signed | 31379.0 | M-M |
| operating system | 13.0 | operating system | 204.0 | M-M |
| place of publication | 9.0 | place of publication | 126.0 | M-M |
| platform | 30.0 | platform | 5894.0 | M-M |
| professorship | 3.0 | professorship | 21.0 | M-M |
| type of electrification | 1.0 | type of electrification | 3.0 | 1-M |
| genre | 216.0 | genre | 9431.0 | M-M |
| affiliation | 3.0 | affiliation | 47.0 | M-M |
| anthem | 2.0 | anthem | 52.0 | M-M |
| biological process | 205.0 | biological process | 257.0 | M-M |
| cleavage | 1.0 | cleavage | 50.0 | 1-M |
| manner of death | 3.0 | manner of death | 2643.0 | M-M |
| route of administration | 4.0 | route of administration | 18.0 | M-M |
| academic major | 2.0 | academic major | 4.0 | M-M |
| significant event | 11.0 | significant event | 815.0 | M-M |
| asteroid spectral type | 2.0 | asteroid spectral type | 143.0 | M-M |
| cell component | 38.0 | cell component | 649.0 | M-M |
| contains the administrative territorial entity | 902.0 | contains the administrative territorial entity | 26.0 | M-M |
| highest point | 2.0 | highest point | 4.0 | M-M |
| parent club | 4.0 | parent club | 6.0 | M-M |
| EC enzyme classification | 1.0 | EC enzyme classification | 1.0 | 1-1 |
| temporal range start | 1.0 | temporal range start | 4.0 | 1-M |
| Lagrangian point | 1.0 | Lagrangian point | 7.0 | 1-M |
| asteroid family | 1.0 | asteroid family | 24.0 | 1-M |
| hymenium type | 1.0 | hymenium type | 707.0 | 1-M |
| temporal range end | 2.0 | temporal range end | 4.0 | M-M |
| interchange station | 3.0 | interchange station | 3.0 | M-M |
| legislative body | 3.0 | legislative body | 96.0 | M-M |
| start point | 2.0 | start point | 18.0 | M-M |
| streak color | 1.0 | streak color | 25.0 | 1-M |
| significant drug interaction | 42.0 | significant drug interaction | 44.0 | M-M |
| has effect | 2.0 | has effect | 1.0 | M-1 |
| killed by | 5.0 | killed by | 27.0 | M-M |
| basionym | 1.0 | basionym | 8.0 | 1-M |
| main subject | 51.0 | main subject | 1564.0 | M-M |
| partner in business or sport | 1.0 | partner in business or sport | 1.0 | 1-1 |
| political ideology | 19.0 | political ideology | 248.0 | M-M |
| wing configuration | 2.0 | wing configuration | 252.0 | M-M |
| mushroom ecological type | 2.0 | mushroom ecological type | 587.0 | M-M |
| screenwriter | 65.0 | screenwriter | 191.0 | M-M |
| type of variable star | 2.0 | type of variable star | 2.0 | M-M |
| fossil found in this unit | 2.0 | fossil found in this unit | 2.0 | M-M |
| dan/kyu rank | 1.0 | dan/kyu rank | 5.0 | 1-M |
| field of this occupation | 3.0 | field of this occupation | 12.0 | M-M |
| measurement scale | 3.0 | measurement scale | 4.0 | M-M |
| successful candidate | 12.0 | successful candidate | 12.0 | M-M |
| occupation | 137.0 | occupation | 223411.0 | M-M |
| twinned administrative body | 98.0 | twinned administrative body | 98.0 | M-M |
| has cause | 5.0 | has cause | 2.0 | M-M |
| Digital Rights Management system | 1.0 | Digital Rights Management system | 13.0 | 1-M |
| crew member(s) | 10.0 | crew member(s) | 148.0 | M-M |
| bodies of water basin category | 1.0 | bodies of water basin category | 17.0 | 1-M |
| edibility | 1.0 | edibility | 185.0 | 1-M |
| published in | 3.0 | published in | 212.0 | M-M |
| original broadcaster | 7.0 | original broadcaster | 385.0 | M-M |
| GHS signal word | 1.0 | GHS signal word | 1.0 | 1-1 |
| MPA film rating | 1.0 | MPA film rating | 6.0 | 1-M |
| director / manager | 36.0 | director / manager | 4.0 | M-M |
| location of discovery | 3.0 | location of discovery | 7.0 | M-M |
| presenter | 24.0 | presenter | 11.0 | M-M |
| theme music | 1.0 | theme music | 1.0 | 1-1 |
| authority | 1.0 | authority | 1.0 | 1-1 |
| chromosome | 2.0 | chromosome | 98.0 | M-M |
| lowest point | 1.0 | lowest point | 1.0 | 1-1 |
| manufacturer | 11.0 | manufacturer | 160.0 | M-M |
| product certification | 2.0 | product certification | 47.0 | M-M |
| takes place in fictional universe | 5.0 | takes place in fictional universe | 83.0 | M-M |
| followed by | 50.0 | followed by | 52.0 | M-M |
| category of people buried here | 1.0 | category of people buried here | 1.0 | 1-1 |
| contributor to the creative work or subject | 311.0 | contributor to the creative work or subject | 8.0 | M-M |
| printed by | 2.0 | printed by | 2.0 | M-M |
| website account on | 24.0 | website account on | 8317.0 | M-M |
| drafted by | 1.0 | drafted by | 9.0 | 1-M |
| nominated for | 2.0 | nominated for | 377.0 | M-M |
| writable file format | 5.0 | writable file format | 4.0 | M-M |
| conflict | 10.0 | conflict | 16039.0 | M-M |
| exemplar of | 3.0 | exemplar of | 8.0 | M-M |
| chief executive officer | 10.0 | chief executive officer | 2.0 | M-M |
| coolant | 1.0 | coolant | 138.0 | 1-M |
| canonization status | 4.0 | canonization status | 1925.0 | M-M |
| publisher | 9.0 | publisher | 757.0 | M-M |
| commander of (DEPRECATED) | 5.0 | commander of (DEPRECATED) | 5.0 | M-M |
| creator | 83.0 | creator | 908.0 | M-M |
| facet of | 2.0 | facet of | 124.0 | M-M |
| driving side | 1.0 | driving side | 4.0 | 1-M |
| parent organization | 5.0 | parent organization | 12.0 | M-M |
| operator | 34.0 | operator | 1045.0 | M-M |
| underlies | 3.0 | underlies | 3.0 | M-M |
// Count how many times each relation type occurs
val res = class_df.groupBy("relationType").count()
display(res)
| relationType | count |
|---|---|
| 1-M | 59.0 |
| M-M | 386.0 |
| 1-1 | 55.0 |
| M-1 | 25.0 |
We can see that the majority of relations are of the type M-M, which is indeed the most general category. While the results are quite noisy, we can find some intuitive examples in the list. For example, relations like "shape" and "mushroom cap shape" are 1-M, showing that most entities are assigned a single unique shape. The M-1 category sadly lacks relations with many source entities. We can still find a few relations that intuitively fit in this category. An example is "academic thesis", since typically the author of a thesis is only one person but one person can author multiple theses.
A possible weakness of this approach for classifying the relations is that it is enough that a "?-M" or "M-?" relationship exist for one entity in order to classify the entire relation as such. One way to improve on this could be to set a threshold, requiring the pattern to be found for more than one entity in order to draw the conclusion for the relation in general. Setting such a threshold is however non-trivial and would surely require taking into account the prevalence of each relation among the edges. On the other hand, any formal definition of a 1-M relation requires only one single entity to satisfy it in order for the relation itself to belong to this group. By this reasoning the only correct way to choose a threshold is at one entity. In the end the question boils down to how noisy we believe the data to be.
./02_load_data
import spark.implicits._
import org.graphframes._
Preprocessing
- Here we inherent previous notebooks varible.
- We add columns of Symmetricity for Motif finding
df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
val twoCycles = graph.find("(a)-[r1]->(b); (b)-[r2]->(a)")
import sqlContext.implicits._ // for $""
import org.apache.spark.sql.functions._ // for `when`
// IF r1 === r2, Label rel as relSym
val twoCycles_w_sym = twoCycles.withColumn("relSym", when($"r1"("rel") === $"r2"("rel"), "Sym").otherwise("UnSym"))
twoCycles: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 2 more fields]
import sqlContext.implicits._
import org.apache.spark.sql.functions._
twoCycles_w_sym: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 3 more fields]
df1: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
entdescdf: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
val symtype = twoCycles_w_sym.filter(twoCycles_w_sym("relSym") === "Sym").select("r1.rel","relSym").groupBy("rel").count().cache()
val list_sym= symtype.select("rel").map(f=>f.getString(0)).collect.toList
val edgesDF_w_sym = edgesDF.withColumn("relSym", when($"rel".isin(list_sym: _*), "Sym").otherwise("UnSym"))
symtype: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [rel: string, count: bigint]
list_sym: List[String] = List(part of, family name, topic's main Wikimedia portal, parent astronomical body, shares border with, based on, present in work, separated from, writing system, topic's main category, given name version for other gender, father, performer, place of burial, influenced by, developer, depicts, fictional or mythical analog of, producer, shape, taxon synonym, located in or next to body of water, replaced by, part of the series, interleaves with, narrative location, participant, capital of, characters, collection, owner of, structure replaced by, has part(s), located in the administrative territorial entity, employer, chairperson, place of birth, lyrics by, subclass of, instance of, located on street, named after, mother house, country of origin, encoded by, composer, occupant, place of death, relative, director, category's main topic, spouse, author, located in/on physical feature, pendant of, record label, from narrative universe, ortholog, conferred by, diplomatic relation, Wikimedia portal's main topic, sport, child astronomical body, adjacent station, location of creation, mother, companion of, imported from Wikimedia project, has facility, replaces, Unknown, inspired by, student of, readable file format, commissioned by, programmed in, statement is subject of, origin of the watercourse, capital, contains settlement, founded by, member of, tracklist, lake outflow, notable work, tributary, follows, feast day, movement, discoverer or inventor, said to be the same as, terminus, owned by, edition or translation of, mouth of the watercourse, student, scheduled service destination, described by source, cast member, connecting line, home venue, decays to, soundtrack release, software engine, depicted by, copyright license, catalog, architect, has use, parent taxon, residence, country, headquarters location, has edition or translation, home world, color, encodes, doctoral advisor, dedicated to, opposite of, filming location, territory claimed by, partially coincident with, point group, winner, stock exchange, child, location, family name identical to this given name, given name, dual to, made from material, unmarried partner, is a list of, genre, affiliation, contains the administrative territorial entity, interchange station, significant drug interaction, killed by, main subject, partner in business or sport, political ideology, screenwriter, twinned administrative body, crew member(s), published in, presenter, manufacturer, followed by, contributor to the creative work or subject, website account on, conflict, chief executive officer, publisher, creator, facet of, parent organization, operator, underlies)
edgesDF_w_sym: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 2 more fields]
display(edgesDF_w_sym)
| src | rel | dst | relSym |
|---|---|---|---|
| Alfred Hauptmann | place of death | Boston | Sym |
| David Louis Band | place of birth | Boston | Sym |
| John Boyle O'Reilly | place of death | Boston | Sym |
| James Seanor | place of birth | Boston | Sym |
| Aerosmith | location of formation | Boston | UnSym |
| Michael Joseph McEttrick | place of death | Boston | Sym |
| Eduardo Catalano | place of death | Boston | Sym |
| Tristram Dalton | place of death | Boston | Sym |
| Turk Van Lake | place of birth | Boston | Sym |
| Sherman Miles | place of death | Boston | Sym |
| James Abercrombie | place of death | Boston | Sym |
| Derek B. Miller | place of birth | Boston | Sym |
| Nerine Kidd | place of birth | Boston | Sym |
| Kenza Tazi | place of birth | Boston | Sym |
| Q16580475 | place of birth | Boston | Sym |
| Charles Pomeroy Parker | place of birth | Boston | Sym |
| Christiana Carteaux Bannister | residence | Boston | Sym |
| Proctor L. Dougherty | place of birth | Boston | Sym |
| Larry Goldings | place of birth | Boston | Sym |
| Lillie P. Bliss | place of birth | Boston | Sym |
| Amos Clark, Jr. | place of death | Boston | Sym |
| Taipei | twinned administrative body | Boston | Sym |
| John E. Rexine | place of birth | Boston | Sym |
| Thomas Hutchinson | place of birth | Boston | Sym |
| Tip O'Neill | place of death | Boston | Sym |
| Emily Greene Balch | place of birth | Boston | Sym |
| Robert Alfred Theobald | place of death | Boston | Sym |
| Robert Semple | place of birth | Boston | Sym |
| Walter Gilbert | place of birth | Boston | Sym |
| John G. Palfrey | place of birth | Boston | Sym |
| Tom Jennings | place of birth | Boston | Sym |
| A Drink Before the War | narrative location | Boston | Sym |
| Robert Wahlberg | place of birth | Boston | Sym |
| Susan Delano McKelvey | place of death | Boston | Sym |
| Charles Sweeney | place of death | Boston | Sym |
| Carolina Barco | place of birth | Boston | Sym |
| Yehuda Krinsky | place of birth | Boston | Sym |
| Christopher Edley, Jr. | place of birth | Boston | Sym |
| Edward M. Kennedy Jr. | place of birth | Boston | Sym |
| Abbott Lawrence | place of death | Boston | Sym |
| John Quelch | place of death | Boston | Sym |
| Albert Sauveur | place of death | Boston | Sym |
| Matthew Sullivan | place of birth | Boston | Sym |
| Predator | narrative location | Boston | Sym |
| Thurston Hall | place of birth | Boston | Sym |
| William Troy | place of birth | Boston | Sym |
| Charles Henry Turner | place of death | Boston | Sym |
| Douglas Tybor Durig | place of birth | Boston | Sym |
| John William McCormack | place of birth | Boston | Sym |
| Jennifer Jostyn | place of birth | Boston | Sym |
| William C. Durant | place of birth | Boston | Sym |
| George Cabot | place of death | Boston | Sym |
| Marianne Leone Cooper | place of birth | Boston | Sym |
| Mario Grossi | place of death | Boston | Sym |
| Sib Hashian | place of birth | Boston | Sym |
| Susan Minot | place of birth | Boston | Sym |
| Mikhail Danilov | place of death | Boston | Sym |
| Lexi Love | place of birth | Boston | Sym |
| John M. Flynn | place of birth | Boston | Sym |
| Rita Hester | place of death | Boston | Sym |
| Nathaniel Carl Goodwin | place of birth | Boston | Sym |
| Isabella Stewart Gardner | place of death | Boston | Sym |
| Washington Allston | work location | Boston | UnSym |
| Richard Sears | place of death | Boston | Sym |
| Richard Sears | place of birth | Boston | Sym |
| Meg Rosoff | place of birth | Boston | Sym |
| Andras Angyal | place of death | Boston | Sym |
| Anthony Shriver | place of birth | Boston | Sym |
| Austin Edward Ford | place of birth | Boston | Sym |
| Barbara McMartin | place of birth | Boston | Sym |
| Benjamin Edes | place of death | Boston | Sym |
| Boston City Council | applies to jurisdiction | Boston | UnSym |
| Henry Marsh | place of birth | Boston | Sym |
| Charles Henry Davis | place of birth | Boston | Sym |
| Charles R. Codman | place of death | Boston | Sym |
| Charles R. Codman | place of birth | Boston | Sym |
| Charles Sawyer Russell | place of birth | Boston | Sym |
| Charles W. Bailey | place of birth | Boston | Sym |
| Chrystal Herne | place of death | Boston | Sym |
| Gretchen Merrill | place of birth | Boston | Sym |
| Crystal Bird Fauset | residence | Boston | Sym |
| Lawrence Whitney | place of death | Boston | Sym |
| Dave "Chico" Ryan | place of death | Boston | Sym |
| David Herbert Donald | place of death | Boston | Sym |
| David Shiner | place of birth | Boston | Sym |
| Frank Knox | place of birth | Boston | Sym |
| Francis Woodman Cleaves | place of birth | Boston | Sym |
| Arthur Berger | place of death | Boston | Sym |
| Bill Holland | place of birth | Boston | Sym |
| Richard Cushing | place of death | Boston | Sym |
| Richard Cushing | place of birth | Boston | Sym |
| Gene Wood | place of death | Boston | Sym |
| William Monahan | place of birth | Boston | Sym |
| James D. Morgan | place of birth | Boston | Sym |
| Padua | twinned administrative body | Boston | Sym |
| Joe Chiccarelli | place of birth | Boston | Sym |
| Richard Armitage | place of birth | Boston | Sym |
| John Gibson | place of birth | Boston | Sym |
| John Marston | place of birth | Boston | Sym |
| Joseph Henry Beale | place of birth | Boston | Sym |
| Erna Lazarus | place of birth | Boston | Sym |
| Peter Abrahams | place of birth | Boston | Sym |
| Lucianne Goldberg | place of birth | Boston | Sym |
| Bill Laimbeer | place of birth | Boston | Sym |
| Paul Coyne | place of birth | Boston | Sym |
| Percy Jewett Burrell | place of birth | Boston | Sym |
| Frank Synott | place of death | Boston | Sym |
| Eric Griffin | place of birth | Boston | Sym |
| Rebecca Eaton | place of birth | Boston | Sym |
| Richard Saltonstall Greenough | place of birth | Boston | Sym |
| Robert Cowdin | place of death | Boston | Sym |
| Robert Rimmer | place of birth | Boston | Sym |
| Sara Wilford | place of birth | Boston | Sym |
| Sheanon Williams | place of birth | Boston | Sym |
| Category:Films set in Boston | category combines topics | Boston | UnSym |
| Chris Cormier | place of birth | Boston | Sym |
| The Holder of the World | narrative location | Boston | Sym |
| Therese Murray | place of birth | Boston | Sym |
| Thomas G. Kelley | place of birth | Boston | Sym |
| Thomas Savage | place of death | Boston | Sym |
| Ernst Badian | place of death | Boston | Sym |
| Wadsworth Harris | place of birth | Boston | Sym |
| Wallace Tripp | place of birth | Boston | Sym |
| William Cooper Nell | place of death | Boston | Sym |
| William Cooper Nell | place of birth | Boston | Sym |
| Richard von Mises | place of death | Boston | Sym |
| David Ignatius Walsh | place of death | Boston | Sym |
| Borah Bergman | place of death | Boston | Sym |
| Luis T. Romero | place of death | Boston | Sym |
| Henry Gilman | place of birth | Boston | Sym |
| John Ciardi | place of birth | Boston | Sym |
| Peter MacKenzie | place of birth | Boston | Sym |
| Carl McKinley | place of death | Boston | Sym |
| Charles Edward Adams | place of birth | Boston | Sym |
| Brian Fair | place of birth | Boston | Sym |
| Mario Corti | residence | Boston | Sym |
| Georg Klemperer | place of death | Boston | Sym |
| Frances Wayne | place of death | Boston | Sym |
| Frances Wayne | place of birth | Boston | Sym |
| Edward Norton | place of birth | Boston | Sym |
| Jimmy McHugh | place of birth | Boston | Sym |
| John L. Blake | place of birth | Boston | Sym |
| Sylvia Plath | residence | Boston | Sym |
| Sylvia Plath | place of birth | Boston | Sym |
| George Underwood | place of death | Boston | Sym |
| William Billings | place of birth | Boston | Sym |
| William Billings | place of death | Boston | Sym |
| Terrayne Crawford | place of birth | Boston | Sym |
| Boston Consulting Group | headquarters location | Boston | Sym |
| Frederick Wiseman | place of birth | Boston | Sym |
| William Stimpson | place of birth | Boston | Sym |
| Financial District | located in the administrative territorial entity | Boston | Sym |
| Frederic Woodman Root | place of birth | Boston | Sym |
| Horace Brooks | place of birth | Boston | Sym |
| Richard J. Kerry | place of death | Boston | Sym |
| Lisa Niver Rajna | place of birth | Boston | Sym |
| George Harrington | place of birth | Boston | Sym |
| James MacGregor Burns | place of birth | Boston | Sym |
| Harrison Henry Atwood | place of death | Boston | Sym |
| James G. Maguire | place of birth | Boston | Sym |
| Edgar Allan Poe | place of birth | Boston | Sym |
| Joe Maneri | place of death | Boston | Sym |
| Sean Gullette | place of birth | Boston | Sym |
| Frederick Stevens | place of birth | Boston | Sym |
| William Perkins Babcock | place of birth | Boston | Sym |
| Eric S. Raymond | place of birth | Boston | Sym |
| Henry Way Kendall | place of birth | Boston | Sym |
| Norma Farber | place of birth | Boston | Sym |
| Robert Joseph Banks | place of birth | Boston | Sym |
| Rodman Philbrick | place of birth | Boston | Sym |
| Allison Janney | place of birth | Boston | Sym |
| Christopher Allport | place of birth | Boston | Sym |
| Eric Turner | place of birth | Boston | Sym |
| Evan Dando | place of birth | Boston | Sym |
| Lawrence Joseph Riley | place of birth | Boston | Sym |
| Barbara Delinsky | place of birth | Boston | Sym |
| Kristin Cashore | place of birth | Boston | Sym |
| Kristin Cashore | work location | Boston | UnSym |
| William James Sidis | place of death | Boston | Sym |
| Melbourne | twinned administrative body | Boston | Sym |
| John Singleton Copley | place of birth | Boston | Sym |
| Joseph Badger | place of death | Boston | Sym |
| Kate Collins | place of birth | Boston | Sym |
| Abigail Johnson | residence | Boston | Sym |
| William Hill Brown | place of birth | Boston | Sym |
| Ted Drury | place of birth | Boston | Sym |
| Marc Ferrari | place of birth | Boston | Sym |
| Kenneth O'Donnell | place of death | Boston | Sym |
| Rich Hill | place of birth | Boston | Sym |
| Increase Sumner | place of death | Boston | Sym |
| Ricky Ford | place of birth | Boston | Sym |
| Charles Devens | place of death | Boston | Sym |
| George Jung | place of birth | Boston | Sym |
| T. J. Thyne | place of birth | Boston | Sym |
| Courtney Eldridge | place of birth | Boston | Sym |
| Ian Biederman | place of birth | Boston | Sym |
| Jimmy Flynn | place of birth | Boston | Sym |
| Aidan Mitchell | place of birth | Boston | Sym |
| Guinevere Turner | place of birth | Boston | Sym |
| Jeanie MacPherson | place of birth | Boston | Sym |
| Henry Brooks Adams | place of birth | Boston | Sym |
| Susanna Rowson | place of death | Boston | Sym |
| Anita Shreve | work location | Boston | UnSym |
| Alan Trefler | place of birth | Boston | Sym |
| Alexander Bradley | place of birth | Boston | Sym |
| Alexander Leaf | place of death | Boston | Sym |
| Alicyn Packard | place of birth | Boston | Sym |
| Beals Coleman Wright | place of birth | Boston | Sym |
| Andrei Zelevinsky | place of death | Boston | Sym |
| Carmen Filpi | place of birth | Boston | Sym |
| Caspar Crowninshield | place of birth | Boston | Sym |
| Caspar Crowninshield | place of death | Boston | Sym |
| William Hickling Prescott | place of death | Boston | Sym |
| Elihu Yale | place of birth | Boston | Sym |
| Craig Ross, Jr. | place of birth | Boston | Sym |
| Daniel Lothrop | place of death | Boston | Sym |
| David Barstow | place of birth | Boston | Sym |
| Dick Kazmaier | place of death | Boston | Sym |
| Janet Tashjian | place of birth | Boston | Sym |
| Emma Nutt | place of birth | Boston | Sym |
| Faith Salie | place of birth | Boston | Sym |
| Frank Newcomb | place of birth | Boston | Sym |
| Freeman Gill | place of birth | Boston | Sym |
| Geoff Edgers | place of birth | Boston | Sym |
| George D. Murray | place of birth | Boston | Sym |
| George W. Casey, Sr. | place of birth | Boston | Sym |
| Henry A. Miley, Jr. | place of birth | Boston | Sym |
| John Andrew Sullivan | place of birth | Boston | Sym |
| Samuel Dexter | place of birth | Boston | Sym |
| Joe Gould | place of birth | Boston | Sym |
| James Fowle Baldwin | place of death | Boston | Sym |
| Jerry Williams | place of death | Boston | Sym |
| John Andrew Barnes III | place of birth | Boston | Sym |
| John Frykman | place of birth | Boston | Sym |
| Jonathan Sewall | place of birth | Boston | Sym |
| Archibald Thompson Davison | place of birth | Boston | Sym |
| George Peter Alexander Healy | place of birth | Boston | Sym |
| Lea Wait | place of birth | Boston | Sym |
| Margaret Green Draper | place of birth | Boston | Sym |
| Mark Goulston | place of birth | Boston | Sym |
| Mary Lou Clements-Mann | place of birth | Boston | Sym |
| Melvin Johnson | place of birth | Boston | Sym |
| Michael DeSisto | place of death | Boston | Sym |
| Michael DeSisto | place of birth | Boston | Sym |
| Moses Roper | place of death | Boston | Sym |
| Nathaniel S. Keith | place of birth | Boston | Sym |
| Norman Foster | place of birth | Boston | Sym |
| Arthur Duffey | place of death | Boston | Sym |
| Minor White | place of death | Boston | Sym |
| Paul Van Doren | place of birth | Boston | Sym |
| Chris Burden | place of birth | Boston | Sym |
| Patrick Renna | place of birth | Boston | Sym |
| Augustus Addison Gould | place of death | Boston | Sym |
| William Henry Lewis | place of death | Boston | Sym |
| George P. Wetmore | place of death | Boston | Sym |
| Kenny Wormald | place of birth | Boston | Sym |
| L Peter Deutsch | place of birth | Boston | Sym |
| Edwin O'Connor | place of death | Boston | Sym |
| Justin Winsor | place of birth | Boston | Sym |
| Catherine Filene Shouse | place of birth | Boston | Sym |
| The Dante Club | narrative location | Boston | Sym |
| Charles Francis Adams, Jr. | place of birth | Boston | Sym |
| Richard Evans Schultes | place of death | Boston | Sym |
| Richard Evans Schultes | place of birth | Boston | Sym |
| Lorna Thayer | place of birth | Boston | Sym |
| Frank Stanton | place of death | Boston | Sym |
| Kerin O'Keefe | place of birth | Boston | Sym |
| Joey Oakes Palfrey | place of birth | Boston | Sym |
| Andrejs Zeidaks | place of death | Boston | Sym |
| Robert Charles Winthrop | place of death | Boston | Sym |
| Robert Charles Winthrop | place of birth | Boston | Sym |
| Frederick G. Katzmann | place of death | Boston | Sym |
| Frederick G. Katzmann | place of birth | Boston | Sym |
| Phil Rasmussen | place of birth | Boston | Sym |
| Gerry Studds | place of death | Boston | Sym |
| Jason Chen | place of birth | Boston | Sym |
| Roger Adams | place of birth | Boston | Sym |
| Mark Leibovich | place of birth | Boston | Sym |
| Q15987713 | place of birth | Boston | Sym |
| Mark Wahlberg | place of birth | Boston | Sym |
| Harold J. Greene | place of birth | Boston | Sym |
| Joseph E. Levine | place of birth | Boston | Sym |
| On Beauty | narrative location | Boston | Sym |
| Lawrence J. Hogan | place of birth | Boston | Sym |
| Daniel Dennett | place of birth | Boston | Sym |
| John Boswell | place of birth | Boston | Sym |
| A Case of Need | narrative location | Boston | Sym |
| William F. Sharpe | place of birth | Boston | Sym |
| Alex Cobb | place of birth | Boston | Sym |
| Brian Noonan | place of birth | Boston | Sym |
| Charles Codman | place of birth | Boston | Sym |
| Richard N. Goodwin | place of birth | Boston | Sym |
| Benjamin Franklin | place of birth | Boston | Sym |
| William Stowell | place of birth | Boston | Sym |
| Bill Collins | place of birth | Boston | Sym |
| Makanda Ken McIntyre | place of birth | Boston | Sym |
| Edwin Richards | place of birth | Boston | Sym |
| Edwin Lord Weeks | place of birth | Boston | Sym |
| Russ Lee | place of birth | Boston | Sym |
| Frank Dayton | place of birth | Boston | Sym |
| Thomas Dudley | place of death | Boston | Sym |
| James Fitzgerald | place of birth | Boston | Sym |
| Gone, Baby, Gone | narrative location | Boston | Sym |
| Dana Barros | place of birth | Boston | Sym |
| Richard Mayo | place of birth | Boston | Sym |
| Ned Dowd | place of birth | Boston | Sym |
| Armand Van Helden | place of birth | Boston | Sym |
| Helen McCloy | place of death | Boston | Sym |
| Terri Lyne Carrington | work location | Boston | UnSym |
| Anatole Broyard | place of death | Boston | Sym |
| Bob Backus | place of birth | Boston | Sym |
| Bob Durgin | place of birth | Boston | Sym |
| James Bryant Conant | place of birth | Boston | Sym |
| Curt Conway | place of birth | Boston | Sym |
| Claudia Rueda | work location | Boston | UnSym |
| David Evans | place of death | Boston | Sym |
| Francis E. Kelly | place of birth | Boston | Sym |
| George Heyliger | place of birth | Boston | Sym |
| George Melville Baker | residence | Boston | Sym |
| Gladys Walton | place of birth | Boston | Sym |
| Henry Hendrickson | place of death | Boston | Sym |
| Jamie Denbo | place of birth | Boston | Sym |
| Jamie Turndorf | place of birth | Boston | Sym |
| Jed Prouty | place of birth | Boston | Sym |
| John D. Harvey | place of birth | Boston | Sym |
| Leonard Chadwick | place of death | Boston | Sym |
| Louis Kronberg | place of birth | Boston | Sym |
| Carroll Quigley | place of birth | Boston | Sym |
| Oliver Winchester | place of birth | Boston | Sym |
| Shadow Fox | narrative location | Boston | Sym |
| The Namesake | narrative location | Boston | Sym |
| Thomas Finneran | place of birth | Boston | Sym |
| Thomas Cushing | place of death | Boston | Sym |
| Károly Bartha | place of death | Boston | Sym |
| William Emerson | place of death | Boston | Sym |
| William Sweeney | place of birth | Boston | Sym |
| Boylston Street | located in the administrative territorial entity | Boston | Sym |
| Josiah P. Cooke | place of birth | Boston | Sym |
| Saul Rosenzweig | place of birth | Boston | Sym |
| Theodore Lyman | place of birth | Boston | Sym |
| Humberto Sousa Medeiros | place of death | Boston | Sym |
| Noah Bean | place of birth | Boston | Sym |
| Burton Pike | place of birth | Boston | Sym |
| Charles Bulfinch | place of death | Boston | Sym |
| Charles Bulfinch | place of birth | Boston | Sym |
| Chuckie Taylor | place of birth | Boston | Sym |
| Boston Marathon bombings | located in the administrative territorial entity | Boston | Sym |
| Milt Raskin | place of birth | Boston | Sym |
| Patrick Ewing, Jr. | place of birth | Boston | Sym |
| Edith Fellows | place of birth | Boston | Sym |
| James Pierpont | place of birth | Boston | Sym |
| Mianne Palfrey | place of birth | Boston | Sym |
| Jill Tasker | place of birth | Boston | Sym |
| Hugh S. Legaré | place of death | Boston | Sym |
| Henry E. Dixey | place of birth | Boston | Sym |
| John Smith | place of birth | Boston | Sym |
| Gerry Connolly | place of birth | Boston | Sym |
| Hugo Rossi | place of birth | Boston | Sym |
| Godfrey Lowell Cabot | place of death | Boston | Sym |
| Godfrey Lowell Cabot | place of birth | Boston | Sym |
| Timothy Davis | place of death | Boston | Sym |
| Thomas Gamaliel Bradford | place of birth | Boston | Sym |
| Henry Pickering Bowditch | place of death | Boston | Sym |
| Henry Pickering Bowditch | place of birth | Boston | Sym |
| Madeleine M. Joullié | residence | Boston | Sym |
| Joanne Simpson | place of birth | Boston | Sym |
| John Henry Willcox | place of death | Boston | Sym |
| Busty Heart | place of birth | Boston | Sym |
| Lawrence Berk | place of birth | Boston | Sym |
| Max Bondy | place of death | Boston | Sym |
| Baruch Marzel | place of birth | Boston | Sym |
| Peleg Coffin, Jr. | place of death | Boston | Sym |
| Peter A. Garland | place of birth | Boston | Sym |
| Tabitha St. Germain | place of birth | Boston | Sym |
| Allan H. Meltzer | place of birth | Boston | Sym |
| Felix Wolfes | place of death | Boston | Sym |
| William P. Murphy Jr. | place of birth | Boston | Sym |
| Geraldine Ferraro | place of death | Boston | Sym |
| Susan Bottomly | place of birth | Boston | Sym |
| Anne Dudek | place of birth | Boston | Sym |
| Edith Nourse Rogers | place of death | Boston | Sym |
| Thomas D. Eliot | place of birth | Boston | Sym |
| Paul Stanton | place of birth | Boston | Sym |
| Al Vega | place of death | Boston | Sym |
| Albert Smith | place of death | Boston | Sym |
| Eric Francis MacKenzie | place of birth | Boston | Sym |
| Michael McDowell | place of death | Boston | Sym |
| Jack Levine | place of birth | Boston | Sym |
| Donald Schön | place of birth | Boston | Sym |
| Dorothy Loudon | place of birth | Boston | Sym |
| Manny Delcarmen | place of birth | Boston | Sym |
| John Pitcairn | place of death | Boston | Sym |
| Eugene Braunwald | work location | Boston | UnSym |
| Joseph Grew | place of birth | Boston | Sym |
| Seán McKiernan | place of birth | Boston | Sym |
| Darren Turcotte | place of birth | Boston | Sym |
| Steven Van Zandt | place of birth | Boston | Sym |
| Ruby Braff | place of birth | Boston | Sym |
| Jane Cowl | place of birth | Boston | Sym |
| Elliott H. Lieb | place of birth | Boston | Sym |
| Fran Sheehan | place of birth | Boston | Sym |
| Edwin Percy Whipple | place of death | Boston | Sym |
| John Cunniff | place of birth | Boston | Sym |
| Brenda Frazier | place of death | Boston | Sym |
| Jonas Wood | place of birth | Boston | Sym |
| Henry Jacob Bigelow | place of birth | Boston | Sym |
| Henry Jacob Bigelow | work location | Boston | UnSym |
| Feliks Roziner | place of death | Boston | Sym |
| Eugene Foss | place of death | Boston | Sym |
| Alvan Tufts Fuller | place of death | Boston | Sym |
| Alvan Tufts Fuller | place of birth | Boston | Sym |
| Jane Toppan | place of birth | Boston | Sym |
| Jasmine Guy | place of birth | Boston | Sym |
| William M. Butler | place of death | Boston | Sym |
| Joseph Francis Maguire | place of birth | Boston | Sym |
| Abby May | place of birth | Boston | Sym |
| Madeline Miller | place of birth | Boston | Sym |
| Albert Bushnell Hart | place of death | Boston | Sym |
| Angeliki Laiou | place of death | Boston | Sym |
| Anne Nagel | place of birth | Boston | Sym |
| Arthur L. Andrews | place of birth | Boston | Sym |
| Benny Rubin | place of birth | Boston | Sym |
| Julius Adams Stratton | place of death | Boston | Sym |
| Ceremony | narrative location | Boston | Sym |
| Cogan's Trade | narrative location | Boston | Sym |
| Colonial Air Transport | headquarters location | Boston | Sym |
| Donald Foley | place of birth | Boston | Sym |
| Eddie Hurley | place of death | Boston | Sym |
| Elizabeth Brater | place of birth | Boston | Sym |
| Eric Loren | place of birth | Boston | Sym |
| Ethan Vogt | place of birth | Boston | Sym |
| Franklin S. Nickerson | place of death | Boston | Sym |
| William Steig | place of death | Boston | Sym |
| Whitey Bulger | place of birth | Boston | Sym |
| Raymond Griffith | place of birth | Boston | Sym |
| Hosea Ballou | place of death | Boston | Sym |
| Walter Gropius | place of death | Boston | Sym |
| Theodore Robert Dudley | place of birth | Boston | Sym |
| Jimmy Brogan | place of birth | Boston | Sym |
| John Elliott Cowdin | place of birth | Boston | Sym |
| John Sullivan Dwight | place of birth | Boston | Sym |
| Joshua Loring | place of birth | Boston | Sym |
| Samuel Sewall | place of death | Boston | Sym |
| Kempster Blanchard Miller | place of birth | Boston | Sym |
| Leon Adams | place of birth | Boston | Sym |
| Samuel Turell Armstrong | place of death | Boston | Sym |
| Maud Howe Elliott | place of birth | Boston | Sym |
| Richard Fletcher | place of death | Boston | Sym |
| Miles Browning | place of death | Boston | Sym |
| Muriel Rahn | place of birth | Boston | Sym |
| Nancy Glass | place of birth | Boston | Sym |
| Nathaniel Jeremiah Bradlee | place of birth | Boston | Sym |
| Roger Manvell | place of death | Boston | Sym |
| Richard Olney | place of death | Boston | Sym |
| Oliver O'Brien | place of birth | Boston | Sym |
| Paul M. English | place of birth | Boston | Sym |
| Albert-László Barabási | work location | Boston | UnSym |
| Richard Bellingham | place of death | Boston | Sym |
| Carolyn Bertozzi | place of birth | Boston | Sym |
| Stephanie Braxton | place of birth | Boston | Sym |
| Stephen Ratcliffe | place of birth | Boston | Sym |
| The Astonishing Life of Octavian Nothing, Traitor to the Nation, Volume I: The Pox Party | narrative location | Boston | Sym |
| The Last Hurrah | narrative location | Boston | Sym |
| Vail Bloom | place of birth | Boston | Sym |
| Norman Levinson | place of death | Boston | Sym |
| Belly | location of formation | Boston | UnSym |
| Anthony Quinn | place of death | Boston | Sym |
| Christopher Gore | place of birth | Boston | Sym |
| Leonard Wood | place of death | Boston | Sym |
| James Bowdoin | place of birth | Boston | Sym |
| James Bowdoin | place of death | Boston | Sym |
| Category:Deaths in Boston, Lincolnshire | category combines topics | Boston | UnSym |
| Jon Kleinberg | place of birth | Boston | Sym |
| Thomas Bulfinch | place of death | Boston | Sym |
| Dorchester | located in the administrative territorial entity | Boston | Sym |
| Charlie Holmes | place of birth | Boston | Sym |
| Christopher Wool | place of birth | Boston | Sym |
| Leonora Bilger | place of birth | Boston | Sym |
| Danny Draven | place of birth | Boston | Sym |
| David Evans | place of birth | Boston | Sym |
| Kiara Muhammad | place of birth | Boston | Sym |
| Q11884045 | place of birth | Boston | Sym |
| The Surgeon | narrative location | Boston | Sym |
| Prince Sadruddin Aga Khan | place of death | Boston | Sym |
| Markus Fritsch | work location | Boston | UnSym |
| Dyer Lum | place of death | Boston | Sym |
| Joseph Hall | place of death | Boston | Sym |
| Samuel Parkman Tuckerman | place of birth | Boston | Sym |
| Elbridge Ross | place of birth | Boston | Sym |
| Polly Palfrey | place of birth | Boston | Sym |
| Harry L. Shapiro | place of birth | Boston | Sym |
| George Richardson Proctor | place of birth | Boston | Sym |
| Hermann Hoerlin | place of death | Boston | Sym |
| John Parker Boyd | place of death | Boston | Sym |
| Mark Andrew Green | place of birth | Boston | Sym |
| John Campbell | place of death | Boston | Sym |
| Mikloš Schwalb | place of death | Boston | Sym |
| Caroline Coolidge Cushman Ticknor | place of birth | Boston | Sym |
| Manhattan Transfer | place of publication | Boston | UnSym |
| James Henigan | place of birth | Boston | Sym |
| James A. Gallivan | place of birth | Boston | Sym |
| Karl Gerhardt | place of birth | Boston | Sym |
| Tony Gaffney | place of birth | Boston | Sym |
| J. Gill | located in the administrative territorial entity | Boston | Sym |
| Leslie H. Martinson | place of birth | Boston | Sym |
| Philip Hale | place of death | Boston | Sym |
| Richard France | place of birth | Boston | Sym |
| Royall Tyler | place of birth | Boston | Sym |
| William Fly | place of death | Boston | Sym |
| Mary Dyer | place of death | Boston | Sym |
| Madeline Kahn | place of birth | Boston | Sym |
| William Barton Rogers | place of death | Boston | Sym |
| Harriet Quimby | place of death | Boston | Sym |
| Frank Robbins | place of birth | Boston | Sym |
| Q257073 | located in the administrative territorial entity | Boston | Sym |
| William Sowden Sims | place of death | Boston | Sym |
| Walter Powers | place of birth | Boston | Sym |
| Williamina Fleming | place of death | Boston | Sym |
| Angelina Weld Grimké | place of birth | Boston | Sym |
| James Taylor | place of birth | Boston | Sym |
| William Mason | place of birth | Boston | Sym |
| Liam Waite | place of birth | Boston | Sym |
| Francis Amasa Walker | place of birth | Boston | Sym |
| Francis Amasa Walker | place of death | Boston | Sym |
| James Hewitt | place of death | Boston | Sym |
| Dennis Miller Bunker | place of death | Boston | Sym |
| Loïs Mailou Jones | place of birth | Boston | Sym |
| Mickey Roach | place of birth | Boston | Sym |
| Dorothy Iannone | place of birth | Boston | Sym |
| Sarah Sze | place of birth | Boston | Sym |
| Oliver Wendell Holmes | place of birth | Boston | Sym |
| Cotton Mather | place of death | Boston | Sym |
| Cotton Mather | place of birth | Boston | Sym |
| James Crafts | place of birth | Boston | Sym |
| Thomas William Parsons | place of birth | Boston | Sym |
| Francis Boott | place of birth | Boston | Sym |
| Lev Lazarevitsj Goldin | place of death | Boston | Sym |
| Joseph R. Levenson | place of birth | Boston | Sym |
| Barbara Mullen | place of birth | Boston | Sym |
| George Goldthwaite | place of birth | Boston | Sym |
| Damon Santostefano | place of birth | Boston | Sym |
| Robert F. Bradford | place of birth | Boston | Sym |
| Robert F. Bradford | place of death | Boston | Sym |
| Charles F. Hurley | place of death | Boston | Sym |
| Solomon Trestin | place of death | Boston | Sym |
| Sarah Kemble Knight | place of birth | Boston | Sym |
| Henry Oliver Hansen | place of birth | Boston | Sym |
| Cell | narrative location | Boston | Sym |
| Alfred Browning Parker | place of birth | Boston | Sym |
| Amy Farrington | place of birth | Boston | Sym |
| Benjamin Byron Davis | place of birth | Boston | Sym |
| Billie Lawless | place of birth | Boston | Sym |
| Museum of Fine Arts Boston | located in the administrative territorial entity | Boston | Sym |
| Faneuil Hall | located in the administrative territorial entity | Boston | Sym |
| Brian Christie | place of birth | Boston | Sym |
| Cameron McRae Winslow | place of death | Boston | Sym |
| Carl Alpert | place of birth | Boston | Sym |
| Maribel Owen | place of birth | Boston | Sym |
| Connie Martinson | place of birth | Boston | Sym |
| Courtney Fathom Sell | place of birth | Boston | Sym |
| George Bonhag | place of birth | Boston | Sym |
| David B. Cohen | place of birth | Boston | Sym |
| Wendell Phillips | place of death | Boston | Sym |
| Wendell Phillips | place of birth | Boston | Sym |
| Rachel Bissex | place of birth | Boston | Sym |
| Edward H. Gibson | place of birth | Boston | Sym |
| Edward Lawrence Logan | place of death | Boston | Sym |
| Elliot Koffman | place of birth | Boston | Sym |
| Fannie Hillsmith | place of birth | Boston | Sym |
| Frederick T. Moore, Jr. | place of birth | Boston | Sym |
| Nathan Appleton | place of death | Boston | Sym |
| Geoffrey Sayre-McCord | place of birth | Boston | Sym |
| George D. Nye | place of death | Boston | Sym |
| George Willis | place of birth | Boston | Sym |
| Henry Simmons Frieze | place of birth | Boston | Sym |
| Howard Bryant | place of birth | Boston | Sym |
| Tom Barrasso | place of birth | Boston | Sym |
| Jack Germond | place of birth | Boston | Sym |
| James G. Carr | place of birth | Boston | Sym |
| John C. Cremony | place of birth | Boston | Sym |
| John Weiss | place of birth | Boston | Sym |
| Jonathan Jackson | place of birth | Boston | Sym |
| Jonathan Jackson | place of death | Boston | Sym |
| Joseph Francis Scott | place of birth | Boston | Sym |
| Laura Poitras | place of birth | Boston | Sym |
| Lauren Elliott | place of birth | Boston | Sym |
| Liam Madden | place of birth | Boston | Sym |
| Elisha Collier | place of birth | Boston | Sym |
| Elisha Collier | place of death | Boston | Sym |
| Christian Wolff | work location | Boston | UnSym |
| Lois Ayres | place of birth | Boston | Sym |
| Marie Cosindas | place of birth | Boston | Sym |
| Matt the Knife | place of birth | Boston | Sym |
| Paul-Henri Campbell | place of birth | Boston | Sym |
| Michael Gould | place of birth | Boston | Sym |
| Suki Schorer | place of birth | Boston | Sym |
| Nathaniel Taylor | place of birth | Boston | Sym |
| Arthur Fiedler | place of birth | Boston | Sym |
| Peter Bent Brigham | place of death | Boston | Sym |
| Peter E. Costello | place of birth | Boston | Sym |
| Rebecca Housel | place of birth | Boston | Sym |
| Roland Merullo | place of birth | Boston | Sym |
| Gregory Maguire | work location | Boston | UnSym |
| Samuel M. Pook | place of birth | Boston | Sym |
| Stephen Dunham | place of birth | Boston | Sym |
| Thomas H. Dunham | place of birth | Boston | Sym |
| Hanns Sachs | place of death | Boston | Sym |
| Walter R. Mansfield | place of birth | Boston | Sym |
| William H. Blanchard | place of birth | Boston | Sym |
| William S. Bennet II | place of death | Boston | Sym |
| Brian Duffy | place of birth | Boston | Sym |
| Harry Dexter White | place of birth | Boston | Sym |
| Richard M. Karp | place of birth | Boston | Sym |
| Eugene O'Neill | place of death | Boston | Sym |
| Gregory Deyermenjian | place of birth | Boston | Sym |
| Samuel Eliot Morison | place of birth | Boston | Sym |
| Samuel Eliot Morison | place of death | Boston | Sym |
| Caleb Blood Smith | place of birth | Boston | Sym |
| Charles William Eliot | place of birth | Boston | Sym |
| David Walton | place of birth | Boston | Sym |
| Horace Mann Junior | place of birth | Boston | Sym |
| Susan Paul | place of birth | Boston | Sym |
| Susan Paul | place of death | Boston | Sym |
| David Gilbarg | place of birth | Boston | Sym |
| Hugh Parker Guiler | place of birth | Boston | Sym |
| Kenneth W. Dam | place of birth | Boston | Sym |
| Emanuel Ondříček | place of death | Boston | Sym |
| Leonardo Ciampa | place of birth | Boston | Sym |
| Nat Hentoff | place of birth | Boston | Sym |
| Samuel Wendell Williston | place of birth | Boston | Sym |
| George Ticknor | place of birth | Boston | Sym |
| Frank Morey | place of birth | Boston | Sym |
| Robert F. McDermott | place of birth | Boston | Sym |
| Uzo Aduba | place of birth | Boston | Sym |
| George Holden Tinkham | place of birth | Boston | Sym |
| Shearjashub Bourne | place of death | Boston | Sym |
| Lucy Toulmin Smith | place of birth | Boston | Sym |
| Henry Vaughan | place of death | Boston | Sym |
| Leonard Nimoy | place of birth | Boston | Sym |
| Richard Hodgson | place of death | Boston | Sym |
| Chin Feng | place of death | Boston | Sym |
| John Neagle | place of birth | Boston | Sym |
| Joseph Henry O'Neil | place of death | Boston | Sym |
| Harry Beal Torrey | place of birth | Boston | Sym |
| William Thompson Sedgwick | place of death | Boston | Sym |
| John Paine | place of birth | Boston | Sym |
| Charles Green Bush | place of birth | Boston | Sym |
| Richard Bowditch Wigglesworth | place of death | Boston | Sym |
| Richard Bowditch Wigglesworth | place of birth | Boston | Sym |
| Roslindale | located in the administrative territorial entity | Boston | Sym |
| Pauline Whittier | place of birth | Boston | Sym |
| William S. McNary | place of death | Boston | Sym |
| Alfred Charles Hobbs | place of birth | Boston | Sym |
| Lauren Koslow | place of birth | Boston | Sym |
| Lillian Roth | place of birth | Boston | Sym |
| Robert Morss Lovett | place of birth | Boston | Sym |
| Tracy Bonham | place of birth | Boston | Sym |
| Abbott Handerson Thayer | place of birth | Boston | Sym |
| Bobby Brown | place of birth | Boston | Sym |
| Donnie Wahlberg | place of birth | Boston | Sym |
| Misha Collins | place of birth | Boston | Sym |
| Sheldon Adelson | place of birth | Boston | Sym |
| Tyler Faith | place of birth | Boston | Sym |
| Quincy Shaw | place of birth | Boston | Sym |
| Dick Dale | place of birth | Boston | Sym |
| Zodiac | narrative location | Boston | Sym |
| Babe Paley | place of birth | Boston | Sym |
| Barry Goudreau | place of birth | Boston | Sym |
| Isador Coriat | place of death | Boston | Sym |
| Arthur Blake | place of death | Boston | Sym |
| Arthur Blake | place of birth | Boston | Sym |
| Alex Grasshoff | place of birth | Boston | Sym |
| Q3796516 | place of death | Boston | Sym |
| Paul McGonagle | place of death | Boston | Sym |
| Wayne Turner | place of birth | Boston | Sym |
| Charles Edward Horn | place of death | Boston | Sym |
| Q4531342 | place of death | Boston | Sym |
| Q4531342 | place of birth | Boston | Sym |
| Helena Koželuhová | place of death | Boston | Sym |
| Michelle Thomas | place of birth | Boston | Sym |
| Arlene Francis | place of birth | Boston | Sym |
| Allan Crite | place of death | Boston | Sym |
| Andrea Robbins | place of birth | Boston | Sym |
| Lewis C. Cantley | work location | Boston | UnSym |
| Carl Frederick Burke | place of death | Boston | Sym |
| Harold Hitz Burton | place of birth | Boston | Sym |
| Charles Russell Lowell | place of birth | Boston | Sym |
| David John Scannell | place of birth | Boston | Sym |
| John Thomas | place of birth | Boston | Sym |
| Edward D. Townsend | place of birth | Boston | Sym |
| George Russell | place of death | Boston | Sym |
| Elizabeth Boott | place of birth | Boston | Sym |
| Krister Stendahl | place of death | Boston | Sym |
| Richard Rust | place of birth | Boston | Sym |
| Franklin W. Smith | place of birth | Boston | Sym |
| George Aiken | place of birth | Boston | Sym |
| George Eustis, Sr. | place of birth | Boston | Sym |
| F. Holland Day | place of birth | Boston | Sym |
| Alan Douglas | place of birth | Boston | Sym |
| Carl Mydans | place of birth | Boston | Sym |
| J. Carter Brown | place of death | Boston | Sym |
| Jane F. Barry | place of birth | Boston | Sym |
| John E. Kerrigan | place of death | Boston | Sym |
| John Wilson | place of death | Boston | Sym |
| Joseph P. Lash | place of death | Boston | Sym |
| Joshua Hall Bates | place of birth | Boston | Sym |
| Vladimir Dedijer | place of death | Boston | Sym |
| James Q. Wilson | place of death | Boston | Sym |
| Laura E. Richards | place of birth | Boston | Sym |
| Louis Jean Heydt | place of death | Boston | Sym |
| Man Gone Down | narrative location | Boston | Sym |
| Michelle Citron | place of birth | Boston | Sym |
| John Amaechi | place of birth | Boston | Sym |
| Peter Gammons | place of birth | Boston | Sym |
| Peter Haskell | place of birth | Boston | Sym |
| Piper Kerman | place of birth | Boston | Sym |
| Risa Lavizzo-Mourey | residence | Boston | Sym |
| Susan Butcher | place of birth | Boston | Sym |
| Samuel Gardner Drake | place of death | Boston | Sym |
| Samuel P. Spear | place of birth | Boston | Sym |
| Suffolk University Law School | located in the administrative territorial entity | Boston | Sym |
| Augustus Peabody Gardner | place of birth | Boston | Sym |
| North End | located in the administrative territorial entity | Boston | Sym |
| William Dana Orcutt | place of death | Boston | Sym |
| William J. A. Bailey | place of birth | Boston | Sym |
| Cariddi Nardulli | place of birth | Boston | Sym |
| Charles B. Cory | place of birth | Boston | Sym |
| Charles Francis Adams IV | place of birth | Boston | Sym |
| Lloyd Wheaton Bowers | place of death | Boston | Sym |
| Marian Hooper Adams | place of birth | Boston | Sym |
| James Reese Europe | place of death | Boston | Sym |
| E. J. Dionne | place of birth | Boston | Sym |
| Looking Backward | narrative location | Boston | Sym |
| Ella Lola | place of birth | Boston | Sym |
| Category:Films shot in Boston | category combines topics | Boston | UnSym |
| Erwin Griswold | place of death | Boston | Sym |
| Joseph Pilato | place of birth | Boston | Sym |
| Stanisław Barańczak | place of death | Boston | Sym |
| John Patrick Higgins | place of birth | Boston | Sym |
| John Patrick Higgins | place of death | Boston | Sym |
| John Schuck | place of birth | Boston | Sym |
| Richard Herd | place of birth | Boston | Sym |
| Francis Condon | place of death | Boston | Sym |
| Mark O'Brien | place of birth | Boston | Sym |
| Fred F. Sears | place of birth | Boston | Sym |
| William Moore | place of birth | Boston | Sym |
| Willard MacGregor | place of birth | Boston | Sym |
| George V. Brown | place of birth | Boston | Sym |
| Iron Lore Entertainment | headquarters location | Boston | Sym |
| Gisele Bündchen | residence | Boston | Sym |
| The Handmaid's Tale | narrative location | Boston | Sym |
| Boston subway system | located in the administrative territorial entity | Boston | Sym |
| Edwin May | place of birth | Boston | Sym |
| Winston L. Prouty | place of death | Boston | Sym |
| James T. Bates | place of birth | Boston | Sym |
| Thomas Barbour | place of death | Boston | Sym |
| Jerry Gray | place of birth | Boston | Sym |
| John A. Keliher | place of birth | Boston | Sym |
| John A. Keliher | place of death | Boston | Sym |
| John Locke | place of death | Boston | Sym |
| John W. Candler | place of birth | Boston | Sym |
| Samuel Sewall | place of birth | Boston | Sym |
| Karl Viëtor | place of death | Boston | Sym |
| Billy Yule | place of birth | Boston | Sym |
| Lev Shvarts | residence | Boston | Sym |
| A Recommendation of Inoculation: According to Baron Dimsdale's Method | place of publication | Boston | UnSym |
| An Appeal in Favor of that Class of Americans Called Africans | place of publication | Boston | UnSym |
| Jared Diamond | place of birth | Boston | Sym |
| Richard E. Byrd | place of death | Boston | Sym |
| Samuel Adams | place of death | Boston | Sym |
| Samuel Adams | place of birth | Boston | Sym |
| Roger Hale Sheaffe | place of birth | Boston | Sym |
| William Farnum | place of birth | Boston | Sym |
| Patricia Cornwell | work location | Boston | UnSym |
| Stephen A. Emery | place of death | Boston | Sym |
| Sekondi-Takoradi | twinned administrative body | Boston | Sym |
| Priscilla Morrill | place of birth | Boston | Sym |
| Watermelon Slim | place of birth | Boston | Sym |
| Judith Merril | place of birth | Boston | Sym |
| Oneohtrix Point Never | place of birth | Boston | Sym |
| Myles Kennedy | place of birth | Boston | Sym |
| James Cutler Dunn Parker | place of birth | Boston | Sym |
| Bill Wilson | place of birth | Boston | Sym |
| George Adams Leland | place of death | Boston | Sym |
| George Adams Leland | place of birth | Boston | Sym |
| Anatoly Zhabotinsky | place of death | Boston | Sym |
| Jude | place of birth | Boston | Sym |
| Theodore Sedgwick | place of death | Boston | Sym |
| Morton Prince | place of death | Boston | Sym |
| Morton Prince | place of birth | Boston | Sym |
| Jonathan Sass | work location | Boston | UnSym |
| Dave Lambert | place of birth | Boston | Sym |
| Maxime Bôcher | place of birth | Boston | Sym |
| Roland Hayes | place of death | Boston | Sym |
| George Patton IV | place of birth | Boston | Sym |
| Tara VanDerveer | place of birth | Boston | Sym |
| Josiah Quincy II | place of birth | Boston | Sym |
| Greg Johnston | place of birth | Boston | Sym |
| Jack Nance | place of birth | Boston | Sym |
| Gilbert Stuart | place of death | Boston | Sym |
| Haifa | twinned administrative body | Boston | Sym |
| Leonard Craske | place of death | Boston | Sym |
| Q4340904 | place of birth | Boston | Sym |
| Henry Gardner | place of birth | Boston | Sym |
| James Remar | place of birth | Boston | Sym |
| James Thomas Fields | place of death | Boston | Sym |
| Lucy Stone | place of death | Boston | Sym |
| Chris Nilan | place of birth | Boston | Sym |
| Peter Guralnick | place of birth | Boston | Sym |
| Caroline Zhang | place of birth | Boston | Sym |
| Alexander Hill Everett | place of birth | Boston | Sym |
| Mason Hammond | place of birth | Boston | Sym |
| Ann Smith Franklin | place of birth | Boston | Sym |
| Anthony J. Carson | place of birth | Boston | Sym |
| Anthony J. Carson | place of death | Boston | Sym |
| Anthony T. Shtogren | place of birth | Boston | Sym |
| Benjamin Arthur Quarles | place of birth | Boston | Sym |
| Bill Gillis | place of birth | Boston | Sym |
| Blanche Ring | place of birth | Boston | Sym |
| Bradford Hill | place of birth | Boston | Sym |
| Carl Greenberg | place of birth | Boston | Sym |
| Carlos Castillo | place of birth | Boston | Sym |
| David Lindsay-Abaire | place of birth | Boston | Sym |
| Eliza Lee Cabot Follen | place of birth | Boston | Sym |
| Erastus Brigham Bigelow | place of death | Boston | Sym |
| Gene Lavanchy | place of birth | Boston | Sym |
| George Ferguson | place of birth | Boston | Sym |
| Henry N. Cobb | place of birth | Boston | Sym |
| Henry Percival Dodge | place of birth | Boston | Sym |
| Antoine Joseph Jobin | place of birth | Boston | Sym |
| Jack Concannon | place of birth | Boston | Sym |
| John Ancrum Winslow | place of death | Boston | Sym |
| John F. Kelly | place of birth | Boston | Sym |
| John Henning | place of death | Boston | Sym |
| John Howard | residence | Boston | Sym |
| John Howard | place of birth | Boston | Sym |
| John R. Tunis | place of birth | Boston | Sym |
| Joseph W. Revere | place of birth | Boston | Sym |
| Kahlil Gibran | place of birth | Boston | Sym |
| Kahlil Gibran | place of death | Boston | Sym |
| Helen Johns | place of birth | Boston | Sym |
| Q6627105 | place of death | Boston | Sym |
| Louise Brigham | place of birth | Boston | Sym |
| Mary Ann Vincent | place of death | Boston | Sym |
| Mather Byles | place of birth | Boston | Sym |
| Maud Wood Park | place of birth | Boston | Sym |
| Richard N. Frye | place of death | Boston | Sym |
| Samantha Runnion | place of birth | Boston | Sym |
| Barry Newman | place of birth | Boston | Sym |
| Ben Bradlee | place of birth | Boston | Sym |
| William M. Evarts | place of birth | Boston | Sym |
| Owlchemy Labs | located in the administrative territorial entity | Boston | Sym |
| Coma | narrative location | Boston | Sym |
| Roland Winters | place of birth | Boston | Sym |
| Samantha Logan | place of birth | Boston | Sym |
| Samuel Schafler | place of death | Boston | Sym |
| Sidney Topol | place of birth | Boston | Sym |
| Small Vices | narrative location | Boston | Sym |
| Q761940 | place of death | Boston | Sym |
| William Healey Dall | place of birth | Boston | Sym |
| Thomas Harcourt | place of birth | Boston | Sym |
| Thomas Kilby Smith | place of birth | Boston | Sym |
| Arthur Casagrande | place of death | Boston | Sym |
| Vincent Dethier | place of birth | Boston | Sym |
| William Bradford Turner | place of birth | Boston | Sym |
| Charles A. Dinarello | place of birth | Boston | Sym |
| John Lewis Bates | place of death | Boston | Sym |
| George von Lengerke Meyer | place of birth | Boston | Sym |
| George von Lengerke Meyer | place of death | Boston | Sym |
| John McCarthy | place of birth | Boston | Sym |
| Ezio Levi | place of death | Boston | Sym |
| John Bardeen | place of death | Boston | Sym |
| Jeffrey Davidow | place of birth | Boston | Sym |
| John Michael Higgins | place of birth | Boston | Sym |
| Edward Franklin Bland | place of death | Boston | Sym |
| Rudolph Nissen | work location | Boston | UnSym |
| Charles J. McCarthy | place of birth | Boston | Sym |
| Eugene Roche | place of birth | Boston | Sym |
| Sandy Saddler | place of birth | Boston | Sym |
| Eddie Collins | place of death | Boston | Sym |
| Edward Tuckerman | place of birth | Boston | Sym |
| School of the Museum of Fine Arts, Boston | located in the administrative territorial entity | Boston | Sym |
| Mario Cantone | place of birth | Boston | Sym |
| Albert Vincent Casey | place of birth | Boston | Sym |
| Jaki Byard | place of death | Boston | Sym |
| John Conness | place of death | Boston | Sym |
| Nicky Jam | place of birth | Boston | Sym |
| Fitz-John Winthrop | place of death | Boston | Sym |
| John Charles Phillips | place of birth | Boston | Sym |
| Lew Rockwell | place of birth | Boston | Sym |
| Roman Jakobson | place of death | Boston | Sym |
| Harold Ross | place of death | Boston | Sym |
| Robert Benjamin Lewis | residence | Boston | Sym |
| Herbert Gidney | place of birth | Boston | Sym |
| Samuel L. Crocker | place of death | Boston | Sym |
| Howard Johnson | place of birth | Boston | Sym |
| Jonathan Kale | place of birth | Boston | Sym |
| Jane Colman Turell | place of birth | Boston | Sym |
| John Davenport | place of death | Boston | Sym |
| Joseph Abraham Zilber | place of birth | Boston | Sym |
| Joseph Tuckerman | place of birth | Boston | Sym |
| Leone Lane | place of birth | Boston | Sym |
| Mike Coppola | place of death | Boston | Sym |
| Anita Fuentes | place of birth | Boston | Sym |
| William Wallace Morland | place of death | Boston | Sym |
| Paul X. Kelley | place of birth | Boston | Sym |
| Willard Van Orman Quine | place of death | Boston | Sym |
| Richard Reeve Baxter | place of death | Boston | Sym |
| Mortal Fear | narrative location | Boston | Sym |
| Cid Corman | place of birth | Boston | Sym |
| Medina Dixon | place of birth | Boston | Sym |
| Frank Ross | place of birth | Boston | Sym |
| Blanchard Ryan | place of birth | Boston | Sym |
| Rosemary Kennedy | place of birth | Boston | Sym |
| William Gilson Farlow | place of birth | Boston | Sym |
| Joseph John Ruocco | place of birth | Boston | Sym |
| Marron Curtis Fort | place of birth | Boston | Sym |
| Big Shug | place of birth | Boston | Sym |
| Francisco Goldman | place of birth | Boston | Sym |
| Gaston Chérau | place of death | Boston | Sym |
| James Henry Emerton | place of death | Boston | Sym |
| Kevin Chapman | place of birth | Boston | Sym |
| Bud Blake | place of death | Boston | Sym |
| William L. Shirer | place of death | Boston | Sym |
| Bob Elliott | place of birth | Boston | Sym |
| Michael Ryan | place of birth | Boston | Sym |
| Thomas Curtis | place of birth | Boston | Sym |
| Leon Tuck | place of death | Boston | Sym |
| Oscar Brodney | place of birth | Boston | Sym |
| Chico Scimone | place of birth | Boston | Sym |
| Christopher Seider | place of death | Boston | Sym |
| John Winthrop the Younger | place of death | Boston | Sym |
| Marc Kirschner | work location | Boston | UnSym |
| Charles Francis Adams III | place of death | Boston | Sym |
| Borden Parker Bowne | place of death | Boston | Sym |
| Béla Böszörményi-Nagy | place of death | Boston | Sym |
| David B. Zilberman | place of death | Boston | Sym |
| Joasaph | place of death | Boston | Sym |
| Joasaph | place of birth | Boston | Sym |
| Robert Cormier | place of death | Boston | Sym |
| Rudolʹf Olʹshevskiĭ | place of death | Boston | Sym |
| Robert Walthour | place of death | Boston | Sym |
| Roy Haynes | place of birth | Boston | Sym |
| Lynne Cox | place of birth | Boston | Sym |
| Ada Adini | place of birth | Boston | Sym |
| Amos Lawrence | place of death | Boston | Sym |
| Ann Bauer | place of birth | Boston | Sym |
| B. O. Flower | place of death | Boston | Sym |
| George Wein | place of birth | Boston | Sym |
| Carla DeSantis Black | place of birth | Boston | Sym |
| Frank E. Guernsey | place of death | Boston | Sym |
| Dan Barry | place of birth | Boston | Sym |
| Dan Barry | place of death | Boston | Sym |
| Dana Bullen | place of birth | Boston | Sym |
| Daniel White | place of death | Boston | Sym |
| George Nolfi | place of birth | Boston | Sym |
| Joe Boyd | place of birth | Boston | Sym |
| Donna Loren | place of birth | Boston | Sym |
| Elizabeth Stuart Phelps Ward | place of birth | Boston | Sym |
| Ellen Sturgis Hooper | place of birth | Boston | Sym |
| Fudge Mabeta | place of birth | Boston | Sym |
| Jon Foster | place of birth | Boston | Sym |
| George Dickson | place of birth | Boston | Sym |
| George Lyman Kittredge | place of birth | Boston | Sym |
| Henry Whitney Bellows | place of birth | Boston | Sym |
| Anton Leader | place of birth | Boston | Sym |
| Warren Rudman | place of birth | Boston | Sym |
| Jess Nevins | place of birth | Boston | Sym |
| John Calvin Stevens | place of birth | Boston | Sym |
| John Keefe | place of birth | Boston | Sym |
| John Rock | place of death | Boston | Sym |
| Kelly Lange | place of birth | Boston | Sym |
| Stephen Greenblatt | place of birth | Boston | Sym |
| Lenny Baker | place of birth | Boston | Sym |
| Max Blumenthal | place of birth | Boston | Sym |
| Nancy Garden | place of birth | Boston | Sym |
| Paul Shapiro | place of birth | Boston | Sym |
| Elliot Richardson | place of death | Boston | Sym |
| Elliot Richardson | place of birth | Boston | Sym |
| Q7323156 | place of birth | Boston | Sym |
| Rob Morris | place of birth | Boston | Sym |
| Samuel Crowther | place of death | Boston | Sym |
| Seth Williams | place of death | Boston | Sym |
| Albert Lord | place of birth | Boston | Sym |
| Susan Hale | place of birth | Boston | Sym |
| Massachusetts | capital | Boston | Sym |
| Shirley Clarke | place of death | Boston | Sym |
| Thomas G. Stevenson | place of birth | Boston | Sym |
| Annisa Pohan | place of birth | Boston | Sym |
| Edward Everett Hale | place of birth | Boston | Sym |
| Wally Peterson | place of birth | Boston | Sym |
| William Dummer | place of birth | Boston | Sym |
| William Warren | place of death | Boston | Sym |
| Thomas Bailey Aldrich | place of death | Boston | Sym |
| Jerry Colonna | place of birth | Boston | Sym |
| Brooks Adams | place of death | Boston | Sym |
| Jonathan Roberts | place of birth | Boston | Sym |
| Janet Auchincloss Rutherfurd | place of death | Boston | Sym |
| Charles Bass | place of birth | Boston | Sym |
| Charles Loring Jackson | place of birth | Boston | Sym |
import spark.implicits._
mergedDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
list: List[String] = List(src, rel, dst, srcentid, srclabel)
mergedDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
1,2,3,5,6,9,17,19,33,41
Motif finding for link prediction
In this notebook, we are interested in motif finding for link prediction in a large scale knowledge graph. In particular, we explore the WikiKG dataset which is a directed graph containing 2,500,604 nodes, 16,109,182 edges and 535 relations. Given a motif \(M\) which contains \(n\) nodes and \(m\) edges, we are intereted in the super-motifs of \(M\) which contain \(n\) nodes and \(m+1\) edges. We would expect that finding frequent super-motifs of \(M\) would give insight for link prediction.
Problem Definition
Given a graph \(G=(V,E)\), let \(T^{(n,m)}\) T_nm denote a set of motifs with \(n\) nodes and \(m\) edges and \(N(T_i^{(n,m)})\) N_T_nm_i be the number of subgraphs in \(G\) defined by motifs \(T_i^{(n,m)}\) T_nm_i. \(D(T_i^{(n,m)})\) D_T_nm_i is the number of induced subgraphs defined by motifs \(T_i^{(n,m)}\) in \(G\).
In particular, we are interested in finding motifs with 3 nodes in graph, which are commonly called triads.
Since we are only interested in motifs with 3 nodes here, we set n=3 and simplify our notation as \(T^{(m)}\). \(T={\cup_{k=1}^6 T^{(k)}}\) denotes the set of all possible motifs with 3 nodes and \(|T|=64\). Here we give the problem 1 we are interested in:
Problem 1: Given a motif \(T_i^{(k)}\in T^{(k)}\), we could construct a subset \(T'^{(k+1)} \subset T^{(k+1)}\) by adding a non-existent edge into \(T_i^{(k)}\). For each motif \(t_j \in T'^{(k+1)}\), we define the significance score of \(t_i\) w.r.t \(T_i^{(k)}\) as following:
\[ S(t_j,T_i^{(k)}) = \frac{N(t_j)}{N(T_i^{(k)}) - D(T_i^{(k)})}. \]
Our goal is to find significant motifs in \(T'^{(k+1)}\) according to significance scores.
Lemma 1: Given a motif \(T_i^{(k)}\in T^{(k)}\) and corresponding \(T'^{(k+1)}\), we have:
\[ \sum_{j=1}^{|T'^{(k+1)}|} S(t_j,T_i^{(k)}) = 1 \]
Proof: It is obvious that \(T_i^{(k)}\) is a sub-motif for all motifs in \(T'^{(k+1)}\), when we compute \(N(T_i^{(k)})\) without \(D(T_i^{(k)})\), the number is exactly the summation of \(N(t_j)\).
According to problem 1 and lemma 1, we could get the significance score distribution of \(T'^{(k+1)}\) given motif \(T_i^{(k)}\). We could easily compute this distribution for all motifs in \(T\). This gives us insight of significant motifs for link recommendation or prediction tasks.
Motifs of 3 nodes
In this section, we show all possible motifs with three nodes and give a example of our problem. The following table shows the number of motifs with same edges. Fig. 1 visualize all possible motifs. Following our definition and we take motif 7 in Fig. 1 as a example. \(T_i^{(2)}\) denote motif 7 which contians 2 edges. Now we are interest to find set of its super-motif \(T'^{(k+1)} =\)[8,15,23,39]. If we only consider connected type. Motifs [1,2,3,5,6,9,17,19,33,41] in figure 1 will be ignored. | edges | motifs | super-motifs set| | ---- | ---- | -----| | 0 | 1 | 6*1 | | 1 | 6 | 5 *6 | | 2 | 15 | 4 *15 | | 3 | 20 | 3 *20 | | 4 | 15 | 2 *15 | | 5 | 6 | 1 *6 | | 6 | 1 | 0 |
![]() |
|---|
| Fig. 1 Motifs with 3 nodes |
mergedDf2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
list2: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel)
mergedDF2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
rel_name_df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list3: List[String] = List(relid, label, description)
relnamedf: org.apache.spark.sql.DataFrame = [relid: string, label: string ... 1 more field]
finalDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 7 more fields]
list4: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel, relid, rellabel)
finalDF: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
edgesDF_: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
list5: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
verticesDf: org.apache.spark.sql.DataFrame = [id: string]
graph: org.graphframes.GraphFrame = GraphFrame(v:[id: string], e:[src: string, dst: string ... 1 more field])
Case Study : Motif 7 and its super motifs
Now we focus on motif 7 and its corresponding super motif set. To be specific, we have \(T_7^{(2)}\) and \(T'^{(3)} = [T_0^{(3)}, T_1^{(3)}, T_2^{(3)}, T_3^{(3)} ]\). - We find motifs in graph and use count to see most significant motifs in super-motif set. In this phase, we ignore relationship typpe and only look at graph structure information defined by motifs. - Then we dive in different motifs by limiting edge relationship. For example, given motif ["(a)-[spouse]->(b); (b)-[child]->(c)"], What kind of relationship should we write for r3 in [a-[r3]->c]? It is natual to say that r3 is child. Based on the statistic information that we get from motif counting. We could get the probability distribution of super motifs conditioning on relationship. And give us relable guess for missing nodes.
// example: Motif 7 and its super motif sets. Our goal is to find significant relations.
val motif_7 = "(a)-[r1]->(b); (b)-[r2]->(c)"
val motif_7_super_motifs = List("(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(a)","(a)-[r1]->(b); (b)-[r2]->(c); (a)-[r3]->(c)", "(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(b)", "(a)-[r1]->(b); (b)-[r2]->(c); (b)-[r3]->(a)")
motif_7: String = (a)-[r1]->(b); (b)-[r2]->(c)
motif_7_super_motifs: List[String] = List((a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(a), (a)-[r1]->(b); (b)-[r2]->(c); (a)-[r3]->(c), (a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(b), (a)-[r1]->(b); (b)-[r2]->(c); (b)-[r3]->(a))
// Example 1, to look at motif_7 and and motif_7_supper_motif_7[1].
val motif_7_result = graph.find(motif_7)
val motif_7_super_motif_0 = graph.find(motif_7_super_motifs(0))
val motif_7_super_motif_1 = graph.find(motif_7_super_motifs(1))
val motif_7_super_motif_2 = graph.find(motif_7_super_motifs(2))
val motif_7_super_motif_3 = graph.find(motif_7_super_motifs(3))
motif_7_result: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 3 more fields]
motif_7_super_motif_0: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 4 more fields]
motif_7_super_motif_1: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 4 more fields]
motif_7_super_motif_2: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 4 more fields]
motif_7_super_motif_3: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 4 more fields]
display(motif_7_result)
// get motif count for motif and corresponding super motif set.
val motif_7_count = motif_7_result.count()
motif_7_count: Long = 461186877
val motif_7_super_motif_0_count = motif_7_super_motif_0.cache().count()
val motif_7_super_motif_1_count = motif_7_super_motif_1.cache().count()
val motif_7_super_motif_2_count = motif_7_super_motif_2.cache().count()
val motif_7_super_motif_3_count = motif_7_super_motif_3.cache().count()
println(motif_7_count," ",motif_7_super_motif_0_count, " ", motif_7_super_motif_1_count," ", motif_7_super_motif_2_count, " ",motif_7_super_motif_3_count)
val motif_7_r1 = motif_7_result.filter("r1.rel=='child'")
display(motif_7_r1)
val motif_7_r2 = motif_7_result.filter("r2.rel=='child'")
display(motif_7_r2)
val motif_7_super_motif_1_r1_r3 = motif_7_super_motif_2.filter("r1.rel=='child' and r3.rel=='child' and a.id!=c.id")
display(motif_7_super_motif_2_r1_r3)
display(motif_7_super_motif_1)
import org.apache.spark.sql.DataFrame
// T_32 := includes all 3 edges 2 vertice combination
val T_32_Query_List = Seq("(a)-[r1]->(b); (b)-[r2]->(c)", "(b)-[r1]->(a); (b)-[r2]->(c)", "(b)-[r1]->(a); (c)-[r2]->(a)")
// Input: A list of query describe T_mn: Seq[String]
// Output: A dataframe [var1, rel, var2,...], for T_mn: Dataframe
def generate_T_mn_dataframe_pipline(T_mn_Query_List : Seq[String]): DataFrame = {
// ref: https://stackoverflow.com/a/37612978
val dfs = T_mn_Query_List.map(query => graph.find(query))
val result_df = dfs.reduce(_ unionByName _).distinct
return result_df
}
// It is not applicable to use a loopy pass to union subgraph from different query: Spark have to define the column name before a loopy union, making such solution not feasible for random length of per query length
// val T_mn_Df = spark.emptyDataFrame
// for( x <- T_mn_Query_List ){
// // Union the query output with previous/empty dataframe
// // Union removes duplication, see: https://stackoverflow.com/questions/52494653/union-does-not-remove-duplicate-rows-in-spark-data-frame
// T_mn_Df.unionByName(graph.find(x), true);
// }
// BTW, This is suprisingly slow
// if (graph.find(x).isEmpty){
// println("Query get no result,: ", x)
// }
import org.apache.spark.sql.DataFrame
T_32_Query_List: Seq[String] = List((a)-[r1]->(b); (b)-[r2]->(c), (b)-[r1]->(a); (b)-[r2]->(c), (b)-[r1]->(a); (c)-[r2]->(a))
generate_T_mn_dataframe_pipline: (T_mn_Query_List: Seq[String])org.apache.spark.sql.DataFrame
val fruits = List(
"apple",
"orange",
"melon"
)
val df_test = fruits
.map(x => ("aaa", "bbb", x)).toDF("aCol", "bCol", "name")
val df_seq = Seq(df_test,df_test,df_test)
val reduced_df = df_seq.reduce(_ unionByName _)
fruits: List[String] = List(apple, orange, melon)
df_test: org.apache.spark.sql.DataFrame = [aCol: string, bCol: string ... 1 more field]
df_seq: Seq[org.apache.spark.sql.DataFrame] = List([aCol: string, bCol: string ... 1 more field], [aCol: string, bCol: string ... 1 more field], [aCol: string, bCol: string ... 1 more field])
display(reduced_df)
| aCol | bCol | name |
|---|---|---|
| aaa | bbb | apple |
| aaa | bbb | orange |
| aaa | bbb | melon |
| aaa | bbb | apple |
| aaa | bbb | orange |
| aaa | bbb | melon |
| aaa | bbb | apple |
| aaa | bbb | orange |
| aaa | bbb | melon |
val dock = generate_T_mn_dataframe_pipline (T_32_Query_List)
display(dock)
val test = spark.emptyDataFrame
val test = test.unionByName(graph.find("(a)-[r1]->(b); (b)-[r2]->(c)"), true);
var dfsLen = 0;
{
var dfs = Array[Any]()
implicit def display(df: Any) {
dfs = dfs :+ df
}
// run user code
display(motif_7_result)
if (dfs.length > 0) {
val userGenerateDf = dfs(0).asInstanceOf[org.apache.spark.sql.DataFrame]
userGenerateDf.createOrReplaceTempView("DatabricksView54870da")
}
dfsLen = dfs.length
}
if (dfsLen > 0) {
try {
display(sql("""WITH q AS (select * from DatabricksView54870da) SELECT 1 FROM q GROUP BY GROUPING SETS (())"""))
} finally {
// cleaning up the view helps us not pollute the name space
spark.sql("drop view if exists DatabricksView54870da")
}
} else {
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
}
Algorithms
Here we give the functions for motif finding and visualization.
// test
Analysis
Here we give quantatitive analysis based on motif finding results and visualize important information.
Analysis using python
In this notebook, we will try to do some analyses of the graph by using python instead of scala.
import pandas as pd
df = spark.read.csv("dbfs:///wikikg-v2/original/train_2015.csv.gz", header=True, inferSchema=True)
import spark.implicits._
import org.graphframes._
df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
df1: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
entdescdf: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
import spark.implicits._
mergedDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
list: List[String] = List(src, rel, dst, srcentid, srclabel)
mergedDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
mergedDf2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
list2: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel)
mergedDF2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
rel_name_df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list3: List[String] = List(relid, label, description)
relnamedf: org.apache.spark.sql.DataFrame = [relid: string, label: string ... 1 more field]
finalDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 7 more fields]
list4: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel, relid, rellabel)
finalDF: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
edgesDF_: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
list5: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
verticesDf: org.apache.spark.sql.DataFrame = [id: string]
graph: org.graphframes.GraphFrame = GraphFrame(v:[id: string], e:[src: string, dst: string ... 1 more field])
PageRank Embeddings
In this notebook, we want to investigate the closest entities and determine how close some entities are to others.
To achieve this, we utilize the personalised page rank algorithm. We create vectors based on the personalized pagerank probabilities from some random nodes. The method is as follows.
- Pick 100 random "anchors" in the graph. These will be nodes chosen at random, which shall together represent "coordinates" or positions in the graph.
- For each anchor, we run personalized pagerank, and get a personalized pagerank score for every node. This gives a view of the distance between each anchor and every node. This method therefore generates a score.
./02_load_data
import org.apache.spark.sql.functions.rand
// Set up anchors
val n_anchors = 100
val anchors = graph.vertices.orderBy(rand()).limit(n_anchors)
import org.apache.spark.sql.functions.rand
n_anchors: Int = 100
anchors: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string]
val results = graph.parallelPersonalizedPageRank.resetProbability(0.15).maxIter(10).sourceIds(anchors.select("id").collect.map(_.getString(0))).run()
results: org.graphframes.GraphFrame = GraphFrame(v:[id: string, pageranks: vector], e:[src: string, dst: string ... 2 more fields])
display(results)
display(results.vertices)
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.feature.VectorAssembler
// https://stackoverflow.com/questions/44956855/filter-a-features-column-of-vector-type
def vectors_unequal(vec1: Vector) = udf((vec2: Vector) => !vec1.equals(vec2))
val va = new VectorAssembler().setInputCols(Array("pageranks")).setOutputCol("vector").transform(results.vertices)
val vecToRemove = Vectors.dense(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
val filtered2 = results.vertices.filter(vectors_unequal(vecToRemove)($"vector")) // Also possible
//filtered2.write.format('csv').option('header',True).mode('overwrite').option('sep','|').save('dbfs:///wikikg-v2/graphframevertices.csv')
results.vertices.map { row => (row.getString("id"), "[" + row.getAs[org.apache.spark.ml.linalg.Vector("pageranks").toArray.mkString(",") + "]") }).write.option("header", true).csv("dbfs:///wikikg-v2/graphframevertices.csv")
import org.apache.spark.ml.functions.vector_to_array
results.vertices.select(vector_to_array($"pageranks").alias("_tmp")).select(exprs:_*)
import org.apache.spark.sql.functions.udf
// In Spark 1.x you'll will have to replace ML Vector with MLLib one
// import org.apache.spark.mllib.linalg.Vector
// In 2.x the below is usually the right choice
import org.apache.spark.ml.linalg.Vector
// Get size of the vector
val n = results.vertices.first.getAs[Vector](0).size
// Simple helper to convert vector to array<double>
// asNondeterministic is available in Spark 2.3 or befor
// It can be removed, but at the cost of decreased performance
val vecToSeq = udf((v: Vector) => v.toArray).asNondeterministic
// Prepare a list of columns to create
val exprs = (0 until n).map(i => $"_tmp".getItem(i).alias(s"f$i"))
results.vertices.select(vecToSeq($"scaledFeatures").alias("_tmp")).select(exprs:_*).write.option("header", true).csv("dbfs:///wikikg-v2/graphframevertices.csv")
results
Discussion
This notebook collects some of our reflections about the project and working with Spark.
While Spark is highly effective at some processes, it was not with ours. We ran into scaling issues, where most operations took hours; even simple ones. This probably was due to an effect of many people using the cluster simultaneously, but this hindered us quite a lot in our development process. As an example, simply filtering a fews vectors was an operation that took several hours, which hindered us from a lot of fast iterations and development, and the fact that all of us were new to Scala did not contribute positively either.
./02_load_data
import spark.implicits._
import org.graphframes._
Preprocessing
- Here we inherent previous notebooks varible.
- We add columns of Symmetricity for Motif finding
df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
val twoCycles = graph.find("(a)-[r1]->(b); (b)-[r2]->(a)")
import sqlContext.implicits._ // for $""
import org.apache.spark.sql.functions._ // for `when`
// IF r1 === r2, Label rel as relSym
val twoCycles_w_sym = twoCycles.withColumn("relSym", when($"r1"("rel") === $"r2"("rel"), "Sym").otherwise("UnSym"))
twoCycles: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 2 more fields]
import sqlContext.implicits._
import org.apache.spark.sql.functions._
twoCycles_w_sym: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 3 more fields]
df1: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
entdescdf: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
val symtype = twoCycles_w_sym.filter(twoCycles_w_sym("relSym") === "Sym").select("r1.rel","relSym").groupBy("rel").count().cache()
val list_sym= symtype.select("rel").map(f=>f.getString(0)).collect.toList
val edgesDF_w_sym = edgesDF.withColumn("relSym", when($"rel".isin(list_sym: _*), "Sym").otherwise("UnSym"))
display(edgesDF_w_sym)
| src | rel | dst | relSym |
|---|---|---|---|
| Alfred Hauptmann | place of death | Boston | Sym |
| David Louis Band | place of birth | Boston | Sym |
| John Boyle O'Reilly | place of death | Boston | Sym |
| James Seanor | place of birth | Boston | Sym |
| Aerosmith | location of formation | Boston | UnSym |
| Michael Joseph McEttrick | place of death | Boston | Sym |
| Eduardo Catalano | place of death | Boston | Sym |
| Tristram Dalton | place of death | Boston | Sym |
| Turk Van Lake | place of birth | Boston | Sym |
| Sherman Miles | place of death | Boston | Sym |
| James Abercrombie | place of death | Boston | Sym |
| Derek B. Miller | place of birth | Boston | Sym |
| Nerine Kidd | place of birth | Boston | Sym |
| Kenza Tazi | place of birth | Boston | Sym |
| Q16580475 | place of birth | Boston | Sym |
| Charles Pomeroy Parker | place of birth | Boston | Sym |
| Christiana Carteaux Bannister | residence | Boston | Sym |
| Proctor L. Dougherty | place of birth | Boston | Sym |
| Larry Goldings | place of birth | Boston | Sym |
| Lillie P. Bliss | place of birth | Boston | Sym |
| Amos Clark, Jr. | place of death | Boston | Sym |
| Taipei | twinned administrative body | Boston | Sym |
| John E. Rexine | place of birth | Boston | Sym |
| Thomas Hutchinson | place of birth | Boston | Sym |
| Tip O'Neill | place of death | Boston | Sym |
| Emily Greene Balch | place of birth | Boston | Sym |
| Robert Alfred Theobald | place of death | Boston | Sym |
| Robert Semple | place of birth | Boston | Sym |
| Walter Gilbert | place of birth | Boston | Sym |
| John G. Palfrey | place of birth | Boston | Sym |
| Tom Jennings | place of birth | Boston | Sym |
| A Drink Before the War | narrative location | Boston | Sym |
| Robert Wahlberg | place of birth | Boston | Sym |
| Susan Delano McKelvey | place of death | Boston | Sym |
| Charles Sweeney | place of death | Boston | Sym |
| Carolina Barco | place of birth | Boston | Sym |
| Yehuda Krinsky | place of birth | Boston | Sym |
| Christopher Edley, Jr. | place of birth | Boston | Sym |
| Edward M. Kennedy Jr. | place of birth | Boston | Sym |
| Abbott Lawrence | place of death | Boston | Sym |
| John Quelch | place of death | Boston | Sym |
| Albert Sauveur | place of death | Boston | Sym |
| Matthew Sullivan | place of birth | Boston | Sym |
| Predator | narrative location | Boston | Sym |
| Thurston Hall | place of birth | Boston | Sym |
| William Troy | place of birth | Boston | Sym |
| Charles Henry Turner | place of death | Boston | Sym |
| Douglas Tybor Durig | place of birth | Boston | Sym |
| John William McCormack | place of birth | Boston | Sym |
| Jennifer Jostyn | place of birth | Boston | Sym |
| William C. Durant | place of birth | Boston | Sym |
| George Cabot | place of death | Boston | Sym |
| Marianne Leone Cooper | place of birth | Boston | Sym |
| Mario Grossi | place of death | Boston | Sym |
| Sib Hashian | place of birth | Boston | Sym |
| Susan Minot | place of birth | Boston | Sym |
| Mikhail Danilov | place of death | Boston | Sym |
| Lexi Love | place of birth | Boston | Sym |
| John M. Flynn | place of birth | Boston | Sym |
| Rita Hester | place of death | Boston | Sym |
| Nathaniel Carl Goodwin | place of birth | Boston | Sym |
| Isabella Stewart Gardner | place of death | Boston | Sym |
| Washington Allston | work location | Boston | UnSym |
| Richard Sears | place of death | Boston | Sym |
| Richard Sears | place of birth | Boston | Sym |
| Meg Rosoff | place of birth | Boston | Sym |
| Andras Angyal | place of death | Boston | Sym |
| Anthony Shriver | place of birth | Boston | Sym |
| Austin Edward Ford | place of birth | Boston | Sym |
| Barbara McMartin | place of birth | Boston | Sym |
| Benjamin Edes | place of death | Boston | Sym |
| Boston City Council | applies to jurisdiction | Boston | UnSym |
| Henry Marsh | place of birth | Boston | Sym |
| Charles Henry Davis | place of birth | Boston | Sym |
| Charles R. Codman | place of death | Boston | Sym |
| Charles R. Codman | place of birth | Boston | Sym |
| Charles Sawyer Russell | place of birth | Boston | Sym |
| Charles W. Bailey | place of birth | Boston | Sym |
| Chrystal Herne | place of death | Boston | Sym |
| Gretchen Merrill | place of birth | Boston | Sym |
| Crystal Bird Fauset | residence | Boston | Sym |
| Lawrence Whitney | place of death | Boston | Sym |
| Dave "Chico" Ryan | place of death | Boston | Sym |
| David Herbert Donald | place of death | Boston | Sym |
| David Shiner | place of birth | Boston | Sym |
| Frank Knox | place of birth | Boston | Sym |
| Francis Woodman Cleaves | place of birth | Boston | Sym |
| Arthur Berger | place of death | Boston | Sym |
| Bill Holland | place of birth | Boston | Sym |
| Richard Cushing | place of death | Boston | Sym |
| Richard Cushing | place of birth | Boston | Sym |
| Gene Wood | place of death | Boston | Sym |
| William Monahan | place of birth | Boston | Sym |
| James D. Morgan | place of birth | Boston | Sym |
| Padua | twinned administrative body | Boston | Sym |
| Joe Chiccarelli | place of birth | Boston | Sym |
| Richard Armitage | place of birth | Boston | Sym |
| John Gibson | place of birth | Boston | Sym |
| John Marston | place of birth | Boston | Sym |
| Joseph Henry Beale | place of birth | Boston | Sym |
| Erna Lazarus | place of birth | Boston | Sym |
| Peter Abrahams | place of birth | Boston | Sym |
| Lucianne Goldberg | place of birth | Boston | Sym |
| Bill Laimbeer | place of birth | Boston | Sym |
| Paul Coyne | place of birth | Boston | Sym |
| Percy Jewett Burrell | place of birth | Boston | Sym |
| Frank Synott | place of death | Boston | Sym |
| Eric Griffin | place of birth | Boston | Sym |
| Rebecca Eaton | place of birth | Boston | Sym |
| Richard Saltonstall Greenough | place of birth | Boston | Sym |
| Robert Cowdin | place of death | Boston | Sym |
| Robert Rimmer | place of birth | Boston | Sym |
| Sara Wilford | place of birth | Boston | Sym |
| Sheanon Williams | place of birth | Boston | Sym |
| Category:Films set in Boston | category combines topics | Boston | UnSym |
| Chris Cormier | place of birth | Boston | Sym |
| The Holder of the World | narrative location | Boston | Sym |
| Therese Murray | place of birth | Boston | Sym |
| Thomas G. Kelley | place of birth | Boston | Sym |
| Thomas Savage | place of death | Boston | Sym |
| Ernst Badian | place of death | Boston | Sym |
| Wadsworth Harris | place of birth | Boston | Sym |
| Wallace Tripp | place of birth | Boston | Sym |
| William Cooper Nell | place of death | Boston | Sym |
| William Cooper Nell | place of birth | Boston | Sym |
| Richard von Mises | place of death | Boston | Sym |
| David Ignatius Walsh | place of death | Boston | Sym |
| Borah Bergman | place of death | Boston | Sym |
| Luis T. Romero | place of death | Boston | Sym |
| Henry Gilman | place of birth | Boston | Sym |
| John Ciardi | place of birth | Boston | Sym |
| Peter MacKenzie | place of birth | Boston | Sym |
| Carl McKinley | place of death | Boston | Sym |
| Charles Edward Adams | place of birth | Boston | Sym |
| Brian Fair | place of birth | Boston | Sym |
| Mario Corti | residence | Boston | Sym |
| Georg Klemperer | place of death | Boston | Sym |
| Frances Wayne | place of death | Boston | Sym |
| Frances Wayne | place of birth | Boston | Sym |
| Edward Norton | place of birth | Boston | Sym |
| Jimmy McHugh | place of birth | Boston | Sym |
| John L. Blake | place of birth | Boston | Sym |
| Sylvia Plath | residence | Boston | Sym |
| Sylvia Plath | place of birth | Boston | Sym |
| George Underwood | place of death | Boston | Sym |
| William Billings | place of birth | Boston | Sym |
| William Billings | place of death | Boston | Sym |
| Terrayne Crawford | place of birth | Boston | Sym |
| Boston Consulting Group | headquarters location | Boston | Sym |
| Frederick Wiseman | place of birth | Boston | Sym |
| William Stimpson | place of birth | Boston | Sym |
| Financial District | located in the administrative territorial entity | Boston | Sym |
| Frederic Woodman Root | place of birth | Boston | Sym |
| Horace Brooks | place of birth | Boston | Sym |
| Richard J. Kerry | place of death | Boston | Sym |
| Lisa Niver Rajna | place of birth | Boston | Sym |
| George Harrington | place of birth | Boston | Sym |
| James MacGregor Burns | place of birth | Boston | Sym |
| Harrison Henry Atwood | place of death | Boston | Sym |
| James G. Maguire | place of birth | Boston | Sym |
| Edgar Allan Poe | place of birth | Boston | Sym |
| Joe Maneri | place of death | Boston | Sym |
| Sean Gullette | place of birth | Boston | Sym |
| Frederick Stevens | place of birth | Boston | Sym |
| William Perkins Babcock | place of birth | Boston | Sym |
| Eric S. Raymond | place of birth | Boston | Sym |
| Henry Way Kendall | place of birth | Boston | Sym |
| Norma Farber | place of birth | Boston | Sym |
| Robert Joseph Banks | place of birth | Boston | Sym |
| Rodman Philbrick | place of birth | Boston | Sym |
| Allison Janney | place of birth | Boston | Sym |
| Christopher Allport | place of birth | Boston | Sym |
| Eric Turner | place of birth | Boston | Sym |
| Evan Dando | place of birth | Boston | Sym |
| Lawrence Joseph Riley | place of birth | Boston | Sym |
| Barbara Delinsky | place of birth | Boston | Sym |
| Kristin Cashore | place of birth | Boston | Sym |
| Kristin Cashore | work location | Boston | UnSym |
| William James Sidis | place of death | Boston | Sym |
| Melbourne | twinned administrative body | Boston | Sym |
| John Singleton Copley | place of birth | Boston | Sym |
| Joseph Badger | place of death | Boston | Sym |
| Kate Collins | place of birth | Boston | Sym |
| Abigail Johnson | residence | Boston | Sym |
| William Hill Brown | place of birth | Boston | Sym |
| Ted Drury | place of birth | Boston | Sym |
| Marc Ferrari | place of birth | Boston | Sym |
| Kenneth O'Donnell | place of death | Boston | Sym |
| Rich Hill | place of birth | Boston | Sym |
| Increase Sumner | place of death | Boston | Sym |
| Ricky Ford | place of birth | Boston | Sym |
| Charles Devens | place of death | Boston | Sym |
| George Jung | place of birth | Boston | Sym |
| T. J. Thyne | place of birth | Boston | Sym |
| Courtney Eldridge | place of birth | Boston | Sym |
| Ian Biederman | place of birth | Boston | Sym |
| Jimmy Flynn | place of birth | Boston | Sym |
| Aidan Mitchell | place of birth | Boston | Sym |
| Guinevere Turner | place of birth | Boston | Sym |
| Jeanie MacPherson | place of birth | Boston | Sym |
| Henry Brooks Adams | place of birth | Boston | Sym |
| Susanna Rowson | place of death | Boston | Sym |
| Anita Shreve | work location | Boston | UnSym |
| Alan Trefler | place of birth | Boston | Sym |
| Alexander Bradley | place of birth | Boston | Sym |
| Alexander Leaf | place of death | Boston | Sym |
| Alicyn Packard | place of birth | Boston | Sym |
| Beals Coleman Wright | place of birth | Boston | Sym |
| Andrei Zelevinsky | place of death | Boston | Sym |
| Carmen Filpi | place of birth | Boston | Sym |
| Caspar Crowninshield | place of birth | Boston | Sym |
| Caspar Crowninshield | place of death | Boston | Sym |
| William Hickling Prescott | place of death | Boston | Sym |
| Elihu Yale | place of birth | Boston | Sym |
| Craig Ross, Jr. | place of birth | Boston | Sym |
| Daniel Lothrop | place of death | Boston | Sym |
| David Barstow | place of birth | Boston | Sym |
| Dick Kazmaier | place of death | Boston | Sym |
| Janet Tashjian | place of birth | Boston | Sym |
| Emma Nutt | place of birth | Boston | Sym |
| Faith Salie | place of birth | Boston | Sym |
| Frank Newcomb | place of birth | Boston | Sym |
| Freeman Gill | place of birth | Boston | Sym |
| Geoff Edgers | place of birth | Boston | Sym |
| George D. Murray | place of birth | Boston | Sym |
| George W. Casey, Sr. | place of birth | Boston | Sym |
| Henry A. Miley, Jr. | place of birth | Boston | Sym |
| John Andrew Sullivan | place of birth | Boston | Sym |
| Samuel Dexter | place of birth | Boston | Sym |
| Joe Gould | place of birth | Boston | Sym |
| James Fowle Baldwin | place of death | Boston | Sym |
| Jerry Williams | place of death | Boston | Sym |
| John Andrew Barnes III | place of birth | Boston | Sym |
| John Frykman | place of birth | Boston | Sym |
| Jonathan Sewall | place of birth | Boston | Sym |
| Archibald Thompson Davison | place of birth | Boston | Sym |
| George Peter Alexander Healy | place of birth | Boston | Sym |
| Lea Wait | place of birth | Boston | Sym |
| Margaret Green Draper | place of birth | Boston | Sym |
| Mark Goulston | place of birth | Boston | Sym |
| Mary Lou Clements-Mann | place of birth | Boston | Sym |
| Melvin Johnson | place of birth | Boston | Sym |
| Michael DeSisto | place of death | Boston | Sym |
| Michael DeSisto | place of birth | Boston | Sym |
| Moses Roper | place of death | Boston | Sym |
| Nathaniel S. Keith | place of birth | Boston | Sym |
| Norman Foster | place of birth | Boston | Sym |
| Arthur Duffey | place of death | Boston | Sym |
| Minor White | place of death | Boston | Sym |
| Paul Van Doren | place of birth | Boston | Sym |
| Chris Burden | place of birth | Boston | Sym |
| Patrick Renna | place of birth | Boston | Sym |
| Augustus Addison Gould | place of death | Boston | Sym |
| William Henry Lewis | place of death | Boston | Sym |
| George P. Wetmore | place of death | Boston | Sym |
| Kenny Wormald | place of birth | Boston | Sym |
| L Peter Deutsch | place of birth | Boston | Sym |
| Edwin O'Connor | place of death | Boston | Sym |
| Justin Winsor | place of birth | Boston | Sym |
| Catherine Filene Shouse | place of birth | Boston | Sym |
| The Dante Club | narrative location | Boston | Sym |
| Charles Francis Adams, Jr. | place of birth | Boston | Sym |
| Richard Evans Schultes | place of death | Boston | Sym |
| Richard Evans Schultes | place of birth | Boston | Sym |
| Lorna Thayer | place of birth | Boston | Sym |
| Frank Stanton | place of death | Boston | Sym |
| Kerin O'Keefe | place of birth | Boston | Sym |
| Joey Oakes Palfrey | place of birth | Boston | Sym |
| Andrejs Zeidaks | place of death | Boston | Sym |
| Robert Charles Winthrop | place of death | Boston | Sym |
| Robert Charles Winthrop | place of birth | Boston | Sym |
| Frederick G. Katzmann | place of death | Boston | Sym |
| Frederick G. Katzmann | place of birth | Boston | Sym |
| Phil Rasmussen | place of birth | Boston | Sym |
| Gerry Studds | place of death | Boston | Sym |
| Jason Chen | place of birth | Boston | Sym |
| Roger Adams | place of birth | Boston | Sym |
| Mark Leibovich | place of birth | Boston | Sym |
| Q15987713 | place of birth | Boston | Sym |
| Mark Wahlberg | place of birth | Boston | Sym |
| Harold J. Greene | place of birth | Boston | Sym |
| Joseph E. Levine | place of birth | Boston | Sym |
| On Beauty | narrative location | Boston | Sym |
| Lawrence J. Hogan | place of birth | Boston | Sym |
| Daniel Dennett | place of birth | Boston | Sym |
| John Boswell | place of birth | Boston | Sym |
| A Case of Need | narrative location | Boston | Sym |
| William F. Sharpe | place of birth | Boston | Sym |
| Alex Cobb | place of birth | Boston | Sym |
| Brian Noonan | place of birth | Boston | Sym |
| Charles Codman | place of birth | Boston | Sym |
| Richard N. Goodwin | place of birth | Boston | Sym |
| Benjamin Franklin | place of birth | Boston | Sym |
| William Stowell | place of birth | Boston | Sym |
| Bill Collins | place of birth | Boston | Sym |
| Makanda Ken McIntyre | place of birth | Boston | Sym |
| Edwin Richards | place of birth | Boston | Sym |
| Edwin Lord Weeks | place of birth | Boston | Sym |
| Russ Lee | place of birth | Boston | Sym |
| Frank Dayton | place of birth | Boston | Sym |
| Thomas Dudley | place of death | Boston | Sym |
| James Fitzgerald | place of birth | Boston | Sym |
| Gone, Baby, Gone | narrative location | Boston | Sym |
| Dana Barros | place of birth | Boston | Sym |
| Richard Mayo | place of birth | Boston | Sym |
| Ned Dowd | place of birth | Boston | Sym |
| Armand Van Helden | place of birth | Boston | Sym |
| Helen McCloy | place of death | Boston | Sym |
| Terri Lyne Carrington | work location | Boston | UnSym |
| Anatole Broyard | place of death | Boston | Sym |
| Bob Backus | place of birth | Boston | Sym |
| Bob Durgin | place of birth | Boston | Sym |
| James Bryant Conant | place of birth | Boston | Sym |
| Curt Conway | place of birth | Boston | Sym |
| Claudia Rueda | work location | Boston | UnSym |
| David Evans | place of death | Boston | Sym |
| Francis E. Kelly | place of birth | Boston | Sym |
| George Heyliger | place of birth | Boston | Sym |
| George Melville Baker | residence | Boston | Sym |
| Gladys Walton | place of birth | Boston | Sym |
| Henry Hendrickson | place of death | Boston | Sym |
| Jamie Denbo | place of birth | Boston | Sym |
| Jamie Turndorf | place of birth | Boston | Sym |
| Jed Prouty | place of birth | Boston | Sym |
| John D. Harvey | place of birth | Boston | Sym |
| Leonard Chadwick | place of death | Boston | Sym |
| Louis Kronberg | place of birth | Boston | Sym |
| Carroll Quigley | place of birth | Boston | Sym |
| Oliver Winchester | place of birth | Boston | Sym |
| Shadow Fox | narrative location | Boston | Sym |
| The Namesake | narrative location | Boston | Sym |
| Thomas Finneran | place of birth | Boston | Sym |
| Thomas Cushing | place of death | Boston | Sym |
| Károly Bartha | place of death | Boston | Sym |
| William Emerson | place of death | Boston | Sym |
| William Sweeney | place of birth | Boston | Sym |
| Boylston Street | located in the administrative territorial entity | Boston | Sym |
| Josiah P. Cooke | place of birth | Boston | Sym |
| Saul Rosenzweig | place of birth | Boston | Sym |
| Theodore Lyman | place of birth | Boston | Sym |
| Humberto Sousa Medeiros | place of death | Boston | Sym |
| Noah Bean | place of birth | Boston | Sym |
| Burton Pike | place of birth | Boston | Sym |
| Charles Bulfinch | place of death | Boston | Sym |
| Charles Bulfinch | place of birth | Boston | Sym |
| Chuckie Taylor | place of birth | Boston | Sym |
| Boston Marathon bombings | located in the administrative territorial entity | Boston | Sym |
| Milt Raskin | place of birth | Boston | Sym |
| Patrick Ewing, Jr. | place of birth | Boston | Sym |
| Edith Fellows | place of birth | Boston | Sym |
| James Pierpont | place of birth | Boston | Sym |
| Mianne Palfrey | place of birth | Boston | Sym |
| Jill Tasker | place of birth | Boston | Sym |
| Hugh S. Legaré | place of death | Boston | Sym |
| Henry E. Dixey | place of birth | Boston | Sym |
| John Smith | place of birth | Boston | Sym |
| Gerry Connolly | place of birth | Boston | Sym |
| Hugo Rossi | place of birth | Boston | Sym |
| Godfrey Lowell Cabot | place of death | Boston | Sym |
| Godfrey Lowell Cabot | place of birth | Boston | Sym |
| Timothy Davis | place of death | Boston | Sym |
| Thomas Gamaliel Bradford | place of birth | Boston | Sym |
| Henry Pickering Bowditch | place of death | Boston | Sym |
| Henry Pickering Bowditch | place of birth | Boston | Sym |
| Madeleine M. Joullié | residence | Boston | Sym |
| Joanne Simpson | place of birth | Boston | Sym |
| John Henry Willcox | place of death | Boston | Sym |
| Busty Heart | place of birth | Boston | Sym |
| Lawrence Berk | place of birth | Boston | Sym |
| Max Bondy | place of death | Boston | Sym |
| Baruch Marzel | place of birth | Boston | Sym |
| Peleg Coffin, Jr. | place of death | Boston | Sym |
| Peter A. Garland | place of birth | Boston | Sym |
| Tabitha St. Germain | place of birth | Boston | Sym |
| Allan H. Meltzer | place of birth | Boston | Sym |
| Felix Wolfes | place of death | Boston | Sym |
| William P. Murphy Jr. | place of birth | Boston | Sym |
| Geraldine Ferraro | place of death | Boston | Sym |
| Susan Bottomly | place of birth | Boston | Sym |
| Anne Dudek | place of birth | Boston | Sym |
| Edith Nourse Rogers | place of death | Boston | Sym |
| Thomas D. Eliot | place of birth | Boston | Sym |
| Paul Stanton | place of birth | Boston | Sym |
| Al Vega | place of death | Boston | Sym |
| Albert Smith | place of death | Boston | Sym |
| Eric Francis MacKenzie | place of birth | Boston | Sym |
| Michael McDowell | place of death | Boston | Sym |
| Jack Levine | place of birth | Boston | Sym |
| Donald Schön | place of birth | Boston | Sym |
| Dorothy Loudon | place of birth | Boston | Sym |
| Manny Delcarmen | place of birth | Boston | Sym |
| John Pitcairn | place of death | Boston | Sym |
| Eugene Braunwald | work location | Boston | UnSym |
| Joseph Grew | place of birth | Boston | Sym |
| Seán McKiernan | place of birth | Boston | Sym |
| Darren Turcotte | place of birth | Boston | Sym |
| Steven Van Zandt | place of birth | Boston | Sym |
| Ruby Braff | place of birth | Boston | Sym |
| Jane Cowl | place of birth | Boston | Sym |
| Elliott H. Lieb | place of birth | Boston | Sym |
| Fran Sheehan | place of birth | Boston | Sym |
| Edwin Percy Whipple | place of death | Boston | Sym |
| John Cunniff | place of birth | Boston | Sym |
| Brenda Frazier | place of death | Boston | Sym |
| Jonas Wood | place of birth | Boston | Sym |
| Henry Jacob Bigelow | place of birth | Boston | Sym |
| Henry Jacob Bigelow | work location | Boston | UnSym |
| Feliks Roziner | place of death | Boston | Sym |
| Eugene Foss | place of death | Boston | Sym |
| Alvan Tufts Fuller | place of death | Boston | Sym |
| Alvan Tufts Fuller | place of birth | Boston | Sym |
| Jane Toppan | place of birth | Boston | Sym |
| Jasmine Guy | place of birth | Boston | Sym |
| William M. Butler | place of death | Boston | Sym |
| Joseph Francis Maguire | place of birth | Boston | Sym |
| Abby May | place of birth | Boston | Sym |
| Madeline Miller | place of birth | Boston | Sym |
| Albert Bushnell Hart | place of death | Boston | Sym |
| Angeliki Laiou | place of death | Boston | Sym |
| Anne Nagel | place of birth | Boston | Sym |
| Arthur L. Andrews | place of birth | Boston | Sym |
| Benny Rubin | place of birth | Boston | Sym |
| Julius Adams Stratton | place of death | Boston | Sym |
| Ceremony | narrative location | Boston | Sym |
| Cogan's Trade | narrative location | Boston | Sym |
| Colonial Air Transport | headquarters location | Boston | Sym |
| Donald Foley | place of birth | Boston | Sym |
| Eddie Hurley | place of death | Boston | Sym |
| Elizabeth Brater | place of birth | Boston | Sym |
| Eric Loren | place of birth | Boston | Sym |
| Ethan Vogt | place of birth | Boston | Sym |
| Franklin S. Nickerson | place of death | Boston | Sym |
| William Steig | place of death | Boston | Sym |
| Whitey Bulger | place of birth | Boston | Sym |
| Raymond Griffith | place of birth | Boston | Sym |
| Hosea Ballou | place of death | Boston | Sym |
| Walter Gropius | place of death | Boston | Sym |
| Theodore Robert Dudley | place of birth | Boston | Sym |
| Jimmy Brogan | place of birth | Boston | Sym |
| John Elliott Cowdin | place of birth | Boston | Sym |
| John Sullivan Dwight | place of birth | Boston | Sym |
| Joshua Loring | place of birth | Boston | Sym |
| Samuel Sewall | place of death | Boston | Sym |
| Kempster Blanchard Miller | place of birth | Boston | Sym |
| Leon Adams | place of birth | Boston | Sym |
| Samuel Turell Armstrong | place of death | Boston | Sym |
| Maud Howe Elliott | place of birth | Boston | Sym |
| Richard Fletcher | place of death | Boston | Sym |
| Miles Browning | place of death | Boston | Sym |
| Muriel Rahn | place of birth | Boston | Sym |
| Nancy Glass | place of birth | Boston | Sym |
| Nathaniel Jeremiah Bradlee | place of birth | Boston | Sym |
| Roger Manvell | place of death | Boston | Sym |
| Richard Olney | place of death | Boston | Sym |
| Oliver O'Brien | place of birth | Boston | Sym |
| Paul M. English | place of birth | Boston | Sym |
| Albert-László Barabási | work location | Boston | UnSym |
| Richard Bellingham | place of death | Boston | Sym |
| Carolyn Bertozzi | place of birth | Boston | Sym |
| Stephanie Braxton | place of birth | Boston | Sym |
| Stephen Ratcliffe | place of birth | Boston | Sym |
| The Astonishing Life of Octavian Nothing, Traitor to the Nation, Volume I: The Pox Party | narrative location | Boston | Sym |
| The Last Hurrah | narrative location | Boston | Sym |
| Vail Bloom | place of birth | Boston | Sym |
| Norman Levinson | place of death | Boston | Sym |
| Belly | location of formation | Boston | UnSym |
| Anthony Quinn | place of death | Boston | Sym |
| Christopher Gore | place of birth | Boston | Sym |
| Leonard Wood | place of death | Boston | Sym |
| James Bowdoin | place of birth | Boston | Sym |
| James Bowdoin | place of death | Boston | Sym |
| Category:Deaths in Boston, Lincolnshire | category combines topics | Boston | UnSym |
| Jon Kleinberg | place of birth | Boston | Sym |
| Thomas Bulfinch | place of death | Boston | Sym |
| Dorchester | located in the administrative territorial entity | Boston | Sym |
| Charlie Holmes | place of birth | Boston | Sym |
| Christopher Wool | place of birth | Boston | Sym |
| Leonora Bilger | place of birth | Boston | Sym |
| Danny Draven | place of birth | Boston | Sym |
| David Evans | place of birth | Boston | Sym |
| Kiara Muhammad | place of birth | Boston | Sym |
| Q11884045 | place of birth | Boston | Sym |
| The Surgeon | narrative location | Boston | Sym |
| Prince Sadruddin Aga Khan | place of death | Boston | Sym |
| Markus Fritsch | work location | Boston | UnSym |
| Dyer Lum | place of death | Boston | Sym |
| Joseph Hall | place of death | Boston | Sym |
| Samuel Parkman Tuckerman | place of birth | Boston | Sym |
| Elbridge Ross | place of birth | Boston | Sym |
| Polly Palfrey | place of birth | Boston | Sym |
| Harry L. Shapiro | place of birth | Boston | Sym |
| George Richardson Proctor | place of birth | Boston | Sym |
| Hermann Hoerlin | place of death | Boston | Sym |
| John Parker Boyd | place of death | Boston | Sym |
| Mark Andrew Green | place of birth | Boston | Sym |
| John Campbell | place of death | Boston | Sym |
| Mikloš Schwalb | place of death | Boston | Sym |
| Caroline Coolidge Cushman Ticknor | place of birth | Boston | Sym |
| Manhattan Transfer | place of publication | Boston | UnSym |
| James Henigan | place of birth | Boston | Sym |
| James A. Gallivan | place of birth | Boston | Sym |
| Karl Gerhardt | place of birth | Boston | Sym |
| Tony Gaffney | place of birth | Boston | Sym |
| J. Gill | located in the administrative territorial entity | Boston | Sym |
| Leslie H. Martinson | place of birth | Boston | Sym |
| Philip Hale | place of death | Boston | Sym |
| Richard France | place of birth | Boston | Sym |
| Royall Tyler | place of birth | Boston | Sym |
| William Fly | place of death | Boston | Sym |
| Mary Dyer | place of death | Boston | Sym |
| Madeline Kahn | place of birth | Boston | Sym |
| William Barton Rogers | place of death | Boston | Sym |
| Harriet Quimby | place of death | Boston | Sym |
| Frank Robbins | place of birth | Boston | Sym |
| Q257073 | located in the administrative territorial entity | Boston | Sym |
| William Sowden Sims | place of death | Boston | Sym |
| Walter Powers | place of birth | Boston | Sym |
| Williamina Fleming | place of death | Boston | Sym |
| Angelina Weld Grimké | place of birth | Boston | Sym |
| James Taylor | place of birth | Boston | Sym |
| William Mason | place of birth | Boston | Sym |
| Liam Waite | place of birth | Boston | Sym |
| Francis Amasa Walker | place of birth | Boston | Sym |
| Francis Amasa Walker | place of death | Boston | Sym |
| James Hewitt | place of death | Boston | Sym |
| Dennis Miller Bunker | place of death | Boston | Sym |
| Loïs Mailou Jones | place of birth | Boston | Sym |
| Mickey Roach | place of birth | Boston | Sym |
| Dorothy Iannone | place of birth | Boston | Sym |
| Sarah Sze | place of birth | Boston | Sym |
| Oliver Wendell Holmes | place of birth | Boston | Sym |
| Cotton Mather | place of death | Boston | Sym |
| Cotton Mather | place of birth | Boston | Sym |
| James Crafts | place of birth | Boston | Sym |
| Thomas William Parsons | place of birth | Boston | Sym |
| Francis Boott | place of birth | Boston | Sym |
| Lev Lazarevitsj Goldin | place of death | Boston | Sym |
| Joseph R. Levenson | place of birth | Boston | Sym |
| Barbara Mullen | place of birth | Boston | Sym |
| George Goldthwaite | place of birth | Boston | Sym |
| Damon Santostefano | place of birth | Boston | Sym |
| Robert F. Bradford | place of birth | Boston | Sym |
| Robert F. Bradford | place of death | Boston | Sym |
| Charles F. Hurley | place of death | Boston | Sym |
| Solomon Trestin | place of death | Boston | Sym |
| Sarah Kemble Knight | place of birth | Boston | Sym |
| Henry Oliver Hansen | place of birth | Boston | Sym |
| Cell | narrative location | Boston | Sym |
| Alfred Browning Parker | place of birth | Boston | Sym |
| Amy Farrington | place of birth | Boston | Sym |
| Benjamin Byron Davis | place of birth | Boston | Sym |
| Billie Lawless | place of birth | Boston | Sym |
| Museum of Fine Arts Boston | located in the administrative territorial entity | Boston | Sym |
| Faneuil Hall | located in the administrative territorial entity | Boston | Sym |
| Brian Christie | place of birth | Boston | Sym |
| Cameron McRae Winslow | place of death | Boston | Sym |
| Carl Alpert | place of birth | Boston | Sym |
| Maribel Owen | place of birth | Boston | Sym |
| Connie Martinson | place of birth | Boston | Sym |
| Courtney Fathom Sell | place of birth | Boston | Sym |
| George Bonhag | place of birth | Boston | Sym |
| David B. Cohen | place of birth | Boston | Sym |
| Wendell Phillips | place of death | Boston | Sym |
| Wendell Phillips | place of birth | Boston | Sym |
| Rachel Bissex | place of birth | Boston | Sym |
| Edward H. Gibson | place of birth | Boston | Sym |
| Edward Lawrence Logan | place of death | Boston | Sym |
| Elliot Koffman | place of birth | Boston | Sym |
| Fannie Hillsmith | place of birth | Boston | Sym |
| Frederick T. Moore, Jr. | place of birth | Boston | Sym |
| Nathan Appleton | place of death | Boston | Sym |
| Geoffrey Sayre-McCord | place of birth | Boston | Sym |
| George D. Nye | place of death | Boston | Sym |
| George Willis | place of birth | Boston | Sym |
| Henry Simmons Frieze | place of birth | Boston | Sym |
| Howard Bryant | place of birth | Boston | Sym |
| Tom Barrasso | place of birth | Boston | Sym |
| Jack Germond | place of birth | Boston | Sym |
| James G. Carr | place of birth | Boston | Sym |
| John C. Cremony | place of birth | Boston | Sym |
| John Weiss | place of birth | Boston | Sym |
| Jonathan Jackson | place of birth | Boston | Sym |
| Jonathan Jackson | place of death | Boston | Sym |
| Joseph Francis Scott | place of birth | Boston | Sym |
| Laura Poitras | place of birth | Boston | Sym |
| Lauren Elliott | place of birth | Boston | Sym |
| Liam Madden | place of birth | Boston | Sym |
| Elisha Collier | place of birth | Boston | Sym |
| Elisha Collier | place of death | Boston | Sym |
| Christian Wolff | work location | Boston | UnSym |
| Lois Ayres | place of birth | Boston | Sym |
| Marie Cosindas | place of birth | Boston | Sym |
| Matt the Knife | place of birth | Boston | Sym |
| Paul-Henri Campbell | place of birth | Boston | Sym |
| Michael Gould | place of birth | Boston | Sym |
| Suki Schorer | place of birth | Boston | Sym |
| Nathaniel Taylor | place of birth | Boston | Sym |
| Arthur Fiedler | place of birth | Boston | Sym |
| Peter Bent Brigham | place of death | Boston | Sym |
| Peter E. Costello | place of birth | Boston | Sym |
| Rebecca Housel | place of birth | Boston | Sym |
| Roland Merullo | place of birth | Boston | Sym |
| Gregory Maguire | work location | Boston | UnSym |
| Samuel M. Pook | place of birth | Boston | Sym |
| Stephen Dunham | place of birth | Boston | Sym |
| Thomas H. Dunham | place of birth | Boston | Sym |
| Hanns Sachs | place of death | Boston | Sym |
| Walter R. Mansfield | place of birth | Boston | Sym |
| William H. Blanchard | place of birth | Boston | Sym |
| William S. Bennet II | place of death | Boston | Sym |
| Brian Duffy | place of birth | Boston | Sym |
| Harry Dexter White | place of birth | Boston | Sym |
| Richard M. Karp | place of birth | Boston | Sym |
| Eugene O'Neill | place of death | Boston | Sym |
| Gregory Deyermenjian | place of birth | Boston | Sym |
| Samuel Eliot Morison | place of birth | Boston | Sym |
| Samuel Eliot Morison | place of death | Boston | Sym |
| Caleb Blood Smith | place of birth | Boston | Sym |
| Charles William Eliot | place of birth | Boston | Sym |
| David Walton | place of birth | Boston | Sym |
| Horace Mann Junior | place of birth | Boston | Sym |
| Susan Paul | place of birth | Boston | Sym |
| Susan Paul | place of death | Boston | Sym |
| David Gilbarg | place of birth | Boston | Sym |
| Hugh Parker Guiler | place of birth | Boston | Sym |
| Kenneth W. Dam | place of birth | Boston | Sym |
| Emanuel Ondříček | place of death | Boston | Sym |
| Leonardo Ciampa | place of birth | Boston | Sym |
| Nat Hentoff | place of birth | Boston | Sym |
| Samuel Wendell Williston | place of birth | Boston | Sym |
| George Ticknor | place of birth | Boston | Sym |
| Frank Morey | place of birth | Boston | Sym |
| Robert F. McDermott | place of birth | Boston | Sym |
| Uzo Aduba | place of birth | Boston | Sym |
| George Holden Tinkham | place of birth | Boston | Sym |
| Shearjashub Bourne | place of death | Boston | Sym |
| Lucy Toulmin Smith | place of birth | Boston | Sym |
| Henry Vaughan | place of death | Boston | Sym |
| Leonard Nimoy | place of birth | Boston | Sym |
| Richard Hodgson | place of death | Boston | Sym |
| Chin Feng | place of death | Boston | Sym |
| John Neagle | place of birth | Boston | Sym |
| Joseph Henry O'Neil | place of death | Boston | Sym |
| Harry Beal Torrey | place of birth | Boston | Sym |
| William Thompson Sedgwick | place of death | Boston | Sym |
| John Paine | place of birth | Boston | Sym |
| Charles Green Bush | place of birth | Boston | Sym |
| Richard Bowditch Wigglesworth | place of death | Boston | Sym |
| Richard Bowditch Wigglesworth | place of birth | Boston | Sym |
| Roslindale | located in the administrative territorial entity | Boston | Sym |
| Pauline Whittier | place of birth | Boston | Sym |
| William S. McNary | place of death | Boston | Sym |
| Alfred Charles Hobbs | place of birth | Boston | Sym |
| Lauren Koslow | place of birth | Boston | Sym |
| Lillian Roth | place of birth | Boston | Sym |
| Robert Morss Lovett | place of birth | Boston | Sym |
| Tracy Bonham | place of birth | Boston | Sym |
| Abbott Handerson Thayer | place of birth | Boston | Sym |
| Bobby Brown | place of birth | Boston | Sym |
| Donnie Wahlberg | place of birth | Boston | Sym |
| Misha Collins | place of birth | Boston | Sym |
| Sheldon Adelson | place of birth | Boston | Sym |
| Tyler Faith | place of birth | Boston | Sym |
| Quincy Shaw | place of birth | Boston | Sym |
| Dick Dale | place of birth | Boston | Sym |
| Zodiac | narrative location | Boston | Sym |
| Babe Paley | place of birth | Boston | Sym |
| Barry Goudreau | place of birth | Boston | Sym |
| Isador Coriat | place of death | Boston | Sym |
| Arthur Blake | place of death | Boston | Sym |
| Arthur Blake | place of birth | Boston | Sym |
| Alex Grasshoff | place of birth | Boston | Sym |
| Q3796516 | place of death | Boston | Sym |
| Paul McGonagle | place of death | Boston | Sym |
| Wayne Turner | place of birth | Boston | Sym |
| Charles Edward Horn | place of death | Boston | Sym |
| Q4531342 | place of death | Boston | Sym |
| Q4531342 | place of birth | Boston | Sym |
| Helena Koželuhová | place of death | Boston | Sym |
| Michelle Thomas | place of birth | Boston | Sym |
| Arlene Francis | place of birth | Boston | Sym |
| Allan Crite | place of death | Boston | Sym |
| Andrea Robbins | place of birth | Boston | Sym |
| Lewis C. Cantley | work location | Boston | UnSym |
| Carl Frederick Burke | place of death | Boston | Sym |
| Harold Hitz Burton | place of birth | Boston | Sym |
| Charles Russell Lowell | place of birth | Boston | Sym |
| David John Scannell | place of birth | Boston | Sym |
| John Thomas | place of birth | Boston | Sym |
| Edward D. Townsend | place of birth | Boston | Sym |
| George Russell | place of death | Boston | Sym |
| Elizabeth Boott | place of birth | Boston | Sym |
| Krister Stendahl | place of death | Boston | Sym |
| Richard Rust | place of birth | Boston | Sym |
| Franklin W. Smith | place of birth | Boston | Sym |
| George Aiken | place of birth | Boston | Sym |
| George Eustis, Sr. | place of birth | Boston | Sym |
| F. Holland Day | place of birth | Boston | Sym |
| Alan Douglas | place of birth | Boston | Sym |
| Carl Mydans | place of birth | Boston | Sym |
| J. Carter Brown | place of death | Boston | Sym |
| Jane F. Barry | place of birth | Boston | Sym |
| John E. Kerrigan | place of death | Boston | Sym |
| John Wilson | place of death | Boston | Sym |
| Joseph P. Lash | place of death | Boston | Sym |
| Joshua Hall Bates | place of birth | Boston | Sym |
| Vladimir Dedijer | place of death | Boston | Sym |
| James Q. Wilson | place of death | Boston | Sym |
| Laura E. Richards | place of birth | Boston | Sym |
| Louis Jean Heydt | place of death | Boston | Sym |
| Man Gone Down | narrative location | Boston | Sym |
| Michelle Citron | place of birth | Boston | Sym |
| John Amaechi | place of birth | Boston | Sym |
| Peter Gammons | place of birth | Boston | Sym |
| Peter Haskell | place of birth | Boston | Sym |
| Piper Kerman | place of birth | Boston | Sym |
| Risa Lavizzo-Mourey | residence | Boston | Sym |
| Susan Butcher | place of birth | Boston | Sym |
| Samuel Gardner Drake | place of death | Boston | Sym |
| Samuel P. Spear | place of birth | Boston | Sym |
| Suffolk University Law School | located in the administrative territorial entity | Boston | Sym |
| Augustus Peabody Gardner | place of birth | Boston | Sym |
| North End | located in the administrative territorial entity | Boston | Sym |
| William Dana Orcutt | place of death | Boston | Sym |
| William J. A. Bailey | place of birth | Boston | Sym |
| Cariddi Nardulli | place of birth | Boston | Sym |
| Charles B. Cory | place of birth | Boston | Sym |
| Charles Francis Adams IV | place of birth | Boston | Sym |
| Lloyd Wheaton Bowers | place of death | Boston | Sym |
| Marian Hooper Adams | place of birth | Boston | Sym |
| James Reese Europe | place of death | Boston | Sym |
| E. J. Dionne | place of birth | Boston | Sym |
| Looking Backward | narrative location | Boston | Sym |
| Ella Lola | place of birth | Boston | Sym |
| Category:Films shot in Boston | category combines topics | Boston | UnSym |
| Erwin Griswold | place of death | Boston | Sym |
| Joseph Pilato | place of birth | Boston | Sym |
| Stanisław Barańczak | place of death | Boston | Sym |
| John Patrick Higgins | place of birth | Boston | Sym |
| John Patrick Higgins | place of death | Boston | Sym |
| John Schuck | place of birth | Boston | Sym |
| Richard Herd | place of birth | Boston | Sym |
| Francis Condon | place of death | Boston | Sym |
| Mark O'Brien | place of birth | Boston | Sym |
| Fred F. Sears | place of birth | Boston | Sym |
| William Moore | place of birth | Boston | Sym |
| Willard MacGregor | place of birth | Boston | Sym |
| George V. Brown | place of birth | Boston | Sym |
| Iron Lore Entertainment | headquarters location | Boston | Sym |
| Gisele Bündchen | residence | Boston | Sym |
| The Handmaid's Tale | narrative location | Boston | Sym |
| Boston subway system | located in the administrative territorial entity | Boston | Sym |
| Edwin May | place of birth | Boston | Sym |
| Winston L. Prouty | place of death | Boston | Sym |
| James T. Bates | place of birth | Boston | Sym |
| Thomas Barbour | place of death | Boston | Sym |
| Jerry Gray | place of birth | Boston | Sym |
| John A. Keliher | place of birth | Boston | Sym |
| John A. Keliher | place of death | Boston | Sym |
| John Locke | place of death | Boston | Sym |
| John W. Candler | place of birth | Boston | Sym |
| Samuel Sewall | place of birth | Boston | Sym |
| Karl Viëtor | place of death | Boston | Sym |
| Billy Yule | place of birth | Boston | Sym |
| Lev Shvarts | residence | Boston | Sym |
| A Recommendation of Inoculation: According to Baron Dimsdale's Method | place of publication | Boston | UnSym |
| An Appeal in Favor of that Class of Americans Called Africans | place of publication | Boston | UnSym |
| Jared Diamond | place of birth | Boston | Sym |
| Richard E. Byrd | place of death | Boston | Sym |
| Samuel Adams | place of death | Boston | Sym |
| Samuel Adams | place of birth | Boston | Sym |
| Roger Hale Sheaffe | place of birth | Boston | Sym |
| William Farnum | place of birth | Boston | Sym |
| Patricia Cornwell | work location | Boston | UnSym |
| Stephen A. Emery | place of death | Boston | Sym |
| Sekondi-Takoradi | twinned administrative body | Boston | Sym |
| Priscilla Morrill | place of birth | Boston | Sym |
| Watermelon Slim | place of birth | Boston | Sym |
| Judith Merril | place of birth | Boston | Sym |
| Oneohtrix Point Never | place of birth | Boston | Sym |
| Myles Kennedy | place of birth | Boston | Sym |
| James Cutler Dunn Parker | place of birth | Boston | Sym |
| Bill Wilson | place of birth | Boston | Sym |
| George Adams Leland | place of death | Boston | Sym |
| George Adams Leland | place of birth | Boston | Sym |
| Anatoly Zhabotinsky | place of death | Boston | Sym |
| Jude | place of birth | Boston | Sym |
| Theodore Sedgwick | place of death | Boston | Sym |
| Morton Prince | place of death | Boston | Sym |
| Morton Prince | place of birth | Boston | Sym |
| Jonathan Sass | work location | Boston | UnSym |
| Dave Lambert | place of birth | Boston | Sym |
| Maxime Bôcher | place of birth | Boston | Sym |
| Roland Hayes | place of death | Boston | Sym |
| George Patton IV | place of birth | Boston | Sym |
| Tara VanDerveer | place of birth | Boston | Sym |
| Josiah Quincy II | place of birth | Boston | Sym |
| Greg Johnston | place of birth | Boston | Sym |
| Jack Nance | place of birth | Boston | Sym |
| Gilbert Stuart | place of death | Boston | Sym |
| Haifa | twinned administrative body | Boston | Sym |
| Leonard Craske | place of death | Boston | Sym |
| Q4340904 | place of birth | Boston | Sym |
| Henry Gardner | place of birth | Boston | Sym |
| James Remar | place of birth | Boston | Sym |
| James Thomas Fields | place of death | Boston | Sym |
| Lucy Stone | place of death | Boston | Sym |
| Chris Nilan | place of birth | Boston | Sym |
| Peter Guralnick | place of birth | Boston | Sym |
| Caroline Zhang | place of birth | Boston | Sym |
| Alexander Hill Everett | place of birth | Boston | Sym |
| Mason Hammond | place of birth | Boston | Sym |
| Ann Smith Franklin | place of birth | Boston | Sym |
| Anthony J. Carson | place of birth | Boston | Sym |
| Anthony J. Carson | place of death | Boston | Sym |
| Anthony T. Shtogren | place of birth | Boston | Sym |
| Benjamin Arthur Quarles | place of birth | Boston | Sym |
| Bill Gillis | place of birth | Boston | Sym |
| Blanche Ring | place of birth | Boston | Sym |
| Bradford Hill | place of birth | Boston | Sym |
| Carl Greenberg | place of birth | Boston | Sym |
| Carlos Castillo | place of birth | Boston | Sym |
| David Lindsay-Abaire | place of birth | Boston | Sym |
| Eliza Lee Cabot Follen | place of birth | Boston | Sym |
| Erastus Brigham Bigelow | place of death | Boston | Sym |
| Gene Lavanchy | place of birth | Boston | Sym |
| George Ferguson | place of birth | Boston | Sym |
| Henry N. Cobb | place of birth | Boston | Sym |
| Henry Percival Dodge | place of birth | Boston | Sym |
| Antoine Joseph Jobin | place of birth | Boston | Sym |
| Jack Concannon | place of birth | Boston | Sym |
| John Ancrum Winslow | place of death | Boston | Sym |
| John F. Kelly | place of birth | Boston | Sym |
| John Henning | place of death | Boston | Sym |
| John Howard | residence | Boston | Sym |
| John Howard | place of birth | Boston | Sym |
| John R. Tunis | place of birth | Boston | Sym |
| Joseph W. Revere | place of birth | Boston | Sym |
| Kahlil Gibran | place of birth | Boston | Sym |
| Kahlil Gibran | place of death | Boston | Sym |
| Helen Johns | place of birth | Boston | Sym |
| Q6627105 | place of death | Boston | Sym |
| Louise Brigham | place of birth | Boston | Sym |
| Mary Ann Vincent | place of death | Boston | Sym |
| Mather Byles | place of birth | Boston | Sym |
| Maud Wood Park | place of birth | Boston | Sym |
| Richard N. Frye | place of death | Boston | Sym |
| Samantha Runnion | place of birth | Boston | Sym |
| Barry Newman | place of birth | Boston | Sym |
| Ben Bradlee | place of birth | Boston | Sym |
| William M. Evarts | place of birth | Boston | Sym |
| Owlchemy Labs | located in the administrative territorial entity | Boston | Sym |
| Coma | narrative location | Boston | Sym |
| Roland Winters | place of birth | Boston | Sym |
| Samantha Logan | place of birth | Boston | Sym |
| Samuel Schafler | place of death | Boston | Sym |
| Sidney Topol | place of birth | Boston | Sym |
| Small Vices | narrative location | Boston | Sym |
| Q761940 | place of death | Boston | Sym |
| William Healey Dall | place of birth | Boston | Sym |
| Thomas Harcourt | place of birth | Boston | Sym |
| Thomas Kilby Smith | place of birth | Boston | Sym |
| Arthur Casagrande | place of death | Boston | Sym |
| Vincent Dethier | place of birth | Boston | Sym |
| William Bradford Turner | place of birth | Boston | Sym |
| Charles A. Dinarello | place of birth | Boston | Sym |
| John Lewis Bates | place of death | Boston | Sym |
| George von Lengerke Meyer | place of birth | Boston | Sym |
| George von Lengerke Meyer | place of death | Boston | Sym |
| John McCarthy | place of birth | Boston | Sym |
| Ezio Levi | place of death | Boston | Sym |
| John Bardeen | place of death | Boston | Sym |
| Jeffrey Davidow | place of birth | Boston | Sym |
| John Michael Higgins | place of birth | Boston | Sym |
| Edward Franklin Bland | place of death | Boston | Sym |
| Rudolph Nissen | work location | Boston | UnSym |
| Charles J. McCarthy | place of birth | Boston | Sym |
| Eugene Roche | place of birth | Boston | Sym |
| Sandy Saddler | place of birth | Boston | Sym |
| Eddie Collins | place of death | Boston | Sym |
| Edward Tuckerman | place of birth | Boston | Sym |
| School of the Museum of Fine Arts, Boston | located in the administrative territorial entity | Boston | Sym |
| Mario Cantone | place of birth | Boston | Sym |
| Albert Vincent Casey | place of birth | Boston | Sym |
| Jaki Byard | place of death | Boston | Sym |
| John Conness | place of death | Boston | Sym |
| Nicky Jam | place of birth | Boston | Sym |
| Fitz-John Winthrop | place of death | Boston | Sym |
| John Charles Phillips | place of birth | Boston | Sym |
| Lew Rockwell | place of birth | Boston | Sym |
| Roman Jakobson | place of death | Boston | Sym |
| Harold Ross | place of death | Boston | Sym |
| Robert Benjamin Lewis | residence | Boston | Sym |
| Herbert Gidney | place of birth | Boston | Sym |
| Samuel L. Crocker | place of death | Boston | Sym |
| Howard Johnson | place of birth | Boston | Sym |
| Jonathan Kale | place of birth | Boston | Sym |
| Jane Colman Turell | place of birth | Boston | Sym |
| John Davenport | place of death | Boston | Sym |
| Joseph Abraham Zilber | place of birth | Boston | Sym |
| Joseph Tuckerman | place of birth | Boston | Sym |
| Leone Lane | place of birth | Boston | Sym |
| Mike Coppola | place of death | Boston | Sym |
| Anita Fuentes | place of birth | Boston | Sym |
| William Wallace Morland | place of death | Boston | Sym |
| Paul X. Kelley | place of birth | Boston | Sym |
| Willard Van Orman Quine | place of death | Boston | Sym |
| Richard Reeve Baxter | place of death | Boston | Sym |
| Mortal Fear | narrative location | Boston | Sym |
| Cid Corman | place of birth | Boston | Sym |
| Medina Dixon | place of birth | Boston | Sym |
| Frank Ross | place of birth | Boston | Sym |
| Blanchard Ryan | place of birth | Boston | Sym |
| Rosemary Kennedy | place of birth | Boston | Sym |
| William Gilson Farlow | place of birth | Boston | Sym |
| Joseph John Ruocco | place of birth | Boston | Sym |
| Marron Curtis Fort | place of birth | Boston | Sym |
| Big Shug | place of birth | Boston | Sym |
| Francisco Goldman | place of birth | Boston | Sym |
| Gaston Chérau | place of death | Boston | Sym |
| James Henry Emerton | place of death | Boston | Sym |
| Kevin Chapman | place of birth | Boston | Sym |
| Bud Blake | place of death | Boston | Sym |
| William L. Shirer | place of death | Boston | Sym |
| Bob Elliott | place of birth | Boston | Sym |
| Michael Ryan | place of birth | Boston | Sym |
| Thomas Curtis | place of birth | Boston | Sym |
| Leon Tuck | place of death | Boston | Sym |
| Oscar Brodney | place of birth | Boston | Sym |
| Chico Scimone | place of birth | Boston | Sym |
| Christopher Seider | place of death | Boston | Sym |
| John Winthrop the Younger | place of death | Boston | Sym |
| Marc Kirschner | work location | Boston | UnSym |
| Charles Francis Adams III | place of death | Boston | Sym |
| Borden Parker Bowne | place of death | Boston | Sym |
| Béla Böszörményi-Nagy | place of death | Boston | Sym |
| David B. Zilberman | place of death | Boston | Sym |
| Joasaph | place of death | Boston | Sym |
| Joasaph | place of birth | Boston | Sym |
| Robert Cormier | place of death | Boston | Sym |
| Rudolʹf Olʹshevskiĭ | place of death | Boston | Sym |
| Robert Walthour | place of death | Boston | Sym |
| Roy Haynes | place of birth | Boston | Sym |
| Lynne Cox | place of birth | Boston | Sym |
| Ada Adini | place of birth | Boston | Sym |
| Amos Lawrence | place of death | Boston | Sym |
| Ann Bauer | place of birth | Boston | Sym |
| B. O. Flower | place of death | Boston | Sym |
| George Wein | place of birth | Boston | Sym |
| Carla DeSantis Black | place of birth | Boston | Sym |
| Frank E. Guernsey | place of death | Boston | Sym |
| Dan Barry | place of birth | Boston | Sym |
| Dan Barry | place of death | Boston | Sym |
| Dana Bullen | place of birth | Boston | Sym |
| Daniel White | place of death | Boston | Sym |
| George Nolfi | place of birth | Boston | Sym |
| Joe Boyd | place of birth | Boston | Sym |
| Donna Loren | place of birth | Boston | Sym |
| Elizabeth Stuart Phelps Ward | place of birth | Boston | Sym |
| Ellen Sturgis Hooper | place of birth | Boston | Sym |
| Fudge Mabeta | place of birth | Boston | Sym |
| Jon Foster | place of birth | Boston | Sym |
| George Dickson | place of birth | Boston | Sym |
| George Lyman Kittredge | place of birth | Boston | Sym |
| Henry Whitney Bellows | place of birth | Boston | Sym |
| Anton Leader | place of birth | Boston | Sym |
| Warren Rudman | place of birth | Boston | Sym |
| Jess Nevins | place of birth | Boston | Sym |
| John Calvin Stevens | place of birth | Boston | Sym |
| John Keefe | place of birth | Boston | Sym |
| John Rock | place of death | Boston | Sym |
| Kelly Lange | place of birth | Boston | Sym |
| Stephen Greenblatt | place of birth | Boston | Sym |
| Lenny Baker | place of birth | Boston | Sym |
| Max Blumenthal | place of birth | Boston | Sym |
| Nancy Garden | place of birth | Boston | Sym |
| Paul Shapiro | place of birth | Boston | Sym |
| Elliot Richardson | place of death | Boston | Sym |
| Elliot Richardson | place of birth | Boston | Sym |
| Q7323156 | place of birth | Boston | Sym |
| Rob Morris | place of birth | Boston | Sym |
| Samuel Crowther | place of death | Boston | Sym |
| Seth Williams | place of death | Boston | Sym |
| Albert Lord | place of birth | Boston | Sym |
| Susan Hale | place of birth | Boston | Sym |
| Massachusetts | capital | Boston | Sym |
| Shirley Clarke | place of death | Boston | Sym |
| Thomas G. Stevenson | place of birth | Boston | Sym |
| Annisa Pohan | place of birth | Boston | Sym |
| Edward Everett Hale | place of birth | Boston | Sym |
| Wally Peterson | place of birth | Boston | Sym |
| William Dummer | place of birth | Boston | Sym |
| William Warren | place of death | Boston | Sym |
| Thomas Bailey Aldrich | place of death | Boston | Sym |
| Jerry Colonna | place of birth | Boston | Sym |
| Brooks Adams | place of death | Boston | Sym |
| Jonathan Roberts | place of birth | Boston | Sym |
| Janet Auchincloss Rutherfurd | place of death | Boston | Sym |
| Charles Bass | place of birth | Boston | Sym |
| Charles Loring Jackson | place of birth | Boston | Sym |
import spark.implicits._
mergedDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
list: List[String] = List(src, rel, dst, srcentid, srclabel)
mergedDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
Motif finding for link prediction
In this notebook, we are interested in motif finding for link prediction in a large scale knowledge graph. In particular, we explore the WikiKG dataset which is a directed graph containing 2,500,604 nodes, 16,109,182 edges and 535 relations. Given a motif \(M\) which contains \(n\) nodes and \(m\) edges, we are intereted in the super-motifs of \(M\) which contain \(n\) nodes and \(m+1\) edges. We would expect that finding frequent super-motifs of \(M\) would give insight for link prediction.
Problem Definition
Given a graph \(G=(V,E)\), let \(T^{(n,m)}\) T_nm denote a set of motifs with \(n\) nodes and \(m\) edges and \(N(T_i^{(n,m)})\) N_T_nm_i be the number of subgraphs in \(G\) defined by motifs \(T_i^{(n,m)}\) T_nm_i. \(D(T_i^{(n,m)})\) D_T_nm_i is the number of induced subgraphs defined by motifs \(T_i^{(n,m)}\) in \(G\).
In particular, we are interested in finding motifs with 3 nodes in graph, which are commonly called triads.
Since we are only interested in motifs with 3 nodes here, we set n=3 and simplify our notation as \(T^{(m)}\). \(T={\cup_{k=1}^6 T^{(k)}}\) denotes the set of all possible motifs with 3 nodes and \(|T|=64\). Here we give the problem 1 we are interested in:
Problem 1: Given a motif \(T_i^{(k)}\in T^{(k)}\), we could construct a subset \(T'^{(k+1)} \subset T^{(k+1)}\) by adding a non-existent edge into \(T_i^{(k)}\). For each motif \(t_j \in T'^{(k+1)}\), we define the significance score of \(t_i\) w.r.t \(T_i^{(k)}\) as following:
\[ S(t_j,T_i^{(k)}) = \frac{N(t_j)}{N(T_i^{(k)}) - D(T_i^{(k)})}. \]
Our goal is to find significant motifs in \(T'^{(k+1)}\) according to significance scores.
Lemma 1: Given a motif \(T_i^{(k)}\in T^{(k)}\) and corresponding \(T'^{(k+1)}\), we have:
\[ \sum_{j=1}^{|T'^{(k+1)}|} S(t_j,T_i^{(k)}) = 1 \]
Proof: It is obvious that \(T_i^{(k)}\) is a sub-motif for all motifs in \(T'^{(k+1)}\), when we compute \(N(T_i^{(k)})\) without \(D(T_i^{(k)})\), the number is exactly the summation of \(N(t_j)\).
According to problem 1 and lemma 1, we could get the significance score distribution of \(T'^{(k+1)}\) given motif \(T_i^{(k)}\). We could easily compute this distribution for all motifs in \(T\). This gives us insight of significant motifs for link recommendation or prediction tasks.
Motifs of 3 nodes
In this section, we show all possible motifs with three nodes and give a example of our problem. The following table shows the number of motifs with same edges. Fig. 1 visualize all possible motifs. Following our definition and we take motif 7 in Fig. 1 as a example. \(T_i^{(2)}\) denote motif 7 which contians 2 edges. Now we are interest to find set of its super-motif \(T'^{(k+1)} =\)[8,15,23,39]. If we only consider connected type. Motifs [1,2,3,5,6,9,17,19,33,41] in figure 1 will be ignored. | edges | motifs | super-motifs set| | ---- | ---- | -----| | 0 | 1 | 6*1 | | 1 | 6 | 5 *6 | | 2 | 15 | 4 *15 | | 3 | 20 | 3 *20 | | 4 | 15 | 2 *15 | | 5 | 6 | 1 *6 | | 6 | 1 | 0 |
![]() |
|---|
| Fig. 1 Motifs with 3 nodes |
mergedDf2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
list2: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel)
mergedDF2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
rel_name_df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list3: List[String] = List(relid, label, description)
relnamedf: org.apache.spark.sql.DataFrame = [relid: string, label: string ... 1 more field]
finalDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 7 more fields]
list4: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel, relid, rellabel)
finalDF: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
edgesDF_: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
list5: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
verticesDf: org.apache.spark.sql.DataFrame = [id: string]
graph: org.graphframes.GraphFrame = GraphFrame(v:[id: string], e:[src: string, dst: string ... 1 more field])
// example: Motif 7 and its super motif sets. Our goal is to find significant relations.
val motif_7 = "(a)-[r1]->(b); (b)-[r2]->(c)"
val motif_7_super_motifs = List("(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(a)","(a)-[r1]->(b); (b)-[r2]->(c); (a)-[r3]->(c)", "(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(b)", "(a)-[r1]->(b); (b)-[r2]->(c); (b)-[r3]->(a)")
// Example 1, to look at motif_7 and and motif_7_supper_motif_7[1].
val motif_7_result = graph.find(motif_7)
val motif_7_super_motif_1 = graph.find(motif_7_super_motifs(1))
display(motif_7_result)
display(motif_7_super_motif_1)
import org.apache.spark.sql.DataFrame
// T_32 := includes all 3 edges 2 vertice combination
val T_32_all_Query_List = Seq("(a)-[r1]->(b); (b)-[r2]->(c)",
"(a)-[r1]->(b); (c)-[r2]->(b)",
"(a)-[r1]->(b); (a)-[r2]->(c)",
"(a)-[r1]->(b); (b)-[r1]->(a);(c)-[r2]->(b)",
"(a)-[r1]->(b); (b)-[r1]->(a);(b)-[r2]->(c)",
"(a)-[r1]->(b); (b)-[r1]->(a);(b)-[r2]->(c);(c)-[r2]->(b)")
val T_32_1_Query_List = Seq("(a)-[r1]->(b); (b)-[r2]->(c)")
val Sup_T_32_1_Query_List = Seq("(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(a)","(a)-[r1]->(b); (b)-[r2]->(c); (a)-[r3]->(c)", "(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(b)", "(a)-[r1]->(b); (b)-[r2]->(c); (b)-[r3]->(a)")
// Input: A list of query describe T_mn: Seq[String]
// Output: A dataframe [var1, rel, var2,...], for T_mn: Dataframe
def generate_T_mn_dataframe_pipline(T_mn_Query_List : Seq[String]): DataFrame = {
// ref: https://stackoverflow.com/a/37612978
val dfs = T_mn_Query_List.map(query => graph.find(query))
val result_df = dfs.reduce(_ unionByName _).distinct
return result_df
}
def count_T_mn_p_dataframe_pipline(T_mn_Query_List : Seq[String]): Seq[Long] = {
// ref: https://stackoverflow.com/a/37612978
val df_num = T_mn_Query_List.map(query => graph.find(query).count())
return df_num
}
// It is not applicable to use a loopy pass to union subgraph from different query: Spark have to define the column name before a loopy union, making such solution not feasible for random length of per query length
// val T_mn_Df = spark.emptyDataFrame
// for( x <- T_mn_Query_List ){
// // Union the query output with previous/empty dataframe
// // Union removes duplication, see: https://stackoverflow.com/questions/52494653/union-does-not-remove-duplicate-rows-in-spark-data-frame
// T_mn_Df.unionByName(graph.find(x), true);
// }
// BTW, This is suprisingly slow
// if (graph.find(x).isEmpty){
// println("Query get no result,: ", x)
// }
import org.apache.spark.sql.DataFrame
T_32_all_Query_List: Seq[String] = List((a)-[r1]->(b); (b)-[r2]->(c), (a)-[r1]->(b); (c)-[r2]->(b), (a)-[r1]->(b); (a)-[r2]->(c), (a)-[r1]->(b); (b)-[r1]->(a);(c)-[r2]->(b), (a)-[r1]->(b); (b)-[r1]->(a);(b)-[r2]->(c), (a)-[r1]->(b); (b)-[r1]->(a);(b)-[r2]->(c);(c)-[r2]->(b))
T_32_1_Query_List: Seq[String] = List((a)-[r1]->(b); (b)-[r2]->(c))
Sup_T_32_1_Query_List: Seq[String] = List((a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(a), (a)-[r1]->(b); (b)-[r2]->(c); (a)-[r3]->(c), (a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(b), (a)-[r1]->(b); (b)-[r2]->(c); (b)-[r3]->(a))
generate_T_mn_dataframe_pipline: (T_mn_Query_List: Seq[String])org.apache.spark.sql.DataFrame
count_T_mn_p_dataframe_pipline: (T_mn_Query_List: Seq[String])Seq[Long]
// Example: count T_32_1
val T_32_1_count = count_T_mn_p_dataframe_pipline (T_32_1_Query_List)
T_32_1_count: Seq[Long] = List(461186877)
// Example: Count T
val Sup_T_32_1_count = count_T_mn_p_dataframe_pipline (Sup_T_32_1_Query_List)
Sup_T_32_1_count: Seq[Long] = List(82327906, 200080767, 430486542, 437699609)
val T_32_all_count = count_T_mn_p_dataframe_pipline (T_32_all_Query_List)
display(dock)
Algorithms
Here we give the functions for motif finding and visualization.
// test
val T_33_1_Query_List = Seq("(a)-[r1]->(b); (b)-[r2]->(c) ;(c)-[r3]->(a)")
val T_33_1 = generate_T_mn_dataframe_pipline (T_33_1_Query_List)
T_33_1_Query_List: Seq[String] = List((a)-[r1]->(b); (b)-[r2]->(c) ;(c)-[r3]->(a))
T_33_1: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 4 more fields]
display(T_33_1)
Analysis
Here we give quantatitive analysis based on motif finding results and visualize important information.
Start by importing neccesary packages
./02_load_data
import spark.implicits._
import org.graphframes._
df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
import scala.util.Random
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._
val SEED = 42
import scala.util.Random
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._
SEED: Int = 42
df1: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
entdescdf: org.apache.spark.sql.DataFrame = [entid: string, label: string ... 1 more field]
import spark.implicits._
mergedDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
list: List[String] = List(src, rel, dst, srcentid, srclabel)
mergedDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 3 more fields]
mergedDf2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
list2: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel)
mergedDF2: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 5 more fields]
rel_name_df: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 1 more field]
list3: List[String] = List(relid, label, description)
relnamedf: org.apache.spark.sql.DataFrame = [relid: string, label: string ... 1 more field]
finalDf: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 7 more fields]
list4: List[String] = List(src, rel, dst, srcentid, srclabel, dstentid, dstlabel, relid, rellabel)
finalDF: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
edgesDF_: org.apache.spark.sql.DataFrame = [srclabel: string, rellabel: string ... 1 more field]
list5: List[String] = List(src, rel, dst)
edgesDF: org.apache.spark.sql.DataFrame = [src: string, rel: string ... 1 more field]
verticesDf: org.apache.spark.sql.DataFrame = [id: string]
graph: org.graphframes.GraphFrame = GraphFrame(v:[id: string], e:[src: string, dst: string ... 1 more field])
Create a random graph to have something to play around with
graph.edges.show()
+--------------------+--------------------+------+
| src| rel| dst|
+--------------------+--------------------+------+
| Dorchester|located in the ad...|Boston|
| Cariddi Nardulli| place of birth|Boston|
| Charles B. Cory| place of birth|Boston|
|Charles Francis A...| place of birth|Boston|
| Charlie Holmes| place of birth|Boston|
| Christopher Wool| place of birth|Boston|
| Leonora Bilger| place of birth|Boston|
| Danny Draven| place of birth|Boston|
| David Evans| place of birth|Boston|
| Kiara Muhammad| place of birth|Boston|
| Q11884045| place of birth|Boston|
| The Surgeon| narrative location|Boston|
|Lloyd Wheaton Bowers| place of death|Boston|
|Prince Sadruddin ...| place of death|Boston|
| Markus Fritsch| work location|Boston|
| Dyer Lum| place of death|Boston|
| Joseph Hall| place of death|Boston|
| Marian Hooper Adams| place of birth|Boston|
| James Reese Europe| place of death|Boston|
| E. J. Dionne| place of birth|Boston|
+--------------------+--------------------+------+
only showing top 20 rows
Check some basic things about our graph
val nEdges = graph.edges.count()
display(graph.edges)
| src | rel | dst |
|---|---|---|
| Dorchester | located in the administrative territorial entity | Boston |
| Cariddi Nardulli | place of birth | Boston |
| Charles B. Cory | place of birth | Boston |
| Charles Francis Adams IV | place of birth | Boston |
| Charlie Holmes | place of birth | Boston |
| Christopher Wool | place of birth | Boston |
| Leonora Bilger | place of birth | Boston |
| Danny Draven | place of birth | Boston |
| David Evans | place of birth | Boston |
| Kiara Muhammad | place of birth | Boston |
| Q11884045 | place of birth | Boston |
| The Surgeon | narrative location | Boston |
| Lloyd Wheaton Bowers | place of death | Boston |
| Prince Sadruddin Aga Khan | place of death | Boston |
| Markus Fritsch | work location | Boston |
| Dyer Lum | place of death | Boston |
| Joseph Hall | place of death | Boston |
| Marian Hooper Adams | place of birth | Boston |
| James Reese Europe | place of death | Boston |
| E. J. Dionne | place of birth | Boston |
| Looking Backward | narrative location | Boston |
| Samuel Parkman Tuckerman | place of birth | Boston |
| Elbridge Ross | place of birth | Boston |
| Ella Lola | place of birth | Boston |
| Category:Films shot in Boston | category combines topics | Boston |
| Polly Palfrey | place of birth | Boston |
| Erwin Griswold | place of death | Boston |
| Joseph Pilato | place of birth | Boston |
| Stanisław Barańczak | place of death | Boston |
| John Patrick Higgins | place of birth | Boston |
| John Patrick Higgins | place of death | Boston |
| John Schuck | place of birth | Boston |
| Richard Herd | place of birth | Boston |
| Harry L. Shapiro | place of birth | Boston |
| Francis Condon | place of death | Boston |
| Mark O'Brien | place of birth | Boston |
| Fred F. Sears | place of birth | Boston |
| William Moore | place of birth | Boston |
| Willard MacGregor | place of birth | Boston |
| George Richardson Proctor | place of birth | Boston |
| George V. Brown | place of birth | Boston |
| Hermann Hoerlin | place of death | Boston |
| John Parker Boyd | place of death | Boston |
| Iron Lore Entertainment | headquarters location | Boston |
| Gisele Bündchen | residence | Boston |
| The Handmaid's Tale | narrative location | Boston |
| Boston subway system | located in the administrative territorial entity | Boston |
| Mark Andrew Green | place of birth | Boston |
| Edwin May | place of birth | Boston |
| John Campbell | place of death | Boston |
| Mikloš Schwalb | place of death | Boston |
| Caroline Coolidge Cushman Ticknor | place of birth | Boston |
| Manhattan Transfer | place of publication | Boston |
| Winston L. Prouty | place of death | Boston |
| James Henigan | place of birth | Boston |
| James A. Gallivan | place of birth | Boston |
| James T. Bates | place of birth | Boston |
| Thomas Barbour | place of death | Boston |
| Jerry Gray | place of birth | Boston |
| John A. Keliher | place of birth | Boston |
| John A. Keliher | place of death | Boston |
| Karl Gerhardt | place of birth | Boston |
| John Locke | place of death | Boston |
| John W. Candler | place of birth | Boston |
| Samuel Sewall | place of birth | Boston |
| Karl Viëtor | place of death | Boston |
| Billy Yule | place of birth | Boston |
| Lev Shvarts | residence | Boston |
| Tony Gaffney | place of birth | Boston |
| A Recommendation of Inoculation: According to Baron Dimsdale's Method | place of publication | Boston |
| J. Gill | located in the administrative territorial entity | Boston |
| An Appeal in Favor of that Class of Americans Called Africans | place of publication | Boston |
| Jared Diamond | place of birth | Boston |
| Leslie H. Martinson | place of birth | Boston |
| Philip Hale | place of death | Boston |
| Richard E. Byrd | place of death | Boston |
| Samuel Adams | place of death | Boston |
| Samuel Adams | place of birth | Boston |
| Richard France | place of birth | Boston |
| Roger Hale Sheaffe | place of birth | Boston |
| Royall Tyler | place of birth | Boston |
| William Fly | place of death | Boston |
| William Farnum | place of birth | Boston |
| Patricia Cornwell | work location | Boston |
| Mary Dyer | place of death | Boston |
| Madeline Kahn | place of birth | Boston |
| William Barton Rogers | place of death | Boston |
| Stephen A. Emery | place of death | Boston |
| Sekondi-Takoradi | twinned administrative body | Boston |
| Priscilla Morrill | place of birth | Boston |
| Harriet Quimby | place of death | Boston |
| Watermelon Slim | place of birth | Boston |
| Frank Robbins | place of birth | Boston |
| Q257073 | located in the administrative territorial entity | Boston |
| William Sowden Sims | place of death | Boston |
| Judith Merril | place of birth | Boston |
| Walter Powers | place of birth | Boston |
| Williamina Fleming | place of death | Boston |
| Angelina Weld Grimké | place of birth | Boston |
| Oneohtrix Point Never | place of birth | Boston |
| Myles Kennedy | place of birth | Boston |
| James Cutler Dunn Parker | place of birth | Boston |
| Bill Wilson | place of birth | Boston |
| George Adams Leland | place of death | Boston |
| George Adams Leland | place of birth | Boston |
| James Taylor | place of birth | Boston |
| Anatoly Zhabotinsky | place of death | Boston |
| Jude | place of birth | Boston |
| William Mason | place of birth | Boston |
| Liam Waite | place of birth | Boston |
| Francis Amasa Walker | place of birth | Boston |
| Francis Amasa Walker | place of death | Boston |
| James Hewitt | place of death | Boston |
| Dennis Miller Bunker | place of death | Boston |
| Loïs Mailou Jones | place of birth | Boston |
| Mickey Roach | place of birth | Boston |
| Theodore Sedgwick | place of death | Boston |
| Morton Prince | place of death | Boston |
| Morton Prince | place of birth | Boston |
| Dorothy Iannone | place of birth | Boston |
| Jonathan Sass | work location | Boston |
| Dave Lambert | place of birth | Boston |
| Maxime Bôcher | place of birth | Boston |
| Roland Hayes | place of death | Boston |
| Sarah Sze | place of birth | Boston |
| George Patton IV | place of birth | Boston |
| Tara VanDerveer | place of birth | Boston |
| Oliver Wendell Holmes | place of birth | Boston |
| Josiah Quincy II | place of birth | Boston |
| Greg Johnston | place of birth | Boston |
| Cotton Mather | place of death | Boston |
| Cotton Mather | place of birth | Boston |
| James Crafts | place of birth | Boston |
| Jack Nance | place of birth | Boston |
| Thomas William Parsons | place of birth | Boston |
| Francis Boott | place of birth | Boston |
| Gilbert Stuart | place of death | Boston |
| Lev Lazarevitsj Goldin | place of death | Boston |
| Haifa | twinned administrative body | Boston |
| Leonard Craske | place of death | Boston |
| Joseph R. Levenson | place of birth | Boston |
| Barbara Mullen | place of birth | Boston |
| George Goldthwaite | place of birth | Boston |
| Q4340904 | place of birth | Boston |
| Henry Gardner | place of birth | Boston |
| Damon Santostefano | place of birth | Boston |
| Robert F. Bradford | place of birth | Boston |
| Robert F. Bradford | place of death | Boston |
| Charles F. Hurley | place of death | Boston |
| James Remar | place of birth | Boston |
| Solomon Trestin | place of death | Boston |
| Sarah Kemble Knight | place of birth | Boston |
| James Thomas Fields | place of death | Boston |
| Henry Oliver Hansen | place of birth | Boston |
| Lucy Stone | place of death | Boston |
| Chris Nilan | place of birth | Boston |
| Peter Guralnick | place of birth | Boston |
| Caroline Zhang | place of birth | Boston |
| Cell | narrative location | Boston |
| Alexander Hill Everett | place of birth | Boston |
| Alfred Browning Parker | place of birth | Boston |
| Mason Hammond | place of birth | Boston |
| Amy Farrington | place of birth | Boston |
| Ann Smith Franklin | place of birth | Boston |
| Anthony J. Carson | place of birth | Boston |
| Anthony J. Carson | place of death | Boston |
| Anthony T. Shtogren | place of birth | Boston |
| Benjamin Arthur Quarles | place of birth | Boston |
| Benjamin Byron Davis | place of birth | Boston |
| Bill Gillis | place of birth | Boston |
| Billie Lawless | place of birth | Boston |
| Museum of Fine Arts Boston | located in the administrative territorial entity | Boston |
| Faneuil Hall | located in the administrative territorial entity | Boston |
| Blanche Ring | place of birth | Boston |
| Bradford Hill | place of birth | Boston |
| Brian Christie | place of birth | Boston |
| Cameron McRae Winslow | place of death | Boston |
| Carl Alpert | place of birth | Boston |
| Carl Greenberg | place of birth | Boston |
| Carlos Castillo | place of birth | Boston |
| Maribel Owen | place of birth | Boston |
| Connie Martinson | place of birth | Boston |
| Courtney Fathom Sell | place of birth | Boston |
| George Bonhag | place of birth | Boston |
| David B. Cohen | place of birth | Boston |
| Wendell Phillips | place of death | Boston |
| Wendell Phillips | place of birth | Boston |
| David Lindsay-Abaire | place of birth | Boston |
| Rachel Bissex | place of birth | Boston |
| Edward H. Gibson | place of birth | Boston |
| Edward Lawrence Logan | place of death | Boston |
| Eliza Lee Cabot Follen | place of birth | Boston |
| Elliot Koffman | place of birth | Boston |
| Erastus Brigham Bigelow | place of death | Boston |
| Fannie Hillsmith | place of birth | Boston |
| Frederick T. Moore, Jr. | place of birth | Boston |
| Nathan Appleton | place of death | Boston |
| Gene Lavanchy | place of birth | Boston |
| Geoffrey Sayre-McCord | place of birth | Boston |
| George D. Nye | place of death | Boston |
| George Ferguson | place of birth | Boston |
| George Willis | place of birth | Boston |
| Henry N. Cobb | place of birth | Boston |
| Henry Percival Dodge | place of birth | Boston |
| Henry Simmons Frieze | place of birth | Boston |
| Antoine Joseph Jobin | place of birth | Boston |
| Howard Bryant | place of birth | Boston |
| Tom Barrasso | place of birth | Boston |
| Jack Concannon | place of birth | Boston |
| Jack Germond | place of birth | Boston |
| James G. Carr | place of birth | Boston |
| John Ancrum Winslow | place of death | Boston |
| John C. Cremony | place of birth | Boston |
| John F. Kelly | place of birth | Boston |
| John Henning | place of death | Boston |
| John Howard | residence | Boston |
| John Howard | place of birth | Boston |
| John R. Tunis | place of birth | Boston |
| John Weiss | place of birth | Boston |
| Jonathan Jackson | place of birth | Boston |
| Jonathan Jackson | place of death | Boston |
| Joseph Francis Scott | place of birth | Boston |
| Joseph W. Revere | place of birth | Boston |
| Kahlil Gibran | place of birth | Boston |
| Kahlil Gibran | place of death | Boston |
| Helen Johns | place of birth | Boston |
| Laura Poitras | place of birth | Boston |
| Lauren Elliott | place of birth | Boston |
| Liam Madden | place of birth | Boston |
| Elisha Collier | place of birth | Boston |
| Elisha Collier | place of death | Boston |
| Christian Wolff | work location | Boston |
| Q6627105 | place of death | Boston |
| Lois Ayres | place of birth | Boston |
| Louise Brigham | place of birth | Boston |
| Marie Cosindas | place of birth | Boston |
| Mary Ann Vincent | place of death | Boston |
| Mather Byles | place of birth | Boston |
| Matt the Knife | place of birth | Boston |
| Maud Wood Park | place of birth | Boston |
| Paul-Henri Campbell | place of birth | Boston |
| Michael Gould | place of birth | Boston |
| Richard N. Frye | place of death | Boston |
| Suki Schorer | place of birth | Boston |
| Samantha Runnion | place of birth | Boston |
| Nathaniel Taylor | place of birth | Boston |
| Barry Newman | place of birth | Boston |
| Ben Bradlee | place of birth | Boston |
| William M. Evarts | place of birth | Boston |
| Arthur Fiedler | place of birth | Boston |
| Owlchemy Labs | located in the administrative territorial entity | Boston |
| Peter Bent Brigham | place of death | Boston |
| Peter E. Costello | place of birth | Boston |
| Coma | narrative location | Boston |
| Rebecca Housel | place of birth | Boston |
| Roland Merullo | place of birth | Boston |
| Roland Winters | place of birth | Boston |
| Gregory Maguire | work location | Boston |
| Samantha Logan | place of birth | Boston |
| Samuel M. Pook | place of birth | Boston |
| Samuel Schafler | place of death | Boston |
| Sidney Topol | place of birth | Boston |
| Small Vices | narrative location | Boston |
| Stephen Dunham | place of birth | Boston |
| Q761940 | place of death | Boston |
| William Healey Dall | place of birth | Boston |
| Thomas H. Dunham | place of birth | Boston |
| Thomas Harcourt | place of birth | Boston |
| Thomas Kilby Smith | place of birth | Boston |
| Arthur Casagrande | place of death | Boston |
| Hanns Sachs | place of death | Boston |
| Vincent Dethier | place of birth | Boston |
| Walter R. Mansfield | place of birth | Boston |
| William Bradford Turner | place of birth | Boston |
| William H. Blanchard | place of birth | Boston |
| William S. Bennet II | place of death | Boston |
| Charles A. Dinarello | place of birth | Boston |
| John Lewis Bates | place of death | Boston |
| Brian Duffy | place of birth | Boston |
| Harry Dexter White | place of birth | Boston |
| George von Lengerke Meyer | place of birth | Boston |
| George von Lengerke Meyer | place of death | Boston |
| Richard M. Karp | place of birth | Boston |
| John McCarthy | place of birth | Boston |
| Eugene O'Neill | place of death | Boston |
| Ezio Levi | place of death | Boston |
| John Bardeen | place of death | Boston |
| Jeffrey Davidow | place of birth | Boston |
| John Michael Higgins | place of birth | Boston |
| Gregory Deyermenjian | place of birth | Boston |
| Samuel Eliot Morison | place of birth | Boston |
| Samuel Eliot Morison | place of death | Boston |
| Edward Franklin Bland | place of death | Boston |
| Burton Pike | place of birth | Boston |
| Moonlight Mile | narrative location | Boston |
| Charles Bulfinch | place of death | Boston |
| Charles Bulfinch | place of birth | Boston |
| Charles Chadwick | place of death | Boston |
| Chuckie Taylor | place of birth | Boston |
| Ali Reza Pahlavi | place of death | Boston |
| Christopher Rojik | place of birth | Boston |
| Clifton R. Wharton, Jr. | place of birth | Boston |
| Boston Marathon bombings | located in the administrative territorial entity | Boston |
| Eddie Edwards | place of birth | Boston |
| Milt Raskin | place of birth | Boston |
| Donald Howard Menzel | place of death | Boston |
| Norman Corwin | place of birth | Boston |
| Patrick Ewing, Jr. | place of birth | Boston |
| Edith Fellows | place of birth | Boston |
| Peter Plympton Smith | place of birth | Boston |
| James Pierpont | place of birth | Boston |
| Serge Chaloff | place of birth | Boston |
| Serge Chaloff | place of death | Boston |
| Mianne Palfrey | place of birth | Boston |
| Carol Beckwith | place of birth | Boston |
| Jill Tasker | place of birth | Boston |
| Harriet Hallowell | place of birth | Boston |
| James Lee Peters | place of birth | Boston |
| Ronald Ludington | place of birth | Boston |
| Hugh S. Legaré | place of death | Boston |
| Richard Whitney | place of birth | Boston |
| Solomon Morris Kupchan | place of death | Boston |
| Henry E. Dixey | place of birth | Boston |
| John Smith | place of birth | Boston |
| John Rennie | place of birth | Boston |
| James Porter | place of death | Boston |
| Gerry Connolly | place of birth | Boston |
| Hugo Rossi | place of birth | Boston |
| Giles Melville Tod | place of birth | Boston |
| Godfrey Lowell Cabot | place of death | Boston |
| Godfrey Lowell Cabot | place of birth | Boston |
| Timothy Davis | place of death | Boston |
| Thomas Gamaliel Bradford | place of birth | Boston |
| Henry Pickering Bowditch | place of death | Boston |
| Henry Pickering Bowditch | place of birth | Boston |
| George C. Homans | place of birth | Boston |
| Frank Scannell | place of birth | Boston |
| Madeleine M. Joullié | residence | Boston |
| Joanne Simpson | place of birth | Boston |
| John Henry Willcox | place of death | Boston |
| John Oliver Hobbes | place of birth | Boston |
| Joseph Kekuku | place of death | Boston |
| Joseph P. Hoar | place of birth | Boston |
| Joseph T. O'Callahan | place of birth | Boston |
| Busty Heart | place of birth | Boston |
| Brian J. White | place of birth | Boston |
| Kenneth Jewett | place of death | Boston |
| Lawrence Berk | place of birth | Boston |
| Margaret Deland | place of death | Boston |
| Max Bondy | place of death | Boston |
| Alexander Hall | place of birth | Boston |
| Baruch Marzel | place of birth | Boston |
| William McGregor Paxton | place of death | Boston |
| Peleg Coffin, Jr. | place of death | Boston |
| Peleg Sprague | place of death | Boston |
| Peter A. Garland | place of birth | Boston |
| Tabitha St. Germain | place of birth | Boston |
| Allan H. Meltzer | place of birth | Boston |
| Felix Wolfes | place of death | Boston |
| William P. Murphy Jr. | place of birth | Boston |
| Geraldine Ferraro | place of death | Boston |
| Harry Crosby | place of birth | Boston |
| Susan Bottomly | place of birth | Boston |
| Anne Dudek | place of birth | Boston |
| Stephin Merritt | place of birth | Boston |
| Edith Nourse Rogers | place of death | Boston |
| Thomas D. Eliot | place of birth | Boston |
| Vincent Connare | place of birth | Boston |
| Paul Stanton | place of birth | Boston |
| West End | located in the administrative territorial entity | Boston |
| William Francis Murray | place of death | Boston |
| William Francis Murray | place of birth | Boston |
| William Gordon | place of birth | Boston |
| William Gordon | place of death | Boston |
| Al Vega | place of death | Boston |
| Albert Smith | place of death | Boston |
| Eric Francis MacKenzie | place of birth | Boston |
| June Anderson | place of birth | Boston |
| William Dawes | place of birth | Boston |
| Margaret Ruthven Lang | place of death | Boston |
| Margaret Ruthven Lang | place of birth | Boston |
| Joseph Gaudentius Anderson | place of birth | Boston |
| Susan Tedeschi | place of birth | Boston |
| Michael McDowell | place of death | Boston |
| Benjamin Hallowell Carew | place of birth | Boston |
| Jack Levine | place of birth | Boston |
| Donald Schön | place of birth | Boston |
| Dorothy Loudon | place of birth | Boston |
| Frank Craven | place of birth | Boston |
| Jack Burns | place of birth | Boston |
| Manny Delcarmen | place of birth | Boston |
| Johnny Curtis | place of birth | Boston |
| Joseph Abboud | place of birth | Boston |
| Joseph W. Smiley | place of birth | Boston |
| John Pitcairn | place of death | Boston |
| Eugene Braunwald | work location | Boston |
| Joseph Grew | place of birth | Boston |
| Ralph Barton Perry | place of death | Boston |
| Henry-Russell Hitchcock | place of birth | Boston |
| Richard Murphy | place of birth | Boston |
| Seán McKiernan | place of birth | Boston |
| Darren Turcotte | place of birth | Boston |
| Ted Donato | place of birth | Boston |
| Virginia Slims of Boston | located in the administrative territorial entity | Boston |
| Steven Van Zandt | place of birth | Boston |
| Vaughn Taylor | place of birth | Boston |
| Eugene Fama | place of birth | Boston |
| Ruby Braff | place of birth | Boston |
| Jane Cowl | place of birth | Boston |
| Elliott H. Lieb | place of birth | Boston |
| Fran Sheehan | place of birth | Boston |
| Edwin Percy Whipple | place of death | Boston |
| The Gospel According to Larry | narrative location | Boston |
| John Cunniff | place of birth | Boston |
| Nicholas Christofilos | place of birth | Boston |
| Thomas R. DiBenedetto | place of birth | Boston |
| Brenda Frazier | place of death | Boston |
| Jonas Wood | place of birth | Boston |
| Peter McNeeley | place of birth | Boston |
| Henry Jacob Bigelow | place of birth | Boston |
| Henry Jacob Bigelow | work location | Boston |
| Feliks Roziner | place of death | Boston |
| Eugene Foss | place of death | Boston |
| Alvan Tufts Fuller | place of death | Boston |
| Alvan Tufts Fuller | place of birth | Boston |
| Paul Andrew Dever | place of death | Boston |
| Paul Andrew Dever | place of birth | Boston |
| Roger Wolcott | place of death | Boston |
| Roger Wolcott | place of birth | Boston |
| Jane Toppan | place of birth | Boston |
| Jasmine Guy | place of birth | Boston |
| Alvin Langdon Coburn | place of birth | Boston |
| Ari Graynor | place of birth | Boston |
| William M. Butler | place of death | Boston |
| Joseph Francis Maguire | place of birth | Boston |
| Abby May | place of birth | Boston |
| Madeline Miller | place of birth | Boston |
| Albert Bushnell Hart | place of death | Boston |
| Angeliki Laiou | place of death | Boston |
| Anne Nagel | place of birth | Boston |
| Ant | place of birth | Boston |
| Arthur L. Andrews | place of birth | Boston |
| Benny Rubin | place of birth | Boston |
| Billy Porter | place of birth | Boston |
| Michael Bivins | place of birth | Boston |
| Julius Adams Stratton | place of death | Boston |
| Sonny Stitt | place of birth | Boston |
| Andrew Bujalski | place of birth | Boston |
| Ceremony | narrative location | Boston |
| Christopher G. Kennedy | place of birth | Boston |
| Cogan's Trade | narrative location | Boston |
| Colonial Air Transport | headquarters location | Boston |
| Anne Twomey | place of birth | Boston |
| Benjamin Apthorp Gould | place of birth | Boston |
| Daniel Warner | place of birth | Boston |
| Deborah Martin | place of birth | Boston |
| Dick Elliott | place of birth | Boston |
| Dodge Morgan | place of death | Boston |
| Donald Foley | place of birth | Boston |
| Eddie Hurley | place of death | Boston |
| Jan Miner | place of birth | Boston |
| Edward Brodney | place of birth | Boston |
| Elizabeth Brater | place of birth | Boston |
| Ellen Hinsey | place of birth | Boston |
| Erastus Corning 2nd | place of death | Boston |
| Eric Loren | place of birth | Boston |
| Ethan Vogt | place of birth | Boston |
| Fanny Ronalds | place of birth | Boston |
| Fannie Farmer | place of birth | Boston |
| Fannie Farmer | place of death | Boston |
| Franklin S. Nickerson | place of death | Boston |
| Alexandre Rockwell | place of birth | Boston |
| William Steig | place of death | Boston |
| Gaston Bell | place of birth | Boston |
| Gennaro Angiulo | place of death | Boston |
| Daniel Goldhagen | place of birth | Boston |
| George Robert Twelves Hewes | place of birth | Boston |
| Artur Rodziński | place of death | Boston |
| Whitey Bulger | place of birth | Boston |
| Grace Paine Terzian | place of birth | Boston |
| Harriet Nevins | place of birth | Boston |
| Raymond Griffith | place of birth | Boston |
| Hosea Ballou | place of death | Boston |
| Walter Gropius | place of death | Boston |
| Theodore Robert Dudley | place of birth | Boston |
| Jeffrey Lurie | place of birth | Boston |
| Jimmy Brogan | place of birth | Boston |
| Joe Hinton | place of death | Boston |
| John Brooks Wheelwright | place of death | Boston |
| John Elliott Cowdin | place of birth | Boston |
| John Quincy Adams II | place of birth | Boston |
| John Sullivan Dwight | place of birth | Boston |
| Joseph Francis | place of birth | Boston |
| Josephine St. Pierre Ruffin | place of birth | Boston |
| Josephine St. Pierre Ruffin | place of death | Boston |
| Joshua Loring | place of birth | Boston |
| Archibald MacLeish | place of death | Boston |
| Samuel Sewall | place of death | Boston |
| Kempster Blanchard Miller | place of birth | Boston |
| Kevin White | place of birth | Boston |
| Kevin White | place of death | Boston |
| Khari Wynn | place of birth | Boston |
| Leon Adams | place of birth | Boston |
| Samuel Turell Armstrong | place of death | Boston |
| Lou Breslow | place of birth | Boston |
| Lucia Fairchild Fuller | place of birth | Boston |
| Maud Howe Elliott | place of birth | Boston |
| Richard Fletcher | place of death | Boston |
| Miles Browning | place of death | Boston |
| Muriel Rahn | place of birth | Boston |
| Nancy Glass | place of birth | Boston |
| Nathaniel Jeremiah Bradlee | place of birth | Boston |
| Don Costa | place of birth | Boston |
| Roger Manvell | place of death | Boston |
| Richard Olney | place of death | Boston |
| Oliver O'Brien | place of birth | Boston |
| Arthur Messinger Comey | place of birth | Boston |
| Paris Themmen | place of birth | Boston |
| Paul M. English | place of birth | Boston |
| Alexander Mackendrick | place of birth | Boston |
| Alan Dawson | place of death | Boston |
| William Fogg Osgood | place of birth | Boston |
| Albert-László Barabási | work location | Boston |
| Richard Bellingham | place of death | Boston |
| Russ Bixler | place of birth | Boston |
| Junior Raglin | place of death | Boston |
| Carolyn Bertozzi | place of birth | Boston |
| Stephanie Braxton | place of birth | Boston |
| Stephen Ratcliffe | place of birth | Boston |
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| Saad | 12.0 |
| Maarten van Rossumplein | 3.0 |
| Q19340620 | 11.0 |
| Menno van Coehoornweg | 1.0 |
| Louis IV of France | 9.0 |
| Vemund | 2.0 |
| Moeletsi Mbeki | 3.0 |
| Sawda bint Zamʿa | 1.0 |
| Renselkade | 1.0 |
| Movement for Multi-Party Democracy | 5.0 |
| Sao | 3.0 |
| St Nicasiusstraat | 2.0 |
| Standerd | 1.0 |
| Stapelen | 1.0 |
| Q19548406 | 1.0 |
| Victorialaan | 2.0 |
| Vliegendesteeg | 2.0 |
| Waalwijkseweg | 1.0 |
| Tienen railway station | 2.0 |
| Nikolske Raion | 1.0 |
| Green League | 15.0 |
| Larbert railway station | 2.0 |
| Bernward | 16.0 |
| Q19688717 | 61.0 |
| 2136 | 4.0 |
| Wright Aeronautical | 6.0 |
| Sean Callery | 3.0 |
| Amable | 3.0 |
| Q19766372 | 2.0 |
| Q19776533 | 3.0 |
| Davyd Sviatoslavich | 5.0 |
| Q19803443 | 26.0 |
| Bastiaan | 21.0 |
| Q19839466 | 1.0 |
| artivist | 16.0 |
| Destination Berlin | 2.0 |
| Pierre Mendès-France University | 2.0 |
| Apodi | 2.0 |
| Lady Inger of Ostrat | 1.0 |
| Skuhrov | 17.0 |
| Pernik Province | 5.0 |
| Oelsen | 2.0 |
| Röbel | 9.0 |
| Walram IV of Nassau-Wiesbaden-Idstein | 5.0 |
| Nossentiner Hütte | 1.0 |
| La Bosse | 4.0 |
| FC Krystal Kherson | 5.0 |
| Nico Mirallegro | 2.0 |
| ancient philosophy | 1.0 |
| Rostom of Kartli | 1.0 |
| 2010 PTT Pattaya Open | 2.0 |
| White River, Mpumalanga | 2.0 |
| Cajamarca | 28.0 |
| canton of Gros-Morne | 2.0 |
| Étampes | 54.0 |
| Ross-on-Wye | 9.0 |
| Sandra Milovanoff | 11.0 |
| Račiněves | 6.0 |
| Zlatko | 52.0 |
| Eurythmics | 40.0 |
| Ügyek | 2.0 |
| City of Matlosana | 3.0 |
| Sportpark Eschen-Mauren | 1.0 |
| Blythewood | 1.0 |
| Doctor P | 1.0 |
| Frederikssundbanen | 21.0 |
| Villahermosa | 18.0 |
| Soyuz TMA-09M | 3.0 |
| Castelldefels railway station | 2.0 |
| Rivière des Mille Îles | 3.0 |
| Thierry Breton | 1.0 |
| Serge-Henri | 2.0 |
| Marnie McPhail | 1.0 |
| Wolf Heinrich Graf von Baudissin | 1.0 |
| Georges Rouget | 1.0 |
| Shawo Township | 3.0 |
| Q2184650 | 2.0 |
| bazaar | 3.0 |
| Přepychy | 11.0 |
| Cappel | 6.0 |
| Kerbach | 3.0 |
| 24318 Vivianlee | 2.0 |
| Dobrinsky District | 2.0 |
| Sollefteå Municipality | 26.0 |
| Q2228637 | 1.0 |
| Aitkin | 1.0 |
| Merry-sur-Yonne | 2.0 |
| godparent | 23.0 |
| Q2253989 | 3.0 |
| A Drink Before the War | 1.0 |
| Rudi Hiti | 1.0 |
| Q2292388 | 1.0 |
| Jane Krakowski | 19.0 |
| 40mm grenade | 8.0 |
| Kwak Kyung-taek | 6.0 |
| Chouain | 6.0 |
| Rita Faltoyano | 2.0 |
| Gmina Jabłonka | 1.0 |
| Do It | 4.0 |
| Alexandre Bertrand | 4.0 |
| Gebhard of Supplinburg | 2.0 |
| Gene Sheldon | 9.0 |
| Florencio Varela | 3.0 |
| So Many Tears | 1.0 |
| Bussloo | 1.0 |
| Q2388017 | 3.0 |
| RKSV Leonidas | 2.0 |
| 1987–88 Japan Soccer League | 2.0 |
| Q2407633 | 1.0 |
| Elizabeth of Carinthia, Queen of Germany | 16.0 |
| rue Pierre-Castagnou | 3.0 |
| bacteriology | 1.0 |
| 1984 Argentine Primera División | 2.0 |
| Amazon basin | 2.0 |
| East Singhbhum district | 3.0 |
| Gmina Zakliczyn | 2.0 |
| Gmina Jodłownik | 1.0 |
| Tyler | 319.0 |
| Abram van Rijckevorsel | 1.0 |
| Sally Thomsett | 2.0 |
| Q2470287 | 2.0 |
| Kate More | 4.0 |
| 14424 Laval | 2.0 |
| FC Prykarpattya Ivano-Frankivsk | 4.0 |
| Q2478997 | 4.0 |
| Cergy-Pontoise University | 1.0 |
| Anne of Lorraine, duchess of Aumale | 5.0 |
| Sakura Sake | 2.0 |
| Christian Décamps | 1.0 |
| Jeffrey Boam | 9.0 |
| Tronzano Vercellese | 13.0 |
| Dunsford | 1.0 |
| Da Nang | 8.0 |
| Velké Přílepy | 7.0 |
| Gorki | 3.0 |
| Will Gluck | 7.0 |
| Grandvaux | 3.0 |
| Belleisle-class ironclad | 2.0 |
| Rheinbrohl | 6.0 |
| Dorien | 9.0 |
| canton of Bois-Colombes | 2.0 |
| Tura Satana | 5.0 |
| 78125 Salimbeni | 1.0 |
| 23213 Ameliachang | 2.0 |
| 21856 Heathermaria | 2.0 |
| Push It | 5.0 |
| 25925 Jamesfenska | 2.0 |
| Ullånger | 1.0 |
| Tom Ripley | 5.0 |
| Rival Schools: United by Fate | 1.0 |
| Peggy Stewart | 1.0 |
| Ans Kremer | 6.0 |
| canton of Mérignac-1 | 2.0 |
| Saint-Anthème | 3.0 |
| Matthias Temmermans | 5.0 |
| Stan Ivar | 2.0 |
| Fraser River | 6.0 |
| Saint-Jean-en-Val | 1.0 |
| Úsilov | 4.0 |
| Marie of France, Duchess of Brabant | 5.0 |
| Don Pardo | 1.0 |
| Abby Elliott | 2.0 |
| 34696 Risoldi | 2.0 |
| Reggie Miller | 1.0 |
| Q2710378 | 2.0 |
| Andrew Wells | 1.0 |
| Răsuceni | 3.0 |
| Axintele | 3.0 |
| Mantyke | 2.0 |
| Q2725390 | 5.0 |
| Prague 13 | 1.0 |
| Derrick O'Connor | 14.0 |
| Bantega | 2.0 |
| Sumerian religion | 1.0 |
| William II Crispo | 2.0 |
| Where Are You? | 3.0 |
| 3rd: Love Escalation! | 2.0 |
| 5705 AM | 2.0 |
| Adam Williams | 13.0 |
| Chiry-Ourscamp | 3.0 |
| Bluffton | 5.0 |
| Hawise of Brittany | 3.0 |
| Iaai | 1.0 |
| Q2828863 | 2.0 |
| Alan Mills | 1.0 |
| Alena Procházková | 1.0 |
| Amanda Walsh | 8.0 |
| Amazing | 14.0 |
| André Forcier | 8.0 |
| Annie Dufresne | 3.0 |
| Q2854890 | 3.0 |
| Associated Artists Productions | 2.0 |
| Eaux-Puiseaux | 5.0 |
| Kim Ho-kon | 1.0 |
| avenue Dorian | 3.0 |
| avenue Gustave-V-de-Suède | 2.0 |
| Bill Chott | 3.0 |
| Bill Williams | 22.0 |
| Black Moses | 1.0 |
| Bobby Roth | 20.0 |
| The Masses Against the Classes | 2.0 |
| Lucille Bliss | 2.0 |
| Bruno Wolkowitch | 13.0 |
| Q2937410 | 1.0 |
| St. Joseph Cathedral | 1.0 |
| Metta Sandiford-Artest | 1.0 |
| October 18 | 6.0 |
| Christian Stolte | 5.0 |
| Q2969466 | 1.0 |
| Q2984731 | 1.0 |
| Q2989601 | 2.0 |
| Côtière | 1.0 |
| Morning, Noon and Night | 2.0 |
| Decas | 2.0 |
| Denise Clair | 5.0 |
| Silk | 13.0 |
| Doraid Liddawi | 5.0 |
| Drey'auc | 2.0 |
| Q3044979 | 2.0 |
| Dava Sobel | 1.0 |
| Q3051375 | 1.0 |
| New World Pictures | 6.0 |
| cadmium-107 | 1.0 |
| Gabe Sachs | 4.0 |
| Amiens railway station | 4.0 |
| Grésy-sur-Aix train station | 2.0 |
| Naratheinkha | 3.0 |
| Melocactus | 9.0 |
| La Vie de Bohème | 4.0 |
| Graham Edwards | 4.0 |
| Killeshandra | 3.0 |
| Rahul Khanna | 8.0 |
| Henriette Poincaré | 1.0 |
| Honda RVF750 RC45 | 1.0 |
| Hugo St-Cyr | 1.0 |
display(graph.outDegrees)
| id | outDegree |
|---|---|
| Maud Wood Park | 5.0 |
| Roland Merullo | 7.0 |
| Walter R. Mansfield | 11.0 |
| Madeleine M. Joullié | 27.0 |
| Seán McKiernan | 8.0 |
| Eugene Foss | 10.0 |
| Ethan Vogt | 8.0 |
| A Drink Before the War | 7.0 |
| Dana Bullen | 6.0 |
| Rudolf Löb | 7.0 |
| George Jung | 8.0 |
| Akiva Yaglom | 12.0 |
| George Hamilton Pearce | 9.0 |
| John A. Scali | 17.0 |
| Molenweg 1, Bemelen | 5.0 |
| Sabina Remundová | 11.0 |
| Zajíčkov | 7.0 |
| Q13429458 | 5.0 |
| Fortunato Baliani | 6.0 |
| Franziska Bennemann | 10.0 |
| Q17415823 | 5.0 |
| Wuzhi | 4.0 |
| Q17594869 | 5.0 |
| Volderstraat 5, Wijk bij Duurstede | 5.0 |
| Kanaalweg O.Z. | 4.0 |
| Bellamypark 35, Vlissingen | 5.0 |
| Lady Amelia Windsor | 9.0 |
| Vila Baleira | 4.0 |
| Der Verlorene | 25.0 |
| Walter Höflechner | 8.0 |
| Gregor Cankar | 9.0 |
| Peter Lalor | 7.0 |
| Franz von Gruithuisen | 9.0 |
| Rajeg | 3.0 |
| Ross Levinsohn | 8.0 |
| Poseidon | 71.0 |
| Jimmy Sherman | 9.0 |
| His Majesty, Bunker Bean | 16.0 |
| Norbert Ratsirahonana | 7.0 |
| Al Santos | 17.0 |
| Złoty Stok | 4.0 |
| Peter Nielsen | 26.0 |
| Kasper Bøgelund | 11.0 |
| Osek nad Bečvou | 13.0 |
| Beverley Callard | 5.0 |
| Palermo | 61.0 |
| Q14250552 | 3.0 |
| canton of Auxi-le-Château | 30.0 |
| Kenneth Bainbridge | 12.0 |
| Q18093523 | 6.0 |
| Valentin Alberti | 9.0 |
| Alseno | 12.0 |
| Q17607584 | 6.0 |
| Tanum Municipality | 12.0 |
| Heaven | 137.0 |
| Amelia Earhart: The Final Flight | 7.0 |
| Ellen Woodbury | 6.0 |
| Q4134070 | 10.0 |
| Leonella Sgorbati | 5.0 |
| Q14417787 | 3.0 |
| Lake Malawi | 6.0 |
| Francisco Chimoio | 8.0 |
| Janka Boga | 5.0 |
| Aragonese Party | 5.0 |
| António Lobo Antunes | 11.0 |
| Dogville | 36.0 |
| Li Qi | 162.0 |
| Richard Walter | 30.0 |
| Darren Baxter | 15.0 |
| Bob Sellers | 11.0 |
| Helena Wayne | 6.0 |
| Marie Helene Franey | 16.0 |
| Koonyum Sun | 4.0 |
| Pasawahan | 6.0 |
| The Setting Sun | 8.0 |
| The Quiller Memorandum | 25.0 |
| Clockwork | 12.0 |
| Ildemar Alcântara | 6.0 |
| Henri Ferrand | 11.0 |
| Gertrude Emily Benham | 7.0 |
| Vlasto Kopač | 8.0 |
| Louis-Albert Necker | 11.0 |
| Walter Frauenberger | 7.0 |
| Papaya | 4.0 |
| Abdoulaye Makhtar Diop | 5.0 |
| Alexandre Bouzaid | 6.0 |
| Diaraf Diouf | 6.0 |
| Isaac Forster | 6.0 |
| Kaffrine Department | 2.0 |
| Holzheim | 17.0 |
| In the Sultan's Garden | 8.0 |
| Two Memories | 21.0 |
| Superfly | 13.0 |
| L'Agnese va a morire | 26.0 |
| Q4788026 | 3.0 |
| Giancarlo Livraghi | 8.0 |
| Christian Wilhelm Kestner | 7.0 |
| The Swinging Confessors | 22.0 |
| Hong Kong 97 | 13.0 |
| Gradius IV | 9.0 |
| Nemesis | 114.0 |
| Koji Kumagai | 6.0 |
| 4686 Maisica | 6.0 |
| George Hepple | 8.0 |
| Heinz Maier-Leibnitz | 19.0 |
| 3792 Preston | 7.0 |
| Q11850657 | 5.0 |
| Q15065925 | 10.0 |
| Q15069596 | 10.0 |
| Q16447673 | 10.0 |
| Q16447958 | 9.0 |
| Q4072643 | 11.0 |
| Q4097389 | 10.0 |
| Q4147051 | 5.0 |
| Aleksey Danilov | 23.0 |
| Dmitry Sholokhov | 7.0 |
| Berndt Johan Hastfer | 6.0 |
| Gary O'Connor | 8.0 |
| William Wallace | 77.0 |
| Ross Callachan | 5.0 |
| 3338 Richter | 7.0 |
| Mitterskirchen | 10.0 |
| Kazuki Nagasawa | 11.0 |
| Pekka Lagerblom | 15.0 |
| Ritchey–Chrétien telescope | 5.0 |
| Gunnel Werner | 5.0 |
| Katy Butler | 6.0 |
| Laura Manuelidis | 8.0 |
| Giuseppe Lechi | 8.0 |
| Geert den Ouden | 13.0 |
| Reunion Blues | 6.0 |
| technetium-113 | 4.0 |
| Fujiwara no Kinsue | 13.0 |
| Always Zoku Sanchōme no Yūhi | 11.0 |
| Q11658317 | 41.0 |
| Stanley Glover | 7.0 |
| Q5966531 | 5.0 |
| ¡Ala... Dina! | 5.0 |
| The Fruitties | 6.0 |
| Sören Bertram | 7.0 |
| Kin | 4.0 |
| Ikeda | 16.0 |
| Dharampur | 5.0 |
| Sanand | 5.0 |
| West Vancouver | 3.0 |
| Q18587571 | 3.0 |
| Diner Dash: Hometown Hero | 12.0 |
| Yōichi Tomizuka | 5.0 |
| Sclerostagonospora | 3.0 |
| Yang Jian | 13.0 |
| Q17495144 | 7.0 |
| The Seine, Morning (Saint-Ouen) | 6.0 |
| Q18510964 | 3.0 |
| A City Is Beautiful at Night | 25.0 |
| Les Braqueuses | 20.0 |
| Daniel Wallard | 8.0 |
| Ricki Noel Lander | 6.0 |
| Sonny Guadarrama | 10.0 |
| Rival Schools: United by Fate | 12.0 |
| Legacy of Kain: Soul Reaver | 14.0 |
| Volfoss | 6.0 |
| Puzzle Bobble 3 | 10.0 |
| B.L.U.E. Legend of Water | 6.0 |
| SNCF | 10.0 |
| Q8253804 | 5.0 |
| John Welsh | 30.0 |
| Chris Priest | 9.0 |
| Roger Preece | 10.0 |
| Dean Greygoose | 8.0 |
| Dennis Hawkins | 8.0 |
| Joe Hinnigan | 11.0 |
| Princess Tangchang | 56.0 |
| Fordell Castle | 5.0 |
| Ramsay Garden | 5.0 |
| province of Argentina | 5.0 |
| Magnus Carlsen | 16.0 |
| Carbost | 6.0 |
| Hoshyar Zebari | 9.0 |
| 19 Fortuna | 8.0 |
| Q11416243 | 6.0 |
| Villedaigne | 8.0 |
| Vernajoul | 9.0 |
| Trevor Barker | 6.0 |
| Keith Drinan | 5.0 |
| Alister McRae | 6.0 |
| Q15649260 | 5.0 |
| 11581 Philipdejager | 6.0 |
| Q10868935 | 5.0 |
| Q11387600 | 5.0 |
| KissHug | 7.0 |
| Eaux-Puiseaux | 8.0 |
| 1436 | 8.0 |
| Henry A. Wiley | 12.0 |
| Shawan County | 25.0 |
| Changli County | 19.0 |
| Ursula Fischer | 11.0 |
| Ursula Summ | 6.0 |
| Wang Tao | 29.0 |
| Vladimir Berlinsky | 6.0 |
| Q6174723 | 6.0 |
| Antoine Reboulot | 10.0 |
| Georg von Oettl | 11.0 |
| Joseph Plateau | 13.0 |
| Q10946661 | 23.0 |
| Ceillac | 11.0 |
| Anyue County | 72.0 |
| Q10959311 | 3.0 |
| Q10884028 | 3.0 |
| Joyce Aluoch | 7.0 |
| The Dead Girl | 23.0 |
| Rinbung County | 12.0 |
| Longyao County | 16.0 |
| Wenquan County | 14.0 |
| American Journal of Medical Genetics | 3.0 |
| Giggi the bully | 23.0 |
| Tianyang District | 13.0 |
| canton of Mauléon-Barousse | 30.0 |
| In Search of Dr. Seuss | 8.0 |
| Sennar's Mission | 5.0 |
| Detinho | 14.0 |
| Raoyang County | 10.0 |
| Claire Chitham | 6.0 |
| Grimes | 9.0 |
| Claire Guieysse | 6.0 |
| Q17450733 | 5.0 |
| Q17451539 | 5.0 |
| Dieter Brüll | 7.0 |
| Donggang | 29.0 |
| Q19926280 | 6.0 |
| Excursion in Italian Countryside | 6.0 |
| Ippu Watanabe | 5.0 |
| Takuro Watanabe | 5.0 |
| Yoakum County | 6.0 |
| Trappe de Timadeuc | 6.0 |
| Époisses | 16.0 |
| Aitkin | 5.0 |
| Maestà of Sant'Agostino | 8.0 |
| Before I Go to Sleep | 23.0 |
| Dear Miss Lonelyhearts | 6.0 |
| Roland Arpin | 9.0 |
| Robert Pritzker | 8.0 |
| Li Yuanyu | 44.0 |
| Coprates quadrangle | 2.0 |
| Manuel Zozaya | 5.0 |
| Manuel Rojas Molina | 7.0 |
| Manuel Delgado Ruiz | 5.0 |
| Manuel do Nascimento Abreu | 5.0 |
| Q16268674 | 8.0 |
| Category:Deaths in Toano | 4.0 |
| Landelijke fietsroute 5 | 12.0 |
| Gardone Val Trompia | 10.0 |
| Ernst Franz Platen zu Hallermund | 5.0 |
| Justin Bailey | 5.0 |
| Justin Beriault | 5.0 |
| Justin Cooper | 6.0 |
| Justin Vaïsse | 6.0 |
| Nathan Malpass | 5.0 |
| Nathan Dahlberg | 6.0 |
| Amstenrade Castle: brick wall southwest of the gate to the vegetable garden | 6.0 |
| An Early Frost | 11.0 |
| Smile For Me | 5.0 |
| Time Distortion | 6.0 |
| Q11514720 | 6.0 |
| Marina Gilardoni | 6.0 |
| Marina Akulova | 8.0 |
| tantalum-182 | 4.0 |
| Bram Fischer | 10.0 |
| Henry George Fischer | 8.0 |
| Chelsea Girl | 7.0 |
| Bud Powell's Moods | 4.0 |
| Growing Up | 22.0 |
| Alan Garner | 25.0 |
| Q17217542 | 6.0 |
| Template:Redirect template | 1.0 |
| Anima Rossa | 6.0 |
| Q5835077 | 8.0 |
| Stay Gold | 22.0 |
| The Moth and the Flame | 18.0 |
| Ertan Demiri | 6.0 |
| Ohio State Route 450 | 8.0 |
| Iris Bay | 5.0 |
| MGM Grand Las Vegas | 6.0 |
| Cadillac Place | 6.0 |
| HHHR Tower | 5.0 |
| Alvadia | 3.0 |
| Fronteira | 6.0 |
| USS Samuel B. Roberts | 25.0 |
| Roger Osborne | 10.0 |
| Dan Severson | 10.0 |
| Wright Aeronautical | 3.0 |
| Flower | 77.0 |
| (73344) 2002 JT119 | 6.0 |
| 1993 IAAF World Cross Country Championships | 4.0 |
| Javier Muñoz Mustafá | 6.0 |
| Hugo Sánchez Portugal | 8.0 |
| Q11431386 | 6.0 |
| canton of Salies-du-Salat | 26.0 |
| Gary Woolsey | 5.0 |
| Q17334729 | 16.0 |
| Q19897620 | 7.0 |
| self-portrait in the nude | 15.0 |
| Q11661474 | 5.0 |
| East Singhbhum district | 3.0 |
| Saran district | 3.0 |
| Mamit district | 4.0 |
| Siddharthnagar district | 3.0 |
| Sindhudurg district | 4.0 |
| Gonda district | 3.0 |
| Pathanamthitta district | 4.0 |
| Alirajpur district | 4.0 |
| Fredonia | 7.0 |
| Mineichirō Adachi | 11.0 |
| pitcher | 5.0 |
| Q14326263 | 3.0 |
| William S. S. Willes | 5.0 |
| Amazing | 45.0 |
| Welcome to Heartbreak | 5.0 |
| Matsudaira Suketoshi | 6.0 |
| Q10892182 | 3.0 |
| canton of Laissac | 12.0 |
| Q11387497 | 5.0 |
| Q11659800 | 5.0 |
| Q1855763 | 4.0 |
| Sportpark Eschen-Mauren | 4.0 |
| Racecourse Ground | 6.0 |
| Benagéber | 6.0 |
| Wang Jun | 38.0 |
| Jonathan Duhamel | 5.0 |
| Jonathan Stokes | 8.0 |
| Jonathan Vervoort | 6.0 |
| Jonathan "Jazz" Russell | 6.0 |
| Vital Darbellay | 11.0 |
| Johann Gerhard König | 9.0 |
| Oretachi no Nirai Kanai | 6.0 |
| Solomon Hutcherson | 6.0 |
| Vener Galiev | 5.0 |
| Joe Moreira | 8.0 |
| Allan Weickert | 6.0 |
| Q16681870 | 13.0 |
| Q11635383 | 4.0 |
| Summer Eyes | 5.0 |
| Banksy | 13.0 |
| Henrietta Skerrett Montalba | 7.0 |
| Thomas Earp | 11.0 |
| Ole Sohn | 10.0 |
| Satomi Yoshihiro | 5.0 |
| Carlo Becchi | 7.0 |
| Q18953856 | 4.0 |
| Qian Weijun | 9.0 |
| Serpent's Path | 5.0 |
| Mauro Di Maggio | 6.0 |
| Mauro Bergamasco | 6.0 |
| Second Chorus | 19.0 |
| Sigurður Ragnar Eyjólfsson | 6.0 |
| Jatirejo | 3.0 |
| Purwodadi | 12.0 |
| Justin Speier | 5.0 |
| Přišimasy | 9.0 |
| Marina Antonazzoni | 7.0 |
| Piano Interpretations by Bud Powell | 5.0 |
| Q11281007 | 4.0 |
| Panoramic tower of La Duchère | 6.0 |
| Q11315798 | 4.0 |
| Ieke Moerdijk | 14.0 |
| Fermentelos | 3.0 |
| Carregueira | 3.0 |
| Ancede | 3.0 |
| União das Freguesias de Silveiros e Rio Covo (Santa Eulália) | 5.0 |
| Fushimi-no-miya Kunisuke-shinnō | 8.0 |
| (39099) 2000 WS12 | 5.0 |
| (39298) 2001 FV132 | 5.0 |
| John F. Bassett | 8.0 |
| Janice MacKinnon | 9.0 |
| Q11431344 | 6.0 |
| Q17340989 | 6.0 |
| Minamoto no Yoshiari | 16.0 |
| Q10916859 | 3.0 |
| Mette Karlsvik | 7.0 |
| Jonathan Hogg | 7.0 |
| Jonathan Larson | 10.0 |
| Rüdiger Vollborn | 7.0 |
| Pilar Jurado | 8.0 |
| Tokiwa Gozen | 8.0 |
| Mauro Formica | 8.0 |
| Mauro Calibani | 5.0 |
| 1947–48 Serie B | 5.0 |
| Wakana Kinoshita | 6.0 |
| Motoni Kinoshita | 6.0 |
| Assigny | 8.0 |
| Barjac | 24.0 |
| Nawaz Sharif | 13.0 |
| Samarkand | 23.0 |
| Southern German Jura | 1.0 |
| Marcelin Tamboulas | 6.0 |
| Otto I, Duke of Pomerania | 9.0 |
| Tamás Vígh | 5.0 |
| Williamson County | 12.0 |
| Jacob S. Coxey, Sr. | 9.0 |
| Perttu Hautala | 5.0 |
| Herrmannella | 3.0 |
| Felice and Boudleaux Bryant | 5.0 |
| Mainz | 36.0 |
| Sergei Rozanov | 15.0 |
| Ab Baars | 11.0 |
| Crisis | 59.0 |
| August Kappler | 9.0 |
| Jiří Baum | 10.0 |
| Nikifor Begichev | 5.0 |
| Ruurd Dirk Hoogland | 7.0 |
| Albert von Sack | 6.0 |
| Marcus Baker | 7.0 |
| Paule Bernard | 7.0 |
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| Annie Royle Taylor | 7.0 |
| Cayetano Pignatelli, 3rd Marquis of Rubí | 6.0 |
| Charles R. Bentley | 8.0 |
| Grenchenberg | 5.0 |
| Q7631098 | 3.0 |
| Waisenhausplatz | 4.0 |
| Büren Castle | 5.0 |
| Rebévelier | 5.0 |
| Emmental administrative district | 47.0 |
| Jerry Laterza | 18.0 |
| Q2828863 | 5.0 |
| Heurelho Gomes | 12.0 |
| Eiður Guðjohnsen | 18.0 |
| Q4779185 | 7.0 |
| Older Office Lady: Using Her Seductive Tongue | 8.0 |
| St. Jacobi (Church : Greifswald, Germany) | 6.0 |
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| Q10935925 | 3.0 |
| Q11138149 | 3.0 |
| Gabriela Schvartzman Muñoz | 5.0 |
| Gabriela Andersen-Schiess | 9.0 |
| Gabriela Dabrowski | 8.0 |
| Q10877075 | 3.0 |
| Iver Mysterud | 6.0 |
| Estadio Insular | 6.0 |
| Bever | 20.0 |
| Saint Boniface | 24.0 |
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| Museum Kurhaus Kleve | 7.0 |
| Order of Merit of North Rhine-Westphalia | 4.0 |
| Q18574093 | 5.0 |
| Golf Resort Tycoon II | 8.0 |
| Betsey Wright | 7.0 |
| Bill Sarpalius | 11.0 |
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| Q1380617 | 5.0 |
| Q2358753 | 3.0 |
| Q2596022 | 6.0 |
| St.-Anna-Kirche | 15.0 |
| Effolderbach | 3.0 |
| Holy Cross Church | 37.0 |
| Cappel | 12.0 |
| Q11092323 | 3.0 |
| Q11145764 | 3.0 |
| Q11081871 | 3.0 |
| Q13973940 | 3.0 |
| Altenkirchen | 14.0 |
| Q13774956 | 3.0 |
| John Blaikie | 6.0 |
| Antwerp | 61.0 |
| Q12039565 | 5.0 |
| Langenau | 13.0 |
| Putzkau | 3.0 |
| Don Alfonso | 6.0 |
| He Xiangning | 6.0 |
| Doroteo Flores | 13.0 |
| V čajové konvici | 5.0 |
| Nives Poli | 7.0 |
| Inez from Hollywood | 6.0 |
| Disraeli | 34.0 |
| Dorothy Adams | 7.0 |
| Wittichenau | 5.0 |
| Stadtpfarrkirche St. Nikolai | 7.0 |
| Dom St.Peter und Paul in Brandenburg | 6.0 |
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| Q11080727 | 3.0 |
| Grayfolded | 6.0 |
| Sarah Mary Fitton | 6.0 |
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| IANA time zone | 2.0 |
| The Hunchback of Soho | 24.0 |
| Rock County | 11.0 |
| Bacchus and Ariadne | 103.0 |
| Hans-Georg von Seidel | 10.0 |
| Q4199586 | 6.0 |
| Adina Mandlová | 8.0 |
| Otto Toeplitz | 14.0 |
| Louise of Orléans | 17.0 |
| François Debeauvais | 9.0 |
| Archie Jackson | 7.0 |
| Dan Alderson | 5.0 |
| Ray Gravell | 9.0 |
| Christopher Hewett | 8.0 |
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| Squadron 34 | 8.0 |
| Karen Carney | 12.0 |
| Karen Armstrong | 10.0 |
| Grivola | 5.0 |
| Melocactus | 4.0 |
| Antonio León Ortega | 7.0 |
| Anywhere Is | 6.0 |
| Shawo Township | 9.0 |
| (69590) 1998 EL8 | 5.0 |
| Q12280297 | 6.0 |
| Giuseppe Pennaroli | 8.0 |
| Wasyl Ciapiński | 7.0 |
| Rumilly | 15.0 |
| Torao Ōoka | 6.0 |
| Kaori Yuasa | 6.0 |
| Rio Hasegawa | 5.0 |
| The First Day of the Rest of Your Life | 22.0 |
| Bo Thomas | 5.0 |
| Bo Ralph | 9.0 |
| Q12340630 | 5.0 |
| Ray Petri | 5.0 |
| Hendrik C. van de Hulst | 11.0 |
| Charles Heycock | 5.0 |
| Peter Lawrenson | 9.0 |
| Richard Edwin Hills | 7.0 |
| Edward Littleton, 1st Baron Hatherton | 10.0 |
| Alexander Gordon, 4th Duke of Gordon | 11.0 |
| William Dickson Lang | 6.0 |
| Ghillean Prance | 7.0 |
| Robert McCance | 5.0 |
| Marco Foscarini | 13.0 |
| Stanley B. Prusiner | 15.0 |
| Archibald Primrose, 5th Earl of Rosebery | 13.0 |
| Charles Berkeley, 2nd Earl of Berkeley | 17.0 |
| John Barlow | 25.0 |
| Henry Hyde, 2nd Earl of Clarendon | 12.0 |
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| Henry Chamberlain Russell | 7.0 |
| Maurice FitzGerald, 18th Knight of Kerry | 8.0 |
| Isaac Todhunter | 14.0 |
| Roddam Narasimha | 9.0 |
| Frank Rose | 5.0 |
| Henry Elderfield | 7.0 |
| Richard R. Ernst | 15.0 |
| Guillaume Mazeas | 8.0 |
| James Rennie | 17.0 |
| Adrian John Brown | 9.0 |
| John Cunningham McLennan | 13.0 |
| Samuel Clarke | 16.0 |
| Robert Edward Bell | 12.0 |
| Worcester | 22.0 |
| October 18 | 4.0 |
| 5483 AM | 3.0 |
| Windmill British Cemetery | 6.0 |
| Conus tribblei | 4.0 |
| Hagar and the Angel | 25.0 |
| Wulfnoth Godwinson | 12.0 |
| Landeyrat | 10.0 |
| Saraband for Dead Lovers | 23.0 |
| The Young Ones | 35.0 |
| Dhul-Nun al-Misri | 17.0 |
| Edmund Beaufort, 2nd Duke of Somerset | 22.0 |
| Ōnaruto Bridge | 4.0 |
| meitnerium-268 | 4.0 |
| Gabrias | 5.0 |
| Antrenas | 5.0 |
| Angels' Story | 4.0 |
| Amulet | 9.0 |
| Jean de Breteuil | 6.0 |
| Claude-Jean Philippe | 9.0 |
| Arres | 8.0 |
| Haudonville | 5.0 |
| Xivry-Circourt | 5.0 |
| Beuvillers | 14.0 |
| Neuviller-lès-Badonviller | 5.0 |
| Audun-le-Roman | 13.0 |
| Maxéville | 7.0 |
| Kerbach | 5.0 |
| Saint-Privat-la-Montagne | 5.0 |
| Les Étangs | 5.0 |
| Secourt | 4.0 |
| Chlothar I | 26.0 |
| Marguerite d'Évreux | 11.0 |
| 1572 | 8.0 |
| Gediz | 3.0 |
| Saint-Omer-Capelle | 4.0 |
| Estrée | 4.0 |
| Heuchin | 4.0 |
| Avondance | 4.0 |
| La Thieuloye | 4.0 |
| Condette | 4.0 |
| FH1 | 4.0 |
| Psammphiletria | 3.0 |
| Life Before Insanity | 5.0 |
| Esquièze-Sère | 5.0 |
| Capvern | 5.0 |
| Boulin | 5.0 |
| canton of Pouyastruc | 32.0 |
| Bize | 9.0 |
| Salles-Adour | 5.0 |
| Aventignan | 5.0 |
| Arzacq-Arraziguet | 4.0 |
| Higuères-Souye | 4.0 |
| Ossau Valley | 4.0 |
| Sainte-Colome | 4.0 |
| Navarrenx | 7.0 |
| Mirepeix | 4.0 |
| Soghad | 4.0 |
| Marie Ephrem Garrelon | 8.0 |
| Cousin Cousine | 18.0 |
| Charlie Dingo | 12.0 |
| Sant Llorenç de Morunys | 7.0 |
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| Expresul de Buftea | 8.0 |
| Oberbüren | 14.0 |
| Battle of Neretva | 31.0 |
| Giulia Gam | 5.0 |
| Beaumont-de-Lomagne | 5.0 |
| 2014 Australian Open – men's singles | 3.0 |
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| Bill Vukovich | 10.0 |
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| canton of Rohan | 11.0 |
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| Filippo della Valle | 7.0 |
| Serge Mangin | 7.0 |
| José María Sá Lemos | 7.0 |
| Friedrich August Theodor Dietrich | 7.0 |
| Heinrich Pohlmann | 6.0 |
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| Peter Briggs | 17.0 |
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| Astrid Rietz | 5.0 |
| Joaquín Torres-García | 12.0 |
| Innocenzo Fraccaroli | 8.0 |
| Michael Sailstorfer | 9.0 |
| Ela Venbroek-Franczyk | 5.0 |
| Henri Gourgouillon | 5.0 |
| Noël Pasquier | 5.0 |
| Gino Micheli | 6.0 |
| Carmelo Cappello | 7.0 |
| Adam Rammelmeyer | 7.0 |
| Frans Wuytack | 7.0 |
| Franz Anton von Zauner | 5.0 |
| Georg Wiese | 6.0 |
| Ingrid Steininger | 5.0 |
| Fred Mennens | 5.0 |
| Grigoriy Myasoyedov | 7.0 |
| Carlos Santiago Mérida | 8.0 |
| Cesare Antonio Canavese | 6.0 |
| Franz Sautner | 6.0 |
| Alfredo Torán | 8.0 |
| Syrius Eberle | 8.0 |
| Oronzo Abbatecola | 8.0 |
| Jacques Bernus | 5.0 |
| Eugen Eckert | 14.0 |
| Jeff Koons | 14.0 |
| Marin-Nicolas Jadoulle | 6.0 |
| Nanni di Bartolo | 5.0 |
| Ferruccio Ferrazzi | 8.0 |
| Chris Ofili | 10.0 |
| Francis Delivré | 5.0 |
| C. Paul Jennewein | 9.0 |
| Ramón Rogent | 9.0 |
| Arcangelo Ianelli | 6.0 |
| Robert Stigell | 6.0 |
| Heinrich Brabender | 9.0 |
| David Adickes | 6.0 |
| Fokion Rok | 5.0 |
| Stefan Esterbauer | 5.0 |
| Henri-Léon Gréber | 9.0 |
| Adolf Brütt | 8.0 |
| Valeriano Trubbiani | 6.0 |
| Buon Gesù | 5.0 |
| El Atazar Reservoir | 6.0 |
| Kernascléden | 7.0 |
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| Zarrinabad District | 2.0 |
| Norm Van Lier | 12.0 |
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| The Church on Cumberland Road | 5.0 |
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| Richard Diamond, Private Detective, season 2 | 5.0 |
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| Richard Frothingham, Jr. | 13.0 |
| Q4133711 | 5.0 |
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| Snježana Kordić | 28.0 |
| Isaac Gálvez | 10.0 |
| Maurice Lippens | 20.0 |
| Alexandre Galopin | 8.0 |
| 1910 Finnish football championship | 5.0 |
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| Ferdinande von Brackel | 7.0 |
| Rational Youth | 2.0 |
| Chromatics | 2.0 |
| The Platinum Collection | 68.0 |
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| Q15634970 | 3.0 |
| John Curtis | 53.0 |
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| Akinobu Uraka | 7.0 |
| Kōji Takeda | 5.0 |
| Kotani Sumiyuki | 6.0 |
| Q17095892 | 4.0 |
| Germain Iglesias | 5.0 |
| Poecilopsettidae | 3.0 |
| Mathieu Ravignat | 8.0 |
| Al-Khayzuran | 5.0 |
| Mieke Senftleben | 7.0 |
| Rainer Funke | 10.0 |
| Horst Armbrust | 7.0 |
| Elsa Friederike Wilhelmine Teuffert | 9.0 |
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| Johann Peter Brandenburg | 12.0 |
| Ribera Alta | 46.0 |
| Marc Cordeel | 8.0 |
| Ornithosuchidae | 3.0 |
| Greg Stiemsma | 15.0 |
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| Henry Phillips | 12.0 |
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| Q3091427 | 5.0 |
| Ferocactus chrysacanthus | 4.0 |
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| Sergio Massa | 12.0 |
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| 33 dead | 8.0 |
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| Jean Meyer | 29.0 |
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| María Julia Alsogaray | 11.0 |
| Putin elected to third term; observers express concern | 5.0 |
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| Marosa di Giorgio | 7.0 |
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| Funeral of Pope John Paul II takes place | 5.0 |
| María Sofía Castro Romero | 8.0 |
| Luis Quiñones de Benavente | 9.0 |
| Gilberto Owen | 8.0 |
| Mayra Santos-Febres | 9.0 |
| Elsa Cross | 9.0 |
| Julián Domínguez | 10.0 |
| Eduardo Barredo | 5.0 |
| Mauricio-José Schwarz | 5.0 |
| The Newsie and the Lady | 12.0 |
| Jorge Francisco Sotomayor | 9.0 |
| Mario Benedetti | 25.0 |
| Alejandro Gutiérrez Gutiérrez | 8.0 |
| Valentí Almirall i Llozer | 10.0 |
| Emilio Palomo Aguado | 5.0 |
| Delia Fiallo | 5.0 |
| Autumn Sun | 6.0 |
| Quince Duncan | 5.0 |
| French journalist held hostage in Iraq freed | 5.0 |
| 7 mujeres, 1 homosexual y Carlos | 7.0 |
| Miguel Capistrán | 6.0 |
| Nagykáta | 5.0 |
| Lorne J. Acquin | 5.0 |
| Washington and Lee University School of Law | 3.0 |
| Nando mo Yume no Naka de Kurikaesu Love Song/Afureru Omoi | 6.0 |
| township of Myanmar | 4.0 |
| My Name Is Khan | 24.0 |
| The Day After Judgment | 8.0 |
| The Pool of the Black One | 13.0 |
| Riverworld | 3.0 |
| Eldest | 10.0 |
| The Frost-Giant's Daughter | 8.0 |
| The Sleeping and the Dead | 6.0 |
| Coliseum Building | 9.0 |
| Q14102039 | 3.0 |
| Fabrizio Poletti | 8.0 |
| Gianni Rivera | 19.0 |
| Auguste Anicet-Bourgeois | 12.0 |
| I Am a Camera | 15.0 |
| Raoul and the Kings of Spain | 10.0 |
| Elina Anttilainen | 5.0 |
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| Afghanistan–Tajikistan border | 5.0 |
| Bolivia–Chile border | 4.0 |
| Liberia–Sierra Leone border | 5.0 |
| Q10867215 | 3.0 |
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| Renmin Road Subdistrict, Jiujiang | 3.0 |
| Freaked | 23.0 |
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| Alfonso II d'Este | 8.0 |
| Shadow of Night | 9.0 |
| 2294 | 4.0 |
| 6194 | 4.0 |
| 7762 | 4.0 |
| 2088 | 7.0 |
| 3414 | 4.0 |
| 2904 | 4.0 |
| 3606 | 4.0 |
| 2162 | 5.0 |
| 9586 | 4.0 |
| 1090 | 8.0 |
| 2136 | 7.0 |
| 7252 | 4.0 |
| 8304 | 4.0 |
| 4032 | 5.0 |
| 9030 | 4.0 |
| 1512 | 8.0 |
| 3210 | 4.0 |
| 6240 | 4.0 |
| 296 | 8.0 |
| Helen Craig | 6.0 |
| Helen Jones | 11.0 |
| Helen Schulman | 7.0 |
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| Karl Hipfinger | 8.0 |
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| Laurie Hernandez | 6.0 |
| Walter Bach | 6.0 |
| Kenzo Shirai | 6.0 |
| Laura Jurca | 6.0 |
| Vitaly Scherbo | 9.0 |
| Arthur Townsend | 10.0 |
| John Tarrant | 4.0 |
| Lyudmila Petrova | 8.0 |
| Elva Dryer | 6.0 |
| Benjamin Maiyo | 5.0 |
| Georg Østerholt | 6.0 |
| Maciej Banaszak | 5.0 |
| Fengshan | 3.0 |
| Gerardo Landriani Capitani | 7.0 |
| Conophytum obcordellum | 3.0 |
| Lewis and Clark County | 4.0 |
| Canadian County | 5.0 |
| Artur Greive | 6.0 |
| Q834727 | 6.0 |
| Gerhard Helbig | 17.0 |
| Alessandro Torri | 8.0 |
| Paul Kußmaul | 7.0 |
| Anne Katrine Frihagen | 6.0 |
| Vincenzio Nannucci | 7.0 |
| Uwe Böker | 6.0 |
| Brian Freeman | 13.0 |
| Da Nang | 6.0 |
| Greg Howard | 19.0 |
| Thomas Mellon | 8.0 |
| Q11181146 | 3.0 |
| Donald R. Haurin | 5.0 |
| Donald Yamamoto | 7.0 |
| Don Cameron | 48.0 |
| Donald Dell | 10.0 |
| Donald Calthrop | 8.0 |
| Donald Charles Baldwin | 5.0 |
| Don Beyer | 10.0 |
| Landscape, Branchville | 5.0 |
| James Archer | 25.0 |
| group portrait | 1.0 |
| Giovanna Bellelli | 7.0 |
| Q17492376 | 7.0 |
| Q17492608 | 8.0 |
| Square portrait of Pieter Jacobsz Olycan | 9.0 |
| Richard Westall | 6.0 |
| Q3399523 | 6.0 |
| This Corner Don’t Pay | 13.0 |
| A Country Lad | 20.0 |
| Seated Bather | 5.0 |
| Yoryi Morel | 5.0 |
| Portrait Study | 9.0 |
| The Poet Philippe Soupault | 8.0 |
| Portrait of Cornelis de Witt (1623-72), Burgomaster of Dordrecht and lord lieutenant of Putten | 10.0 |
| George Francis Joseph | 5.0 |
| Heinrich Dohm | 5.0 |
| Portrait of Tieleman Roosterman | 7.0 |
| Léon Bonnat | 17.0 |
| Portrait of Michiel Jansz. van Middelhoven | 8.0 |
| Prince Hoare | 7.0 |
| Christoph Metzler | 9.0 |
| Reinhold Einwallner | 9.0 |
| Q14865205 | 9.0 |
| Kimon Koulouris | 10.0 |
| Sophocles Venizelos | 20.0 |
| Konstantinos Stefanakis | 12.0 |
| Amaryllis | 20.0 |
| The Beginning | 56.0 |
| Fredrikssons fabrikk – The movie | 5.0 |
| Q11131360 | 3.0 |
| René Graciet | 9.0 |
| Thierry Sandre | 10.0 |
| Q17224485 | 5.0 |
| Crane Yu | 5.0 |
| Mina Taniyama | 5.0 |
| Atsuo Sawada | 6.0 |
| Masakatsu Funaki | 7.0 |
| Diogo da Costa Oliveira | 5.0 |
| Béla Imrédy | 11.0 |
| Stuartina | 3.0 |
| Kadaru Nagabhushanam | 7.0 |
| Volgograd | 36.0 |
| Aayutha Ezhuthu | 16.0 |
| Alaipayuthey | 12.0 |
| S. P. B. Charan | 8.0 |
| Sidharth Bharathan | 7.0 |
| Sarvepalli Radhakrishnan | 19.0 |
| Hannan Majid | 6.0 |
| 1892 Wimbledon Championships – gentlemen's singles | 4.0 |
| Gaoxin | 3.0 |
| Q16065309 | 6.0 |
| George Bugliarello | 5.0 |
| Nanda Lwin | 7.0 |
| Mami Suzuki | 7.0 |
| Q10944741 | 3.0 |
| Q11096205 | 3.0 |
| Stříbro | 20.0 |
| Q17341343 | 5.0 |
| Q17443680 | 5.0 |
| Q17599109 | 7.0 |
| Q17615804 | 5.0 |
| Simona Sodini | 6.0 |
| Angelo Roth | 9.0 |
| Q3823496 | 8.0 |
| Crossing Over | 28.0 |
| Emil Kremenliev | 16.0 |
| Giorgio Vasari | 23.0 |
| Benny Beimer | 15.0 |
| Pocona Municipality | 3.0 |
| Q11610582 | 5.0 |
| Nakayama Station | 8.0 |
| Corbère | 11.0 |
| Tim Maculan | 6.0 |
| Tim Hölscher | 6.0 |
| Tim Greve | 11.0 |
| Tim Braeutigam | 5.0 |
| Tim & Bob | 7.0 |
| Tim Hughes | 7.0 |
| Tim B. Heaton | 7.0 |
| Tim Lankester | 7.0 |
| Millas | 15.0 |
| canton of Latour-de-France | 14.0 |
| Ladislaus, Count Esterházy | 10.0 |
| Etel Adnan | 13.0 |
| African Cookbook | 4.0 |
| Parliament of Western Australia | 4.0 |
| Hubert Nuss | 8.0 |
| Hubert van Asch van Wijck | 6.0 |
| Hubert Doggart | 7.0 |
| Q10870299 | 3.0 |
| Q3576869 | 13.0 |
| Ballesteros | 4.0 |
| Lal-lo | 4.0 |
| Walram, Count of Jülich | 5.0 |
| San Andres | 15.0 |
| Q9632926 | 5.0 |
| Castellcir | 12.0 |
| Geraldine Chaplin | 18.0 |
| Giuseppe Tartini | 10.0 |
| Cryptocephalus | 3.0 |
| James Berardinelli | 7.0 |
| The Law and Jake Wade | 15.0 |
| Marianne Koller-Bohl | 7.0 |
| Christiane Langenberger | 11.0 |
| James Fazy | 14.0 |
| Hermann Liechti | 11.0 |
| Maurice Péquignot | 9.0 |
Motif finding example
We can use the find-method to specify and search for simple motifs (https://graphframes.github.io/graphframes/docs/_site/api/scala/org/graphframes/GraphFrame.html#find(pattern:String):org.apache.spark.sql.DataFrame). Let's start by looking for some cycles:
//val allTwoCycles = List("(a)-[r1]->(b); (a)-[r2]->(c)", "(a)-[r1]->(b); (b)-[r2]->(a)", "(a)-[r1]->(b); (b)-[r2]->(c)", "(a)-[r1]->(b); (c)-[r2]->(a)", "(a)-[r1]->(b); (c)-[r2]->(b)", "(a)-[r1]->(c); (b)-[r2]->(a)", "(a)-[r1]->(c); (b)-[r2]->(c)", "(a)-[r1]->(c); (c)-[r2]->(a)", "(a)-[r1]->(c); (c)-[r2]->(b)", "(b)-[r1]->(a); (b)-[r2]->(c)", "(b)-[r1]->(a); (c)-[r2]->(a)", "(b)-[r1]->(a); (c)-[r2]->(b)", "(b)-[r1]->(c); (c)-[r2]->(a)", "(b)-[r1]->(c); (c)-[r2]->(b)", "(c)-[r1]->(a); (c)-[r2]->(b)")
/*val twoCycles1 = graph.find("(a)-[r1]->(b); (b)-[r2]->(a)") // Find symmetric motifs
val twoCycles2 = graph.find("")
display(twoCycles)*/
//val allThreeCycles = List("(a)-[r1]->(b); (a)-[r2]->(c); (b)-[r3]->(a)", "(a)-[r1]->(b); (a)-[r2]->(c); (b)-[r3]->(c)", "(a)-[r1]->(b); (a)-[r2]->(c); (c)-[r3]->(a)", "(a)-[r1]->(b); (a)-[r2]->(c); (c)-[r3]->(b)", "(a)-[r1]->(b); (b)-[r2]->(a); (b)-[r3]->(c)", "(a)-[r1]->(b); (b)-[r2]->(a); (c)-[r3]->(a)", "(a)-[r1]->(b); (b)-[r2]->(a); (c)-[r3]->(b)", "(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(a)", "(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(b)", "(a)-[r1]->(b); (c)-[r2]->(a); (c)-[r3]->(b)", "(a)-[r1]->(c); (b)-[r2]->(a); (b)-[r3]->(c)", "(a)-[r1]->(c); (b)-[r2]->(a); (c)-[r3]->(a)", "(a)-[r1]->(c); (b)-[r2]->(a); (c)-[r3]->(b)", "(a)-[r1]->(c); (b)-[r2]->(c); (c)-[r3]->(a)", "(a)-[r1]->(c); (b)-[r2]->(c); (c)-[r3]->(b)", "(a)-[r1]->(c); (c)-[r2]->(a); (c)-[r3]->(b)", "(b)-[r1]->(a); (b)-[r2]->(c); (c)-[r3]->(a)", "(b)-[r1]->(a); (b)-[r2]->(c); (c)-[r3]->(b)", "(b)-[r1]->(a); (c)-[r2]->(a); (c)-[r3]->(b)", "(b)-[r1]->(c); (c)-[r2]->(a); (c)-[r3]->(b)")
val twoCycles1 = graph.find("(a)-[r1]->(b); (b)-[r2]->(c)")
val twoCycles2 = graph.find("(a)-[r1]->(b); (c)-[r3]->(a)") // Note
val twoCycles3 = graph.find("(b)-[r2]->(c); (c)-[r3]->(a)")
val threeCycles = graph.find("(a)-[r1]->(b); (b)-[r2]->(c); (c)-[r3]->(a)")
twoCycles1: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 3 more fields]
twoCycles2: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 3 more fields]
twoCycles3: org.apache.spark.sql.DataFrame = [b: struct<id: string>, r2: struct<src: string, rel: string ... 1 more field> ... 3 more fields]
threeCycles: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 4 more fields]
display(threeCycles)
display(twoCycles1)
twoCycles1.show()
+--------------------+--------------------+--------------------+--------------------+--------------------+
| a| r1| b| r2| c|
+--------------------+--------------------+--------------------+--------------------+--------------------+
| {& Yet & Yet}|{& Yet & Yet, fol...|{Goodbye Enemy Ai...|{Goodbye Enemy Ai...| {& Yet & Yet}|
|{You, You're a Hi...|{You, You're a Hi...|{Winter Hymn Coun...|{Winter Hymn Coun...| {& Yet & Yet}|
| {& Yet & Yet}|{& Yet & Yet, fol...|{Winter Hymn Coun...|{Winter Hymn Coun...| {& Yet & Yet}|
| {(10499) 1986 RN5}|{(10499) 1986 RN5...| {10498 Bobgent}|{10498 Bobgent, f...| {(10499) 1986 RN5}|
| {(10497) 1986 RQ}|{(10497) 1986 RQ,...| {10498 Bobgent}|{10498 Bobgent, f...| {(10499) 1986 RN5}|
| {(10499) 1986 RN5}|{(10499) 1986 RN5...| {10500 Nishi-koen}|{10500 Nishi-koen...| {(10499) 1986 RN5}|
| {10501 Ardmacha}|{10501 Ardmacha, ...| {10500 Nishi-koen}|{10500 Nishi-koen...| {(10499) 1986 RN5}|
| {11056 Volland}|{11056 Volland, f...| {(11057) 1991 NL}|{(11057) 1991 NL,...| {(11058) 1991 PN10}|
| {(11058) 1991 PN10}|{(11058) 1991 PN1...| {(11057) 1991 NL}|{(11057) 1991 NL,...| {(11058) 1991 PN10}|
| {(11058) 1991 PN10}|{(11058) 1991 PN1...|{11059 Nulliusinv...|{11059 Nulliusinv...| {(11058) 1991 PN10}|
| {(11060) 1991 RA13}|{(11060) 1991 RA1...|{11059 Nulliusinv...|{11059 Nulliusinv...| {(11058) 1991 PN10}|
| {(117404) 2005 AC9}|{(117404) 2005 AC...| {(117403) 2005 AO8}|{(117403) 2005 AO...| {(117404) 2005 AC9}|
| {(13020) 1988 PW2}|{(13020) 1988 PW2...| {(13021) 1988 RY5}|{(13021) 1988 RY5...| {(13020) 1988 PW2}|
| {(13022) 1988 RL9}|{(13022) 1988 RL9...| {(13021) 1988 RY5}|{(13021) 1988 RY5...| {(13020) 1988 PW2}|
|{(136198) 2003 UJ...|{(136198) 2003 UJ...| {Eris}|{Eris, follows, (...|{(136198) 2003 UJ...|
| {Apple of Discord}|{Apple of Discord...| {Eris}|{Eris, follows, (...|{(136198) 2003 UJ...|
| {(136200) 2003 VS5}|{(136200) 2003 VS...| {Eris}|{Eris, follows, (...|{(136198) 2003 UJ...|
| {Dysnomia}|{Dysnomia, parent...| {Eris}|{Eris, follows, (...|{(136198) 2003 UJ...|
| {Eris}|{Eris, named afte...| {Eris}|{Eris, follows, (...|{(136198) 2003 UJ...|
| {Iliad}|{Iliad, character...| {Eris}|{Eris, follows, (...|{(136198) 2003 UJ...|
+--------------------+--------------------+--------------------+--------------------+--------------------+
only showing top 20 rows
Now, we should count how often this the TwoCycle motif we had is a submotif of the ThreeCycle we were looking for.
We have 20*15 = 3000 different combinations of submotifs. With each TwoCycle motif pattern taking approximately 2 minutes, and each ThreeCycle motif taking about X minutes, we would need quite some time to process all of this.
twoCycles1.createOrReplaceTempView("twoCycles1")
threeCycles.createOrReplaceTempView("threeCycles")
command-2098477901282047:1: warning: method registerTempTable in class Dataset is deprecated (since 2.0.0): Use createOrReplaceTempView(viewName) instead.
twoCycles1.registerTempTable("twoCycles1")
^
command-2098477901282047:2: warning: method registerTempTable in class Dataset is deprecated (since 2.0.0): Use createOrReplaceTempView(viewName) instead.
threeCycles.registerTempTable("threeCycles")
^
var xx_ = sqlContext.sql(
"""
SELECT r1.rel from threeCycles LIMIT 10
""")
val xx = xx_
xx.show()
+----------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| a| r1| b| r2| c| r3|
+----------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| {Nina Yankovic}|{Nina Yankovic, f...|{"Weird Al" Yanko...|{"Weird Al" Yanko...|{"Weird Al" Yanko...|{"Weird Al" Yanko...|
| {Nina Yankovic}|{Nina Yankovic, m...| {Suzanne Yankovic}|{Suzanne Yankovic...|{"Weird Al" Yanko...|{"Weird Al" Yanko...|
| {Eris}|{Eris, named afte...| {Eris}|{Eris, follows, (...|{(136198) 2003 UJ...|{(136198) 2003 UJ...|
|{clarithromycin}|{clarithromycin, ...| {chlorpromazine}|{chlorpromazine, ...| {(RS)-sulpiride}|{(RS)-sulpiride, ...|
|{clarithromycin}|{clarithromycin, ...| {dofetilide}|{dofetilide, sign...| {(RS)-sulpiride}|{(RS)-sulpiride, ...|
|{clarithromycin}|{clarithromycin, ...| {dronedarone}|{dronedarone, sig...| {(RS)-sulpiride}|{(RS)-sulpiride, ...|
|{clarithromycin}|{clarithromycin, ...| {sevoflurane}|{sevoflurane, sig...| {(RS)-sulpiride}|{(RS)-sulpiride, ...|
|{clarithromycin}|{clarithromycin, ...| {disopyramide}|{disopyramide, si...| {(RS)-sulpiride}|{(RS)-sulpiride, ...|
|{clarithromycin}|{clarithromycin, ...| {thioridazine}|{thioridazine, si...| {(RS)-sulpiride}|{(RS)-sulpiride, ...|
|{clarithromycin}|{clarithromycin, ...| {bepridil}|{bepridil, signif...| {(RS)-sulpiride}|{(RS)-sulpiride, ...|
+----------------+--------------------+--------------------+--------------------+--------------------+--------------------+
xx_: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 4 more fields]
xx: org.apache.spark.sql.DataFrame = [a: struct<id: string>, r1: struct<src: string, rel: string ... 1 more field> ... 4 more fields]
var res = sqlContext.sql(
"""
WITH t1 as (
), t2 as (
)
SELECT t1.r1.rel AS rel1, t1.r2.rel AS rel2, COUNT(*) AS total2cycles, COUNT_IF(t1.r3 IS NOT NULL) AS total3cycles, COUNT_IF(t1.r3 IS NOT NULL)/COUNT(*) freq FROM threeCycles t1
LEFT JOIN twoCycles1 t2 WHERE t1.r1 = t2.r1 AND t1.r2 = t2.r2
GROUP BY rel1, rel2
ORDER BY total2cycles, total3cycles DESC
"""
)
val result = res.toDF()
res: org.apache.spark.sql.DataFrame = [rel1: string, rel2: string ... 3 more fields]
result: org.apache.spark.sql.DataFrame = [rel1: string, rel2: string ... 3 more fields]
result.show()
var res = sqlContext.sql("""describe twoCycles1;""")
+--------+--------------------+-------+
|col_name| data_type|comment|
+--------+--------------------+-------+
| a| struct<id:string>| null|
| r1|struct<src:string...| null|
| b| struct<id:string>| null|
| r2|struct<src:string...| null|
| c| struct<id:string>| null|
+--------+--------------------+-------+
res: org.apache.spark.sql.DataFrame = [col_name: string, data_type: string ... 1 more field]
result: org.apache.spark.sql.DataFrame = [col_name: string, data_type: string ... 1 more field]
val res = sqlContext.sql("""SELECT t2.r1.rel as rel1, t2.r2.rel as rel2, COUNT(*) as freq
FROM twoCycles1 t2
GROUP BY t2.r1.rel, t2.r2.rel
ORDER BY COUNT(*) DESC LIMIT 1000""")
var result = res.toDF()
//result.show()
res: org.apache.spark.sql.DataFrame = [rel1: string, rel2: string ... 1 more field]
result: org.apache.spark.sql.DataFrame = [rel1: string, rel2: string ... 1 more field]
display(result)
| rel1 | rel2 | freq |
|---|---|---|
| country of citizenship | diplomatic relation | 3.6845941e7 |
| country of citizenship | contains the administrative territorial entity | 3.474579e7 |
| country of citizenship | member of | 2.1953345e7 |
| located in the administrative territorial entity | contains the administrative territorial entity | 1.4476948e7 |
| country of citizenship | head of government | 1.3609197e7 |
| country | contains the administrative territorial entity | 1.1929348e7 |
| country of citizenship | shares border with | 1.1911693e7 |
| instance of | has quality | 9121996.0 |
| given name | said to be the same as | 8206101.0 |
| country of citizenship | instance of | 8196974.0 |
| instance of | subclass of | 8151582.0 |
| country | member of | 8103113.0 |
| place of birth | twinned administrative body | 7145829.0 |
| country | diplomatic relation | 5864592.0 |
| country of citizenship | located in time zone | 5828225.0 |
| country of citizenship | head of state | 5780061.0 |
| country of citizenship | public holiday | 5258937.0 |
| given name | instance of | 5123325.0 |
| place of death | twinned administrative body | 4732618.0 |
| sex or gender | instance of | 4537175.0 |
| country | shares border with | 4469662.0 |
| instance of | instance of | 3607693.0 |
| given name | cast member | 3454064.0 |
| place of birth | instance of | 3266484.0 |
| country | head of government | 2855223.0 |
| place of birth | shares border with | 2779517.0 |
| place of birth | contains the administrative territorial entity | 2731338.0 |
| country of origin | contains the administrative territorial entity | 2619940.0 |
| country | instance of | 2401432.0 |
| country of citizenship | significant event | 2379485.0 |
| country of citizenship | basic form of government | 2227962.0 |
| place of death | instance of | 2197456.0 |
| occupation | subclass of | 2161319.0 |
| place of death | shares border with | 1960567.0 |
| place of death | contains the administrative territorial entity | 1956518.0 |
| country of citizenship | capital | 1865572.0 |
| place of birth | located in the administrative territorial entity | 1848343.0 |
| country of citizenship | currency | 1843623.0 |
| occupation | instance of | 1840727.0 |
| country of citizenship | named after | 1834131.0 |
| instance of | part of | 1825832.0 |
| country | head of state | 1804817.0 |
| country | public holiday | 1786217.0 |
| instance of | has part(s) | 1736134.0 |
| country of citizenship | country | 1635289.0 |
| place of birth | country | 1592470.0 |
| country of citizenship | official language | 1572134.0 |
| Unknown | Unknown | 1569880.0 |
| instance of | described by source | 1544568.0 |
| shares border with | shares border with | 1523858.0 |
| occupation | field of this occupation | 1517986.0 |
| instance of | from narrative universe | 1512788.0 |
| sex or gender | subclass of | 1512549.0 |
| sex or gender | opposite of | 1512543.0 |
| sex or gender | has quality | 1512303.0 |
| located in the administrative territorial entity | shares border with | 1511411.0 |
| instance of | parent taxon | 1510746.0 |
| instance of | taxon rank | 1510726.0 |
| instance of | IUCN conservation status | 1510559.0 |
| instance of | temporal range start | 1510476.0 |
| place of birth | cast member | 1508243.0 |
| country of citizenship | continent | 1488771.0 |
| country | located in time zone | 1449704.0 |
| place of death | cast member | 1411605.0 |
| place of birth | head of government | 1336109.0 |
| country of citizenship | legislative body | 1332780.0 |
| located in the administrative territorial entity | instance of | 1315206.0 |
| country of origin | head of government | 1295136.0 |
| country of citizenship | anthem | 1259140.0 |
| country of citizenship | category for films shot at this location | 1233355.0 |
| country of citizenship | category for people who died here | 1212172.0 |
| country of citizenship | topic's main Wikimedia portal | 1194512.0 |
| country of citizenship | highest point | 1163936.0 |
| country of citizenship | category of associated people | 1159180.0 |
| country of citizenship | cast member | 1155091.0 |
| cast member | occupation | 1122209.0 |
| given name | sex or gender | 1093022.0 |
| country of citizenship | part of | 1081929.0 |
| place of death | head of government | 1079092.0 |
| place of death | located in the administrative territorial entity | 1035264.0 |
| country of origin | diplomatic relation | 1030281.0 |
| given name | occupation | 1024401.0 |
| country of origin | member of | 1008284.0 |
| country of citizenship | follows | 983236.0 |
| place of death | country | 932869.0 |
| located in the administrative territorial entity | located in the administrative territorial entity | 929307.0 |
| country of citizenship | has part(s) | 922686.0 |
| member of political party | instance of | 910989.0 |
| country of citizenship | flag | 883127.0 |
| located in the administrative territorial entity | twinned administrative body | 852303.0 |
| located in the administrative territorial entity | country | 844371.0 |
| given name | given name | 825204.0 |
| country | currency | 759818.0 |
| member of political party | political ideology | 746941.0 |
| cast member | instance of | 717412.0 |
| given name | country of citizenship | 704267.0 |
| country of citizenship | located in the administrative territorial entity | 702098.0 |
| cast member | sex or gender | 699041.0 |
| country of origin | shares border with | 698140.0 |
| member of political party | chairperson | 685513.0 |
| country of citizenship | highest judicial authority | 664496.0 |
| cast member | country of citizenship | 659000.0 |
| given name | language of work or name | 647382.0 |
| given name | genre | 637148.0 |
| country of citizenship | office held by head of government | 634386.0 |
| member of political party | country | 633650.0 |
| cast member | given name | 602899.0 |
| given name | name day | 597017.0 |
| country | official language | 571866.0 |
| located on street | instance of | 562221.0 |
| given name | Unknown | 554299.0 |
| located on street | located in the administrative territorial entity | 552585.0 |
| located on street | country | 551394.0 |
| work location | twinned administrative body | 547239.0 |
| member of political party | headquarters location | 539811.0 |
| country | continent | 538245.0 |
| place of birth | category for people who died here | 529260.0 |
| cast member | place of birth | 525771.0 |
| country | capital | 516387.0 |
| country | country | 511466.0 |
| given name | director | 503056.0 |
| given name | country | 498476.0 |
| participant in | participant | 493721.0 |
| given name | original language of film or TV show | 491981.0 |
| located in the administrative territorial entity | contains settlement | 491103.0 |
| given name | located in the administrative territorial entity | 488485.0 |
| given name | country of origin | 475371.0 |
| country | highest point | 475254.0 |
| given name | place of birth | 473562.0 |
| located on street | location | 467145.0 |
| country | basic form of government | 454138.0 |
| country of citizenship | patron saint | 450341.0 |
| country of origin | located in time zone | 448994.0 |
| given name | present in work | 437905.0 |
| place of birth | located in time zone | 435150.0 |
| country | legislative body | 423573.0 |
| occupation | cast member | 418266.0 |
| country | significant event | 417636.0 |
| work location | contains the administrative territorial entity | 417171.0 |
| country of origin | instance of | 415518.0 |
| country | category for films shot at this location | 409430.0 |
| country | anthem | 405352.0 |
| located in the administrative territorial entity | cast member | 402017.0 |
| country | topic's main Wikimedia portal | 389180.0 |
| country of origin | public holiday | 381720.0 |
| given name | record label | 379598.0 |
| member of sports team | instance of | 377259.0 |
| educated at | instance of | 365695.0 |
| given name | screenwriter | 362421.0 |
| given name | family name identical to this given name | 355099.0 |
| given name | performer | 354581.0 |
| country | category for people who died here | 354361.0 |
| country | category of associated people | 353135.0 |
| award received | instance of | 350676.0 |
| country | named after | 349793.0 |
| twinned administrative body | twinned administrative body | 347684.0 |
| shares border with | located in the administrative territorial entity | 342467.0 |
| given name | depicts | 331867.0 |
| given name | child | 331572.0 |
| follows | instance of | 331313.0 |
| followed by | instance of | 330107.0 |
| genre | instance of | 328092.0 |
| country | located in the administrative territorial entity | 321797.0 |
| country of citizenship | record label | 321791.0 |
| country of citizenship | said to be the same as | 318581.0 |
| place of birth | record label | 314783.0 |
| work location | instance of | 311041.0 |
| given name | producer | 310662.0 |
| country of citizenship | replaces | 304717.0 |
| place of birth | genre | 301440.0 |
| given name | follows | 299579.0 |
| country of citizenship | screenwriter | 297052.0 |
| shares border with | instance of | 296474.0 |
| place of death | category for people who died here | 293734.0 |
| country | part of | 286829.0 |
| country of citizenship | followed by | 283269.0 |
| instance of | country | 281533.0 |
| given name | followed by | 276878.0 |
| family name | instance of | 275533.0 |
| country | flag | 271456.0 |
| member of sports team | league | 270491.0 |
| position held | subclass of | 267742.0 |
| place of death | record label | 266045.0 |
| occupation | patron saint | 262548.0 |
| place of birth | category for films shot at this location | 260317.0 |
| member of sports team | home venue | 259303.0 |
| shares border with | country | 258727.0 |
| shares border with | contains the administrative territorial entity | 258053.0 |
| country of citizenship | genre | 257486.0 |
| country of citizenship | original language of film or TV show | 255730.0 |
| given name | director of photography | 255568.0 |
| educated at | country | 254972.0 |
| country of citizenship | main regulatory text | 253493.0 |
| place of death | located in time zone | 251922.0 |
| country of citizenship | performer | 250563.0 |
| contains the administrative territorial entity | located in the administrative territorial entity | 249211.0 |
| place of birth | performer | 248781.0 |
| followed by | follows | 245728.0 |
| follows | followed by | 244820.0 |
| languages spoken, written or signed | instance of | 244269.0 |
| place of death | genre | 241656.0 |
| position held | instance of | 240644.0 |
| given name | place of death | 237761.0 |
| country | office held by head of government | 236495.0 |
| located in the administrative territorial entity | head of government | 235304.0 |
| cast member | place of death | 233593.0 |
| member of sports team | sport | 230297.0 |
| work location | head of government | 230188.0 |
| given name | made from material | 229833.0 |
| work location | shares border with | 229200.0 |
| genre | subclass of | 227781.0 |
| director | occupation | 224784.0 |
| country of origin | head of state | 224236.0 |
| genre | depicts | 223488.0 |
| part of | has part(s) | 221221.0 |
| narrative location | twinned administrative body | 220898.0 |
| place of birth | original language of film or TV show | 217981.0 |
| located in the administrative territorial entity | list of monuments | 216590.0 |
| member of sports team | head coach | 213106.0 |
| follows | follows | 210384.0 |
| narrative location | contains the administrative territorial entity | 210330.0 |
| given name | father | 210051.0 |
| followed by | followed by | 209837.0 |
| place of birth | category of associated people | 209205.0 |
| participant in | location | 208378.0 |
| country of citizenship | executive body | 206189.0 |
| given name | given name version for other gender | 203545.0 |
| location | instance of | 202914.0 |
| place of birth | follows | 202648.0 |
| given name | production company | 199469.0 |
| educated at | located in the administrative territorial entity | 199211.0 |
| given name | spouse | 196013.0 |
| place of death | performer | 194563.0 |
| contains the administrative territorial entity | instance of | 194340.0 |
| occupation | part of | 193366.0 |
| country of citizenship | director | 189298.0 |
| country of citizenship | coat of arms | 189106.0 |
| educated at | rector | 185959.0 |
| original language of film or TV show | instance of | 185253.0 |
| performer | instance of | 184273.0 |
| located in the administrative territorial entity | capital | 183619.0 |
| given name | twinned administrative body | 182918.0 |
| given name | shares border with | 182896.0 |
| place of death | original language of film or TV show | 182592.0 |
| member of political party | follows | 180528.0 |
| contains the administrative territorial entity | country | 179386.0 |
| contains the administrative territorial entity | shares border with | 176810.0 |
| filming location | contains the administrative territorial entity | 176335.0 |
| position held | country | 176261.0 |
| performer | occupation | 175258.0 |
| father | child | 173695.0 |
| member of sports team | country | 172927.0 |
| place of birth | director | 171917.0 |
| narrative location | instance of | 169827.0 |
| participant in | has part(s) | 169809.0 |
| place of birth | followed by | 169556.0 |
| participant in | oath made by | 169101.0 |
| family name | cast member | 168204.0 |
| country | follows | 165127.0 |
| place of death | category for films shot at this location | 163722.0 |
| instance of | depicts | 163433.0 |
| filming location | twinned administrative body | 162328.0 |
| given name | position held | 158114.0 |
| cast member | award received | 157516.0 |
| contains the administrative territorial entity | contains the administrative territorial entity | 157226.0 |
| child | Unknown | 154808.0 |
| country of citizenship | driving side | 154683.0 |
| narrative location | cast member | 153356.0 |
| given name | narrative location | 152807.0 |
| follows | cast member | 152207.0 |
| place of death | follows | 151684.0 |
| located in the administrative territorial entity | category for people who died here | 151602.0 |
| participant in | torch lit by | 151285.0 |
| country of citizenship | founded by | 149409.0 |
| followed by | cast member | 148605.0 |
| country | has part(s) | 148511.0 |
| country | cast member | 147300.0 |
| place of birth | country of origin | 146733.0 |
| position held | part of | 145994.0 |
| main subject | depicts | 145919.0 |
| place of death | director | 145036.0 |
| screenwriter | occupation | 144268.0 |
| place of death | category of associated people | 143377.0 |
| followed by | performer | 141431.0 |
| part of the series | cast member | 141327.0 |
| located in the administrative territorial entity | located in time zone | 140571.0 |
| cast member | educated at | 140558.0 |
| place of birth | located in or next to body of water | 140141.0 |
| follows | performer | 139933.0 |
| taxon rank | part of | 138583.0 |
| place of death | country of origin | 138241.0 |
| work location | located in the administrative territorial entity | 136442.0 |
| country | coat of arms | 135964.0 |
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| cause of death | subclass of | 26236.0 |
| has part(s) | part of | 26205.0 |
| creator | place of death | 26153.0 |
| mother | Unknown | 26086.0 |
| Unknown | place of death | 26043.0 |
| category combines topics | instance of | 26038.0 |
| given name | category for people who died here | 26022.0 |
| subclass of | instance of | 26020.0 |
| location of creation | contains the administrative territorial entity | 26005.0 |
| country of origin | located in the administrative territorial entity | 25935.0 |
| director of photography | place of birth | 25915.0 |
| given name | website account on | 25914.0 |
| given name | game mode | 25878.0 |
| has part(s) | depicts | 25791.0 |
| collection | located in the administrative territorial entity | 25759.0 |
| named after | contains the administrative territorial entity | 25722.0 |
| military branch | instance of | 25592.0 |
| cast member | Unknown | 25547.0 |
| country of citizenship | main subject | 25439.0 |
| named after | occupation | 25315.0 |
| country of citizenship | twinned administrative body | 25302.0 |
| performer | award received | 25133.0 |
| place of death | enclave within | 25002.0 |
| capital | country | 24912.0 |
| follows | screenwriter | 24786.0 |
| follows | part of | 24767.0 |
| follows | part of the series | 24754.0 |
| residence | contains the administrative territorial entity | 24682.0 |
| followed by | part of | 24499.0 |
| given name | from narrative universe | 24476.0 |
| place of death | executive body | 24436.0 |
| depicts | said to be the same as | 24423.0 |
| spouse | country of citizenship | 24416.0 |
| place of birth | composer | 24352.0 |
| place of birth | creator | 24330.0 |
| screenwriter | place of death | 24292.0 |
| notable work | creator | 24271.0 |
| child | spouse | 24226.0 |
| performer | follows | 24174.0 |
| creator | described by source | 24129.0 |
| instance of | said to be the same as | 24071.0 |
| country | located in or next to body of water | 24067.0 |
| place of birth | part of the series | 24050.0 |
| member of sports team | part of | 23976.0 |
| place of death | capital | 23965.0 |
| place of death | party chief representative | 23895.0 |
| language of work or name | instance of | 23814.0 |
| place of birth | taxon rank | 23674.0 |
| place of death | contains settlement | 23554.0 |
| award received | followed by | 23535.0 |
| collection | has part(s) | 23531.0 |
| place of birth | parent taxon | 23524.0 |
| notable work | location | 23444.0 |
| located in the administrative territorial entity | part of | 23419.0 |
| site of astronomical discovery | located in the administrative territorial entity | 23411.0 |
| website account on | instance of | 23366.0 |
| Unknown | family name | 23328.0 |
| notable work | cast member | 23103.0 |
| place of death | said to be the same as | 23037.0 |
| place of death | composer | 22996.0 |
| place of death | part of the series | 22962.0 |
| shares border with | category for people who died here | 22903.0 |
| part of the series | original language of film or TV show | 22898.0 |
| occupation | director of photography | 22867.0 |
| occupation | filming location | 22856.0 |
| occupation | main subject | 22849.0 |
| located on street | shares border with | 22830.0 |
| located in the administrative territorial entity | author | 22829.0 |
| languages spoken, written or signed | record label | 22823.0 |
| located in the administrative territorial entity | director of photography | 22808.0 |
| narrative location | follows | 22797.0 |
| work location | category of associated people | 22727.0 |
| cast member | conflict | 22614.0 |
| place of birth | original broadcaster | 22548.0 |
| narrative location | located in time zone | 22525.0 |
| platform | follows | 22425.0 |
| followed by | part of the series | 22359.0 |
| award received | conferred by | 22354.0 |
| work location | follows | 22198.0 |
| director | educated at | 22099.0 |
| child | place of death | 22063.0 |
| military branch | part of | 22001.0 |
| named after | cast member | 21838.0 |
| location | significant event | 21658.0 |
| work location | record label | 21554.0 |
| mouth of the watercourse | contains the administrative territorial entity | 21528.0 |
| followed by | screenwriter | 21441.0 |
| collection | significant event | 21359.0 |
| country of citizenship | country of citizenship | 21331.0 |
| made from material | made from material | 21302.0 |
| continent | instance of | 21268.0 |
| mother | instance of | 21247.0 |
| given name | composer | 21234.0 |
| country | author | 21169.0 |
| headquarters location | shares border with | 21158.0 |
| father | place of birth | 21157.0 |
| place of death | taxon rank | 21125.0 |
| place of death | parent taxon | 21101.0 |
| work location | category for films shot at this location | 21088.0 |
| discoverer or inventor | place of birth | 20997.0 |
| screenwriter | award received | 20972.0 |
| educated at | has part(s) | 20960.0 |
| located in the administrative territorial entity | narrative location | 20756.0 |
| owned by | contains the administrative territorial entity | 20731.0 |
| follows | country of origin | 20648.0 |
| platform | subclass of | 20638.0 |
| followed by | country of origin | 20600.0 |
| place of death | described by source | 20591.0 |
| residence | instance of | 20476.0 |
| given name | lifestyle | 20429.0 |
| platform | manufacturer | 20365.0 |
| notable work | collection | 20330.0 |
| narrative location | original language of film or TV show | 20328.0 |
| depicts | collection | 20317.0 |
| country of citizenship | adjacent station | 20309.0 |
| country of origin | record label | 20273.0 |
| member of | has part(s) | 20267.0 |
| filming location | record label | 20181.0 |
| country of origin | part of | 20122.0 |
| instance of | opposite of | 20108.0 |
| depicts | creator | 20053.0 |
| twinned administrative body | category for people who died here | 20042.0 |
| given name | discoverer or inventor | 20018.0 |
| work location | performer | 19930.0 |
| place of birth | anthem | 19922.0 |
| given name | languages spoken, written or signed | 19872.0 |
| filming location | diplomatic relation | 19755.0 |
| military branch | allegiance | 19668.0 |
| record label | founded by | 19656.0 |
| given name | religious order | 19562.0 |
| lyrics by | occupation | 19476.0 |
| author | educated at | 19471.0 |
| notable work | country | 19467.0 |
| academic degree | subclass of | 19450.0 |
| record label | owned by | 19325.0 |
| platform | followed by | 19312.0 |
| father | place of death | 19308.0 |
| given name | office held by head of government | 19276.0 |
| languages spoken, written or signed | Unknown | 19262.0 |
| participant in | organizer | 19228.0 |
| said to be the same as | cast member | 19174.0 |
| place of death | has part(s) | 18998.0 |
| contains settlement | instance of | 18987.0 |
| religion or worldview | subclass of | 18976.0 |
| mother | sex or gender | 18957.0 |
| place of birth | executive body | 18944.0 |
| place of birth | collection | 18942.0 |
| place of birth | Unknown | 18910.0 |
| place of death | official language | 18894.0 |
| place of death | connecting line | 18752.0 |
| based on | creator | 18679.0 |
| part of the series | platform | 18571.0 |
| place of death | Unknown | 18569.0 |
| performer | family name | 18543.0 |
| performer | instrument | 18543.0 |
| author | award received | 18460.0 |
| described by source | instance of | 18443.0 |
| category combines topics | located in the administrative territorial entity | 18443.0 |
| located in the administrative territorial entity | award received | 18431.0 |
| adjacent station | located in the administrative territorial entity | 18423.0 |
| participant in | significant event | 18421.0 |
| given name | is a list of | 18337.0 |
| twinned administrative body | cast member | 18325.0 |
| capital | shares border with | 18196.0 |
| platform | programmed in | 18154.0 |
| part of the series | country of origin | 18147.0 |
| spouse | place of birth | 18128.0 |
| place of death | language of work or name | 18014.0 |
val res = sqlContext.sql("""SELECT t2.r1.rel as rel1, t2.r2.rel as rel2, COUNT(*) as freq
FROM twoCycles1 t2
GROUP BY t2.r1.rel, t2.r2.rel
ORDER BY COUNT(*) LIMIT 1000""")
var result = res.toDF()
//result.show()
Federated Learning for Brain Tumor Segmentation
Project members:
- Jingru Fu - KTH Royal Institute of Technology
- Lidia Kidane - Umeå University
- Romuald Esdras Wandji - Umeå University
Content (→ Presenter)
- Introduction → Jingru
1.1 Federated Learning in medical field
1.2 Brain tumor segmentation
1.3 Hierarchy of presentation and code - System Architecture → Lidia
2.1 Distributed machine learning
2.2 Federated learning
2.3 System design
2.4 Frameworks for federated learning
2.5 Scalability issue and how we dealt with it - Methodology → Romuald
3.1 Federated Learning
3.2 U-Net Architectures
3.3 Distributed Training - Experiments and Results → Refer to notebooks 01 and 02
Introduction
1. Federated Learning in medical field
-
Reference: The future of digital health with federated learning
-
Federated learning (FL) is a learning paradigm seeking to address the problem of data governance and privacy by training algorithms collaboratively without exchanging the data itself.

a FL aggregation server—the typical FL workflow in which a federation of training nodes receive the global model, resubmit their partially trained models to a central server intermittently for aggregation and then continue training on the consensus model that the server returns. b FL peer to peer—alternative formulation of FL in which each training node exchanges its partially trained models with some or all of its peers and each does its own aggregation. c Centralised training—the general non-FL training workflow in which data acquiring sites donate their data to a central Data Lake from which they and others are able to extract data for local, independent training.
2. Brain tumor segmentation
- Data Description and Visualization
All BraTS multimodal scans are available as NIfTI files (.nii.gz) and describe a) native (T1) and b) post-contrast T1-weighted (T1Gd), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, and were acquired with different clinical protocols and various scanners from multiple (n=19) institutions. The data is collected from this link. All the imaging datasets have been segmented manually, by one to four raters, following the same annotation protocol, and their annotations were approved by experienced neuro-radiologists. Annotations comprise the GD-enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2), and the necrotic and non-enhancing tumor core (NCR/NET — label 1), as described both in the BraTS 2012-2013 TMI paper and in the latest BraTS summarizing paper. The provided data are distributed after their pre-processing, i.e., co-registered to the same anatomical template, interpolated to the same resolution (1 mm^3) and skull-stripped.
- Two modalities (T1Gd and T2-FLAIR) as inputs of the model
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import random
import os
import cv2
import glob
# PIL adds image processing capabilities to your Python interpreter.
import PIL
from PIL import Image, ImageOps
# Shutil module offers high-level operation on a file like a copy, create, and remote operation on the file.
import shutil
# skimage is a collection of algorithms for image processing and computer vision.
from skimage import data
from skimage.util import montage
import skimage.transform as skTrans
from skimage.transform import rotate
from skimage.transform import resize
# NEURAL IMAGING
import nilearn as nl
import nibabel as nib # access a multitude of neuroimaging data formats
# ML Libraries
import keras
import keras.backend as K
from keras.callbacks import CSVLogger
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.utils import plot_model
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping, TensorBoard
from tensorflow.keras.layers.experimental import preprocessing
# make numpy printouts easier to read
np.set_printoptions(precision = 3, suppress = True)
import warnings
warnings.filterwarnings('ignore')
# dataset path
train_data = "/dbfs/FileStore/tables/BraTS2020/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/"
valid_data = "/dbfs/FileStore/tables/BraTS2020/BraTS2020_ValidationData/MICCAI_BraTS2020_ValidationData/"
test_image_flair = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_flair.nii').get_fdata()
test_image_t1 = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_t1.nii').get_fdata()
test_image_t1ce = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_t1ce.nii').get_fdata()
test_image_t2 = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_t2.nii').get_fdata()
test_mask = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_seg.nii').get_fdata()
fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 5, figsize = (20, 10))
slice_w = 25
# FLAIR
ax1.imshow(test_image_flair[:,:,test_image_flair.shape[0]//2-slice_w], cmap = 'gray')
ax1.set_title('Image flair')
# T1
ax2.imshow(test_image_t1[:,:,test_image_t1.shape[0]//2-slice_w], cmap = 'gray')
ax2.set_title('Image t1')
# T1CE
ax3.imshow(test_image_t1ce[:,:,test_image_t1ce.shape[0]//2-slice_w], cmap = 'gray')
ax3.set_title('Image t1ce')
# T2
ax4.imshow(test_image_t2[:,:,test_image_t2.shape[0]//2-slice_w], cmap = 'gray')
ax4.set_title('Image t2')
# MASK
ax5.imshow(test_mask[:,:,test_mask.shape[0]//2-slice_w])
ax5.set_title('Mask')
# skip 50:-50 slices since there is not much to see
fig, ax1 = plt.subplots(1, 1, figsize = (15, 15))
ax1.imshow(rotate(montage(test_image_t1[50:-50,:,:]), 90, resize = True), cmap = 'gray')
# skip 50:-50 slices since there is not much to see
fig, ax1 = plt.subplots(1, 1, figsize = (15, 15))
ax1.imshow(rotate(montage(test_mask[50:-50,:,:]), 90, resize = True), cmap = 'gray')
3. Hierarchy of presentation and code
- Presentation
- Federated part → Lidia
- Distributed part → Romuald
- Code: Three trainings have been done:
- Centralised training
- Federated training: Three clients are simulated with unbalanced amounts of data (200 vs 40 vs 9)
- Centralised Single Client Training for client 3
- Federated Training
# list of directories
train_val_directories = [f.path for f in os.scandir(train_data) if f.is_dir()]
# remove BraTS20_Training_355 since it has ill formatted name for seg.nii file
train_val_directories.remove(train_data + 'BraTS20_Training_355')
# function to convert list of paths into IDs
def pathListIntoIDs(dirList):
x = []
for i in range(0, len(dirList)):
x.append(dirList[i][dirList[i].rfind('/')+1:])
return x
ids = pathListIntoIDs(train_val_directories)
# split ids into train+test and validation
train_test_ids, val_ids = train_test_split(ids, test_size = 0.2, random_state = 42)
# split train+test into train and test
train_ids, test_ids = train_test_split(train_test_ids, test_size = 0.15, random_state = 42)
# function to display data distribution
def showDataLayout():
plt.bar(["Train","Valid","Test"],
[len(train_ids), len(val_ids), len(test_ids)], align='center',color=[ 'green','red', 'blue'])
plt.legend()
plt.ylabel('Number of images')
plt.title('Data distribution')
plt.show()
showDataLayout()
# define segmentation areas
SEGMENT_CLASSES = {
0 : 'NOT TUMOR',
1 : 'NECROTIC/CORE', # or NON-ENHANCING TUMOR CORE
2 : 'EDEMA',
3 : 'ENHANCING' # original 4 -> converted into 3 later
}
# there are 155 slices per volume
# to start at 5 and use 145 slices means we will skip the first 5 and last 5
VOLUME_SLICES = 100
VOLUME_START_AT = 22 # first slice of volume that we will include
IMG_SIZE = 128
# override keras sequence DataGenerator class
class DataGenerator(keras.utils.Sequence):
# generates data for Keras
def __init__(self, list_IDs, dim=(IMG_SIZE,IMG_SIZE), batch_size = 1, n_channels = 2, shuffle=True):
# Initialization
self.dim = dim
self.batch_size = batch_size
self.list_IDs = list_IDs
self.n_channels = n_channels
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
# denotes the number of batches per epoch
return int(np.floor(len(self.list_IDs) / self.batch_size))
def __getitem__(self, index):
# generate one batch of data
# Generate indexes of the batch
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
# Find list of IDs
Batch_ids = [self.list_IDs[k] for k in indexes]
# Generate data
X, y = self.__data_generation(Batch_ids)
return X, y
def on_epoch_end(self):
# updates indexes after each epoch
self.indexes = np.arange(len(self.list_IDs))
if self.shuffle == True:
np.random.shuffle(self.indexes)
def __data_generation(self, Batch_ids):
'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
# initialization
X = np.zeros((self.batch_size*VOLUME_SLICES, *self.dim, self.n_channels))
y = np.zeros((self.batch_size*VOLUME_SLICES, 240, 240))
Y = np.zeros((self.batch_size*VOLUME_SLICES, *self.dim, 4))
# Generate data
for c, i in enumerate(Batch_ids):
case_path = os.path.join(train_data, i)
data_path = os.path.join(case_path, f'{i}_flair.nii');
flair = nib.load(data_path).get_fdata()
data_path = os.path.join(case_path, f'{i}_t1ce.nii');
ce = nib.load(data_path).get_fdata()
data_path = os.path.join(case_path, f'{i}_seg.nii');
seg = nib.load(data_path).get_fdata()
for j in range(VOLUME_SLICES):
X[j +VOLUME_SLICES*c,:,:,0] = cv2.resize(flair[:,:,j+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE));
X[j +VOLUME_SLICES*c,:,:,1] = cv2.resize(ce[:,:,j+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE));
y[j +VOLUME_SLICES*c] = seg[:,:,j+VOLUME_START_AT];
# Generate masks
y[y==4] = 3;
mask = tf.one_hot(y, 4);
Y = tf.image.resize(mask, (IMG_SIZE, IMG_SIZE));
#print("X size = {};\nY size = {}".format(X.shape, Y.shape))
return X/np.max(X), Y
System Architecture
Federated learning (FL)
machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized.
- FL brings the model to where the data lives, train it locally, and only upload the update to the server
- Local data storing and processing with global coordination is made possible by the emerging technology of mobile edge computing(MEC), where edge nodes, such as sensors, home gateways, micro servers, and small cells, are equipped with storage and computation capability.
Advantages
-
Highly efficient use of network bandwidth Less information is required to be transmitted to the cloud.
-
Privacy With guaranteed privacy, more users will be willing to take part in collaborative model training and so, better inference models are built.
-
Low latency The latency is much lower than that when decisions are made in the cloud before transmitting them to the end devices which is vital for time critical applications.
Comparison between Parameter Server and FL: Federated Learning protocol is very similar to the traditional parameter server protocol. The differences are: - In data center setting, shared storage is usually used, which means the worker machine do not keep persistent data storage on their own, and they fetch data from the shared storage at the beginning of each iteration. - In FL, the data, and thus the loss function, on the different clients may be very heterogeneous, and far from being representative of the joint data.(e.g. the data stored on each client may be highly non-IID) - In FL, the server never keeps track of any individual client information and only uses aggregates to ensure privacy. Because of the high churn in FL setting, only a small subset of the devices are selected by the server in each round.
### Scalability The implementation is scalable based on:
-Resource constrained system The federated learning simulation is is based on Tensorflow federated learning framework, can be deployed on set of clusters or a single machine based on the the need.
-Increasing number of clients As number of clients increase, subset of clients are selected to to be part of the model update process.
# function to take in data and return a dictionary with client names as keys and values as data shards
def create_client(data, num_clients, initial = 'client'):
# create a list of client names
client_names = ['{}_{}'.format(initial, i+1) for i in range(num_clients)]
# size of data shard
size = len(data)//num_clients
# create data shard for each client for i in [200, 40, 9]
shards = [data[0:200], data[200:240], data[240:249]]
print(len(data),len(shards), len(client_names))
print(len(shards[0]),len(shards[1]),len(shards[2]))
print(shards[0][0])
# number of clients must equal number of shards
assert(len(shards) == len(client_names))
return {client_names[i] : shards[i] for i in range(len(client_names))}
def weight_scaling_factor(data):
return len(data)/len(train_ids)
def scale_model_weights(weight, scalar):
'''function for scaling a models weights'''
weight_final = []
steps = len(weight)
for i in range(steps):
weight_final.append(scalar * weight[i])
return weight_final
def sum_scaled_weights(scaled_weight_list):
'''Return the sum of the listed scaled weights. The is equivalent to scaled avg of the weights'''
avg_grad = list()
#get the average grad accross all client gradients
for grad_list_tuple in zip(*scaled_weight_list):
layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)
avg_grad.append(layer_mean)
return avg_grad
# function to evaluate the model on test data and print the current round and metrics
def evaluate_model(data, model, round):
test_generator = DataGenerator(data)
results = model.evaluate(test_generator, batch_size = batch_size, verbose = 1)
loss, accuracy = results[0], results[1]*100
print(f'round: {round} | loss: {loss} | accuracy: {accuracy:.2f}%')
# create clients
clients = create_client(train_ids,3)
valid_generator = DataGenerator(val_ids)
Distributed Training
We opt for distributed training accross each institution to take advantage of the available processing units, and the ultimate goal is to reduce the training time while making faster iteration to reach modeling goals
Here we are interested in Data parallelism which is distributed training category used to improve the efficiency of training the model with massive datasets
The distributed learning process can be summarized as follow: 1. Each GPU performs a forward pass on a different slice of the input data to compute the loss 2. Each GPU compute the gradient based ont he loss functions 3. The gradients are aggregated accross each of the devices, via an All-Reduce algorigm 4. The optimizer updates the weights using the reduced gradient, thereby keeping the devices in sync
An All-Reduce algorithm here is refered to as an operation that reduce a set of arrays on distributed workers into a single array that is distributed back to each of these workers
In distributed training, an additional computation is performed at the end of each training step where all workers exchange with each other the gradients and calculate the average
This approach been implemented using tf.distribute.MirroredStrategy from Tensorflow which supports synchronous distributed training on multiple GPUs on one machine. It creates one replica per GPU device. Each variable in the model is mirrored across all the replicas. Together, these variables form a single conceptual variable called MirroredVariable. These variables are kept in sync with each other by applying identical updates.
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
strategy = tf.distribute.MirroredStrategy()
print("Number of devices: {}".format(strategy.num_replicas_in_sync))
# Losses
from keras_unet_collection import losses
# losses.dice, losses.dice_coef
# dice loss as defined above for 4 classes
def dice_coef_class(y_true, y_pred, smooth=1.0):
class_num = 4
for i in range(class_num):
y_true_f = K.flatten(y_true[:,:,:,i])
y_pred_f = K.flatten(y_pred[:,:,:,i])
intersection = K.sum(y_true_f * y_pred_f)
loss = ((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
# K.print_tensor(loss, message='loss value for class {} : '.format(SEGMENT_CLASSES[i]))
if i == 0:
total_loss = loss
else:
total_loss = total_loss + loss
total_loss = total_loss / class_num
# K.print_tensor(total_loss, message=' total dice coef: ')
return total_loss
# define per class evaluation of dice coef
# inspired by https://github.com/keras-team/keras/issues/9395
def dice_coef_necrotic(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,1] * y_pred[:,:,:,1]))
return (2. * intersection) / (K.sum(y_true[:,:,:,1]) + K.sum(y_pred[:,:,:,1]) + epsilon) # I dont like squre
def dice_coef_edema(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,2] * y_pred[:,:,:,2]))
return (2. * intersection) / (K.sum(y_true[:,:,:,2]) + K.sum(y_pred[:,:,:,2]) + epsilon)
def dice_coef_enhancing(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,3] * y_pred[:,:,:,3]))
return (2. * intersection) / (K.sum(y_true[:,:,:,3]) + K.sum(y_pred[:,:,:,3]) + epsilon)
# Computing Precision
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
# Computing Sensitivity
def sensitivity(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
return true_positives / (possible_positives + K.epsilon())
# Computing Specificity
def specificity(y_true, y_pred):
true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))
possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))
return true_negatives / (possible_negatives + K.epsilon())
U-Net Architectures
U-net is a special type of architecture for semantic image segmentation purposes [1], it consists of two main paths namely the encoder and the decoder * The encoder or contracting path: Similar to a regular CNN, it tries to understand the what of the image, it does it by using convolutions and max pooling * The decoder or expansion path: which is responsible to find the where part of the image by applying sequences of up-convolutions and concatenations with features from the corresponding contracting path
# U-NET
def build_unet(inputs, ker_init, dropout):
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(inputs)
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv1)
pool = MaxPooling2D(pool_size=(2, 2))(conv1)
conv = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool)
conv = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv3)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool4)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv5)
drop5 = Dropout(dropout)(conv5)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(drop5))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv9)
up = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv9))
merge = concatenate([conv1,up], axis = 3)
conv = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge)
conv = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv)
conv10 = Conv2D(4, (1,1), activation = 'softmax')(conv)
return Model(inputs = inputs, outputs = conv10)
#### Federated Learning
Unlinke the centralized learning, in **Federated Learning**, Institutions do not share their data but instead train a shared model locally and only send model updates to the central server. The server accumulates and aggregates the individual updates to yield a global model and then forwards the new shared parameters to each client for further training. Once the model updates have been applied, they are discarded by the central server as they are only required for enhancing the current global model.
The training process of the Federated Learning system we implemented can be summarized as follow:
1. The collaborator or institution receives the global model updates from the server and locally trains on their local data and sends the local model updates to the central server.
2. The central server receives the local model updates and performs secure aggregation without learning information about any collaborator to yield a global model.
3. The central server forwards the new shared parameters to the collaborators for further training.
4. Go back to 1 for another federated round.

input_layer = Input((IMG_SIZE, IMG_SIZE, 2))
save_path = "/dbfs/FileStore/tables/BraTS2020/"
# add callback for training process
csv_logger = CSVLogger(f'{save_path}training_fl.log', separator=',', append=False)
checkpoint_filepath = f'{save_path}checkpoint_fl'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
monitor='val_accuracy',
mode='max',
save_best_only=True)
callbacks = [
model_checkpoint_callback,
keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=2, min_lr=0.000001, verbose=1),
csv_logger
]
def evaluate_model(data, model):
test_generator = DataGenerator(data)
results = model.evaluate(test_generator, batch_size = 32, verbose = 1)
for i in range(len(results)):
print("Metric_{}={}".format(i, results[i]))
return results
ROUNDS = 3
SELECTED_EACH_ROUND = 1
BATCH_SIZE = 1
EPOCHS_CLIENT = 10
# initialize global model
K.clear_session()
global_model = build_unet(input_layer, 'he_normal', 0.2)
global_model.compile(
loss = "categorical_crossentropy",
optimizer = keras.optimizers.Adam(learning_rate = 0.001),
metrics = ['accuracy', tf.keras.metrics.MeanIoU(num_classes = 4), dice_coef_class, losses.dice, losses.dice_coef, precision, sensitivity, specificity, dice_coef_necrotic, dice_coef_edema, dice_coef_enhancing]
)
print("Begin Training")
# commence global training loop
for round in range(1, ROUNDS):
print(f'\nRound: {round}')
# get global model's weights
global_weights = global_model.get_weights()
# initial list to collect local model weights after scaling
scaled_local_weight_list = list()
# get client names
client_names= list(clients.keys())
random.shuffle(client_names)
count = 1
# loop through each client and create new local model
for client in client_names:
print(f'Client {count}')
with strategy.scope():
local_model = build_unet(input_layer, 'he_normal', 0.2)
local_model.compile(
loss = "categorical_crossentropy",
optimizer = keras.optimizers.Adam(learning_rate = 0.001),
metrics = ['accuracy', tf.keras.metrics.MeanIoU(num_classes = 4), dice_coef_class, losses.dice, losses.dice_coef, precision, sensitivity, specificity, dice_coef_necrotic, dice_coef_edema, dice_coef_enhancing])
#set local model weight to the weight of the global model
local_model.set_weights(global_weights)
# get client data and pass it through a data generator
data = DataGenerator(clients[client], batch_size = BATCH_SIZE * strategy.num_replicas_in_sync )
# fit local model with client's data
local_model.fit(data, epochs=EPOCHS_CLIENT, steps_per_epoch = len(data), verbose = 1) #callbacks = callbacks, validation_data = valid_generator)
# scale the model weights and add to list
scaling_factor = weight_scaling_factor(data)
print(f'scaling_factor = {scaling_factor}')
scaled_weights = scale_model_weights(local_model.get_weights(), scaling_factor)
# not adding scaling
scaled_local_weight_list.append(local_model.get_weights()) # Here should be scaled_local_weight_list.append(scaled_weights)??
# scaled_local_weight_list.append(scaled_weights)
# clear session to free memory after each communication round
K.clear_session()
count += 1
#to get the average over all the local model, we simply take the sum of the scaled weights
print('len of scaled_local_weight_list = {}'.format(len(scaled_local_weight_list)))
average_weights = sum_scaled_weights(scaled_local_weight_list)
#update global model
global_model.set_weights(average_weights)
print('\nTraining Done!')
# evaluation
save_path = "/dbfs/FileStore/tables/BraTS2020/"
# load training history
history = pd.read_csv(f'{save_path}training_fl.log', sep = ',', engine = 'python')
acc = history['accuracy']
epoch = range(len(acc))
loss = history['loss']
dice_class = history['dice_coef_class']
dice = history['dice_coef']
mean_iou = history['mean_io_u']
# visualize the training process
f, ax = plt.subplots(1, 5, figsize = (25, 8))
# ACCURACY
ax[0].plot(epoch, acc, 'b', label = 'Training Accuracy')
ax[0].legend()
# LOSS
ax[1].plot(epoch, loss, 'b', label = 'Training Loss')
ax[1].legend()
# CLASS DICE COEFFICIENT
ax[2].plot(epoch, dice_class, 'b', label = 'Training Class Dice Coefficient')
ax[2].legend()
# DICE COEFFICIENT
ax[3].plot(epoch, dice, 'b', label = 'Training Dice Coefficient')
ax[3].legend()
# Mean IoU
ax[4].plot(epoch, mean_iou, 'b', label = 'Training MeanIoU')
ax[4].legend()
plt.show()
Discussion
This experiment is a simulation experiment, which simulates federated learning on a machine and implements the Federated averaging algorithm.
In this project, we investigate the unbalanced scenario of federated learning in which different clients have access to different amounts of data. A total of three clients are set up with significantly different amounts of data, e.g., 400 vs 40 vs 9. First, we tested the same model (a basic U-Net) on one client with nine training data and received a 0.36 DICE score (dicecoefclass). Then we tested if this client would benefit from federated learning. As a result, the DICE score is around 0.63, which represents a 0.3 improvement over 0.36.
Areas for further improvement: - Model: Only a basic U-Net was investigated. In the report, it is also suggested that the performance of federated learning would benefit from more advanced U-Nets; - Modality: Different clients might have different modalities that we could simulate in the further; - Hyperparameters: There are four main hyperparameters in federated learning: the round of federated learning procedure (ROUNDS); the selected client in each round (SELECTEDEACHROUND); the batch size of local training for a single client (BATCHSIZE); the epoch number of local training for a single client (EPOCHSCLIENT). In this project, we only present the result using a single setting: ROUNDS = 5, SELECTEDEACHROUND = 1, BATCHSIZE = 1, and EPOCHSCLIENT = 10. We have also tested other settings using another machine, but the results don't provide more information, so we chose only to present this one. - Other options for implementing federated learning: We also believe that investigating more options for implementing federated learning will be beneficial. We found the following other options to be of interest: Databricks+PyGitHub; TensorFlowFederated(TFF); Flower(PyTorch based); MONAI+NVIDIA: link1, link2; Ray(Pytorch).
[1] Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
This is our baseline for Tumor Segmentation Task. All the code used for this project is modified and derived based on this repo.
-
Note: Please do not rerun the code since training is time-consuming (~4h);
-
Code is tested on tiny-debug-cluster-gpu.
Load Libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import os
import cv2
# glob (short for global) is used to return all file paths that match a specific pattern.
import glob
# PIL adds image processing capabilities to your Python interpreter.
import PIL
import PIL.Image
if not hasattr(PIL.Image, 'Resampling'): # Pillow<9.0
PIL.Image.Resampling = PIL.Image
from PIL import ImageOps
# Shutil module offers high-level operation on a file like a copy, create, and remote operation on the file.
import shutil
# skimage is a collection of algorithms for image processing and computer vision.
from skimage import data
from skimage.util import montage
import skimage.transform as skTrans
from skimage.transform import rotate
from skimage.transform import resize
# NEURAL IMAGING
import nilearn as nl
import nibabel as nib # access a multitude of neuroimaging data formats
import nilearn.plotting as nlplt
# import gif_your_nifti.core as gif2nif
# ML Libraries
# import keras
import tensorflow.keras.backend as K
from tensorflow.keras.callbacks import CSVLogger
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.utils import plot_model
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping, TensorBoard
from tensorflow.keras.layers.experimental import preprocessing
from ray.train.tensorflow import TensorflowTrainer
from ray import tune
# make numpy printouts easier to read
np.set_printoptions(precision = 3, suppress = True)
import warnings
warnings.filterwarnings('ignore')
All BraTS multimodal scans are available as NIfTI files (.nii.gz) and describe a) native (T1) and b) post-contrast T1-weighted (T1Gd), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, and were acquired with different clinical protocols and various scanners from multiple (n=19) institutions. The data is collected from this link. All the imaging datasets have been segmented manually, by one to four raters, following the same annotation protocol, and their annotations were approved by experienced neuro-radiologists. Annotations comprise the GD-enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2), and the necrotic and non-enhancing tumor core (NCR/NET — label 1), as described both in the BraTS 2012-2013 TMI paper and in the latest BraTS summarizing paper. The provided data are distributed after their pre-processing, i.e., co-registered to the same anatomical template, interpolated to the same resolution (1 mm^3) and skull-stripped.
# dataset path
train_data = "/dbfs/FileStore/tables/BraTS2020/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/"
valid_data = "/dbfs/FileStore/tables/BraTS2020/BraTS2020_ValidationData/MICCAI_BraTS2020_ValidationData/"
id_img = '369'
test_image_flair = nib.load(train_data + f'BraTS20_Training_{id_img}/BraTS20_Training_{id_img}_flair.nii').get_fdata()
test_image_t1 = nib.load(train_data + f'BraTS20_Training_{id_img}/BraTS20_Training_{id_img}_t1.nii').get_fdata()
test_image_t1ce = nib.load(train_data + f'BraTS20_Training_{id_img}/BraTS20_Training_{id_img}_t1ce.nii').get_fdata()
test_image_t2 = nib.load(train_data + f'BraTS20_Training_{id_img}/BraTS20_Training_{id_img}_t2.nii').get_fdata()
test_mask = nib.load(train_data + f'BraTS20_Training_{id_img}/BraTS20_Training_{id_img}_seg.nii').get_fdata()
fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 5, figsize = (20, 10))
slice_w = 25
# FLAIR
ax1.imshow(test_image_flair[:,:,test_image_flair.shape[0]//2-slice_w], cmap = 'gray')
ax1.set_title('Image flair')
# T1
ax2.imshow(test_image_t1[:,:,test_image_t1.shape[0]//2-slice_w], cmap = 'gray')
ax2.set_title('Image t1')
# T1CE
ax3.imshow(test_image_t1ce[:,:,test_image_t1ce.shape[0]//2-slice_w], cmap = 'gray')
ax3.set_title('Image t1ce')
# T2
ax4.imshow(test_image_t2[:,:,test_image_t2.shape[0]//2-slice_w], cmap = 'gray')
ax4.set_title('Image t2')
# MASK
ax5.imshow(test_mask[:,:,test_mask.shape[0]//2-slice_w])
ax5.set_title('Mask')
# skip 50:-50 slices since there is not much to see
fig, ax1 = plt.subplots(1, 1, figsize = (15, 15))
ax1.imshow(rotate(montage(test_image_t1[50:-50,:,:]), 90, resize = True), cmap = 'gray')
# skip 50:-50 slices since there is not much to see
fig, ax1 = plt.subplots(1, 1, figsize = (15, 15))
ax1.imshow(rotate(montage(test_mask[50:-50,:,:]), 90, resize = True), cmap = 'gray')
# list of directories
train_val_directories = [f.path for f in os.scandir(train_data) if f.is_dir()]
# remove BraTS20_Training_355 since it has ill formatted name for seg.nii file
train_val_directories.remove(train_data + 'BraTS20_Training_355')
# function to convert list of paths into IDs
def pathListIntoIDs(dirList):
x = []
for i in range(0, len(dirList)):
x.append(dirList[i][dirList[i].rfind('/')+1:])
return x
ids = pathListIntoIDs(train_val_directories)
# split ids into train+test and validation
train_test_ids, val_ids = train_test_split(ids, test_size = 0.2)
# split train+test into train and test
train_ids, test_ids = train_test_split(train_test_ids, test_size = 0.15)
# function to display data distribution
def showDataLayout():
plt.bar(["Train","Valid","Test"],
[len(train_ids), len(val_ids), len(test_ids)], align='center',color=[ 'green','red', 'blue'])
plt.legend()
plt.ylabel('Number of images')
plt.title('Data distribution')
plt.show()
showDataLayout()
IMG_SIZE = 128
# define segmentation areas
SEGMENT_CLASSES = {
0 : 'NOT TUMOR',
1 : 'NECROTIC/CORE', # or NON-ENHANCING TUMOR CORE
2 : 'EDEMA',
3 : 'ENHANCING' # original 4 -> converted into 3 later
}
# there are 155 slices per volume
# to start at 5 and use 145 slices means we will skip the first 5 and last 5
VOLUME_SLICES = 100
VOLUME_START_AT = 22 # first slice of volume that we will include
# override keras sequence DataGenerator class
class DataGenerator(keras.utils.Sequence):
# generates data for Keras
def __init__(self, list_IDs, dim=(IMG_SIZE,IMG_SIZE), batch_size = 1 , n_channels = 2, shuffle=True):
# Initialization
self.dim = dim
self.batch_size = batch_size
self.list_IDs = list_IDs
self.n_channels = n_channels
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
# denotes the number of batches per epoch
return int(np.floor(len(self.list_IDs) / self.batch_size))
def __getitem__(self, index):
# generate one batch of data
# Generate indexes of the batch
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
# Find list of IDs
Batch_ids = [self.list_IDs[k] for k in indexes]
# Generate data
X, y = self.__data_generation(Batch_ids)
return X, y
def on_epoch_end(self):
# updates indexes after each epoch
self.indexes = np.arange(len(self.list_IDs))
if self.shuffle == True:
np.random.shuffle(self.indexes)
def __data_generation(self, Batch_ids):
'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
# initialization
X = np.zeros((self.batch_size*VOLUME_SLICES, *self.dim, self.n_channels))
y = np.zeros((self.batch_size*VOLUME_SLICES, 240, 240))
Y = np.zeros((self.batch_size*VOLUME_SLICES, *self.dim, 4))
# Generate data
for c, i in enumerate(Batch_ids):
case_path = os.path.join(train_data, i)
data_path = os.path.join(case_path, f'{i}_flair.nii');
flair = nib.load(data_path).get_fdata()
data_path = os.path.join(case_path, f'{i}_t1ce.nii');
ce = nib.load(data_path).get_fdata()
data_path = os.path.join(case_path, f'{i}_seg.nii');
seg = nib.load(data_path).get_fdata()
for j in range(VOLUME_SLICES):
X[j +VOLUME_SLICES*c,:,:,0] = cv2.resize(flair[:,:,j+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE));
X[j +VOLUME_SLICES*c,:,:,1] = cv2.resize(ce[:,:,j+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE));
y[j +VOLUME_SLICES*c] = seg[:,:,j+VOLUME_START_AT];
# Generate masks
y[y==4] = 3;
mask = tf.one_hot(y, 4);
Y = tf.image.resize(mask, (IMG_SIZE, IMG_SIZE));
return X/np.max(X), Y
training_generator = DataGenerator(train_ids)
valid_generator = DataGenerator(val_ids)
test_generator = DataGenerator(test_ids)
tf.distribute.Strategy is a TensorFlow API to distribute training across multiple GPUs, multiple machines, or TPUs. Using this API, we can distribute our existing models and training code with minimal code changes.
tf.distribute.Strategy has been designed with these key goals in mind:
- Easy to use and support multiple user segments, including researchers, machine learning engineers, etc.
- Provide good performance out of the box.
- Easy switching between strategies.
- You can distribute training using tf.distribute.Strategy with a high-level API like Keras Model.fit, as well as custom training loops (and, in general, any computation using TensorFlow).
In TensorFlow 2.x, we can execute our programs eagerly, or in a graph using tf.function. tf.distribute.Strategy intends to support both these modes of execution, but works best with tf.function. Eager mode is only recommended for debugging purposes and not supported for tf.distribute.TPUStrategy. Although training is the focus of this guide, this API can also be used for distributing evaluation and prediction on different platforms.
tf.distribute.Strategy can be used with very few changes to the code, because the underlying components of TensorFlow have been changed to become strategy-aware. This includes variables, layers, models, optimizers, metrics, summaries, and checkpoints.
tf.distribute.MirroredStrategy supports synchronous distributed training on multiple GPUs on one machine. It creates one replica per GPU device. Each variable in the model is mirrored across all the replicas. Together, these variables form a single conceptual variable called MirroredVariable. These variables are kept in sync with each other by applying identical updates.
Efficient all-reduce algorithms are used to communicate the variable updates across the devices. All-reduce aggregates tensors across all the devices by adding them up, and makes them available on each device. It’s a fused algorithm that is very efficient and can reduce the overhead of synchronization significantly. There are many all-reduce algorithms and implementations available, depending on the type of communication available between devices. By default, it uses the NVIDIA Collective Communication Library (NCCL) as the all-reduce implementation.
Types of strategies
tf.distribute.Strategy intends to cover a number of use cases along different axes. Some of these axes are:
- Synchronous vs asynchronous training: These are two common ways of distributing training with data parallelism. In sync training, all workers train over different slices of input data in sync, and aggregating gradients at each step. In async training, all workers are independently training over the input data and updating variables asynchronously. Typically sync training is supported via all-reduce and async through parameter server architecture.
- Hardware platform: which is good for scaling our training onto multiple GPUs on one machine, or multiple machines in a network (with 0 or more GPUs each), or on Cloud TPUs.
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
strategy = tf.distribute.MirroredStrategy()
print("Number of devices: {}".format(strategy.num_replicas_in_sync))
# Losses
from keras_unet_collection import losses
# losses.dice, losses.dice_coef
# dice loss as defined above for 4 classes
def dice_coef_class(y_true, y_pred, smooth=1.0):
class_num = 4
for i in range(class_num):
y_true_f = K.flatten(y_true[:,:,:,i])
y_pred_f = K.flatten(y_pred[:,:,:,i])
intersection = K.sum(y_true_f * y_pred_f)
loss = ((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
# K.print_tensor(loss, message='loss value for class {} : '.format(SEGMENT_CLASSES[i]))
if i == 0:
total_loss = loss
else:
total_loss = total_loss + loss
total_loss = total_loss / class_num
# K.print_tensor(total_loss, message=' total dice coef: ')
return total_loss
# define per class evaluation of dice coef
# inspired by https://github.com/keras-team/keras/issues/9395
def dice_coef_necrotic(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,1] * y_pred[:,:,:,1]))
return (2. * intersection) / (K.sum(y_true[:,:,:,1]) + K.sum(y_pred[:,:,:,1]) + epsilon) # I dont like squre
def dice_coef_edema(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,2] * y_pred[:,:,:,2]))
return (2. * intersection) / (K.sum(y_true[:,:,:,2]) + K.sum(y_pred[:,:,:,2]) + epsilon)
def dice_coef_enhancing(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,3] * y_pred[:,:,:,3]))
return (2. * intersection) / (K.sum(y_true[:,:,:,3]) + K.sum(y_pred[:,:,:,3]) + epsilon)
# Computing Precision
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
# Computing Sensitivity
def sensitivity(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
return true_positives / (possible_positives + K.epsilon())
# Computing Specificity
def specificity(y_true, y_pred):
true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))
possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))
return true_negatives / (possible_negatives + K.epsilon())
# U-NET
def build_unet(inputs, ker_init, dropout):
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(inputs)
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv1)
pool = MaxPooling2D(pool_size=(2, 2))(conv1)
conv = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool)
conv = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv3)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool4)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv5)
drop5 = Dropout(dropout)(conv5)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(drop5))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv9)
up = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv9))
merge = concatenate([conv1,up], axis = 3)
conv = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge)
conv = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv)
conv10 = Conv2D(4, (1,1), activation = 'softmax')(conv)
return Model(inputs = inputs, outputs = conv10)
input_layer = Input((IMG_SIZE, IMG_SIZE, 2))
save_path = "/dbfs/FileStore/tables/BraTS2020/"
# add callback for training process
csv_logger = CSVLogger(f'{save_path}training_baseline2.log', separator=',', append=False)
checkpoint_filepath = f'{save_path}checkpoint'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
monitor='val_accuracy',
mode='max',
save_best_only=True)
callbacks = [
model_checkpoint_callback,
keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=2, min_lr=0.000001, verbose=1),
csv_logger
]
# TRAIN MODEL
BATCH_SIZE = 1
training_generator = DataGenerator(train_ids, batch_size = BATCH_SIZE * strategy.num_replicas_in_sync )
K.clear_session()
with strategy.scope():
model = build_unet(input_layer, 'he_normal', 0.2)
model.compile(
loss = "categorical_crossentropy",
optimizer = keras.optimizers.Adam(learning_rate = 0.001),
metrics = ['accuracy', tf.keras.metrics.MeanIoU(num_classes = 4), dice_coef_class, losses.dice, losses.dice_coef, precision, sensitivity, specificity, dice_coef_necrotic, dice_coef_edema, dice_coef_enhancing])
history = model.fit(training_generator,
epochs=30,
steps_per_epoch=len(train_ids),
callbacks= callbacks,
validation_data = valid_generator
)
# save the model
model.save(f"{save_path}model_baseline.h5")
# load trained model
model = build_unet(input_layer, 'he_normal', 0.2)
model.load_weights(checkpoint_filepath)
# load training history
history = pd.read_csv(f"{save_path}training_baseline2.log", sep = ',', engine = 'python')
acc = history['accuracy']
val_acc = history['val_accuracy']
epoch = range(len(acc))
loss = history['loss']
val_loss = history['val_loss']
dice_class = history['dice_coef_class']
val_dice_class = history['val_dice_coef_class']
dice = history['dice_coef']
val_dice = history['val_dice_coef']
mean_iou = history['mean_io_u']
val_mean_iou = history['val_mean_io_u']
# visualize the training process
f, ax = plt.subplots(1, 5, figsize = (25, 8))
# ACCURACY
ax[0].plot(epoch, acc, 'b', label = 'Training Accuracy')
ax[0].plot(epoch, val_acc, 'r', label = 'Validation Accuracy')
ax[0].legend()
# LOSS
ax[1].plot(epoch, loss, 'b', label = 'Training Loss')
ax[1].plot(epoch, val_loss, 'r', label = 'Validation Loss')
ax[1].legend()
# CLASS DICE COEFFICIENT
ax[2].plot(epoch, dice_class, 'b', label = 'Training Class Dice Coefficient')
ax[2].plot(epoch, val_dice_class, 'r', label = 'Validation Class Dice Coefficient')
ax[2].legend()
# DICE COEFFICIENT
ax[3].plot(epoch, dice, 'b', label = 'Training Dice Coefficient')
ax[3].plot(epoch, val_dice, 'r', label = 'Validation Dice Coefficient')
ax[3].legend()
# Mean IoU
ax[4].plot(epoch, mean_iou, 'b', label = 'Training MeanIoU')
ax[4].plot(epoch, val_mean_iou, 'r', label = 'Validation MeanIoU')
ax[4].legend()
plt.show()
def predictByPath(case_path,case):
files = next(os.walk(case_path))[2]
X = np.empty((VOLUME_SLICES, IMG_SIZE, IMG_SIZE, 2))
# y = np.empty((VOLUME_SLICES, IMG_SIZE, IMG_SIZE))
vol_path = os.path.join(case_path, f'BraTS20_Training_{case}_flair.nii');
flair=nib.load(vol_path).get_fdata()
vol_path = os.path.join(case_path, f'BraTS20_Training_{case}_t1ce.nii');
ce=nib.load(vol_path).get_fdata()
# vol_path = os.path.join(case_path, f'BraTS20_Training_{case}_seg.nii');
# seg=nib.load(vol_path).get_fdata()
for j in range(VOLUME_SLICES):
X[j,:,:,0] = cv2.resize(flair[:,:,j+VOLUME_START_AT], (IMG_SIZE,IMG_SIZE))
X[j,:,:,1] = cv2.resize(ce[:,:,j+VOLUME_START_AT], (IMG_SIZE,IMG_SIZE))
# y[j,:,:] = cv2.resize(seg[:,:,j+VOLUME_START_AT], (IMG_SIZE,IMG_SIZE))
# model.evaluate(x=X,y=y[:,:,:,0], callbacks= callbacks)
return model.predict(X/np.max(X), verbose=1)
def showPredictsById(case, start_slice = 60):
path = f"/dbfs/FileStore/tables/BraTS2020/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/BraTS20_Training_{case}"
gt = nib.load(os.path.join(path, f'BraTS20_Training_{case}_seg.nii')).get_fdata()
origImage = nib.load(os.path.join(path, f'BraTS20_Training_{case}_flair.nii')).get_fdata()
p = predictByPath(path, case)
core = p[:,:,:,1]
edema= p[:,:,:,2]
enhancing = p[:,:,:,3]
plt.figure(figsize=(18, 50))
f, axarr = plt.subplots(1,6, figsize = (18, 50))
for i in range(6): # for each image, add brain background
axarr[i].imshow(cv2.resize(origImage[:,:,start_slice+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE)), cmap="gray", interpolation='none')
axarr[0].imshow(cv2.resize(origImage[:,:,start_slice+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE)), cmap="gray")
axarr[0].title.set_text('Original image flair')
curr_gt=cv2.resize(gt[:,:,start_slice+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE), interpolation = cv2.INTER_NEAREST)
axarr[1].imshow(curr_gt, cmap="Reds", interpolation='none', alpha=0.3) # ,alpha=0.3,cmap='Reds'
axarr[1].title.set_text('Ground truth')
axarr[2].imshow(p[start_slice,:,:,1:4], cmap="Reds", interpolation='none', alpha=0.3)
axarr[2].title.set_text('all classes')
axarr[3].imshow(edema[start_slice,:,:], cmap="OrRd", interpolation='none', alpha=0.3)
axarr[3].title.set_text(f'{SEGMENT_CLASSES[1]} predicted')
axarr[4].imshow(core[start_slice,:,], cmap="OrRd", interpolation='none', alpha=0.3)
axarr[4].title.set_text(f'{SEGMENT_CLASSES[2]} predicted')
axarr[5].imshow(enhancing[start_slice,:,], cmap="OrRd", interpolation='none', alpha=0.3)
axarr[5].title.set_text(f'{SEGMENT_CLASSES[3]} predicted')
plt.show()
showPredictsById(case = test_ids[3][-3:])
case = test_ids[4][-3:]
path = f"/dbfs/FileStore/tables/BraTS2020/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/BraTS20_Training_{case}"
# ground truth
gt = nib.load(os.path.join(path, f'BraTS20_Training_{case}_seg.nii')).get_fdata()
p = predictByPath(path, case)
core = p[:, :, :, 1]
edema = p[:, :, :, 2]
enhancing = p[:, :, :, 3]
# slice at
i = 40
eval_class = 2
# use only one class for per class evaluation
gt[gt != eval_class] = 1
resized_gt = cv2.resize(gt[:,:,i+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE))
plt.figure()
f, axarr = plt.subplots(1,2)
axarr[0].imshow(resized_gt, cmap="gray")
axarr[0].title.set_text('ground truth')
axarr[1].imshow(p[i,:,:,eval_class], cmap="gray")
axarr[1].title.set_text(f'predicted class: {SEGMENT_CLASSES[eval_class]}')
plt.show()
-
Note: Please do not rerun the code since training is time-consuming;
-
Code is tested on tiny-debug-cluster-gpu.
Load Libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import random
import os
import cv2
import glob
# PIL adds image processing capabilities to your Python interpreter.
import PIL
from PIL import Image, ImageOps
# Shutil module offers high-level operation on a file like a copy, create, and remote operation on the file.
import shutil
# skimage is a collection of algorithms for image processing and computer vision.
from skimage import data
from skimage.util import montage
import skimage.transform as skTrans
from skimage.transform import rotate
from skimage.transform import resize
# NEURAL IMAGING
import nilearn as nl
import nibabel as nib # access a multitude of neuroimaging data formats
# ML Libraries
import keras
import keras.backend as K
from keras.callbacks import CSVLogger
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.utils import plot_model
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping, TensorBoard
from tensorflow.keras.layers.experimental import preprocessing
# make numpy printouts easier to read
np.set_printoptions(precision = 3, suppress = True)
import warnings
warnings.filterwarnings('ignore')
# dataset path
train_data = "/dbfs/FileStore/tables/BraTS2020/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/"
valid_data = "/dbfs/FileStore/tables/BraTS2020/BraTS2020_ValidationData/MICCAI_BraTS2020_ValidationData/"
Data Visualization
%md All BraTS multimodal scans are available as NIfTI files (.nii.gz) and describe a) native (T1) and b) post-contrast T1-weighted (T1Gd), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, and were acquired with different clinical protocols and various scanners from multiple (n=19) institutions. The data is collected from this link. All the imaging datasets have been segmented manually, by one to four raters, following the same annotation protocol, and their annotations were approved by experienced neuro-radiologists. Annotations comprise the GD-enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2), and the necrotic and non-enhancing tumor core (NCR/NET — label 1), as described both in the BraTS 2012-2013 TMI paper and in the latest BraTS summarizing paper. The provided data are distributed after their pre-processing, i.e., co-registered to the same anatomical template, interpolated to the same resolution (1 mm^3) and skull-stripped.
test_image_flair = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_flair.nii').get_fdata()
test_image_t1 = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_t1.nii').get_fdata()
test_image_t1ce = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_t1ce.nii').get_fdata()
test_image_t2 = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_t2.nii').get_fdata()
test_mask = nib.load(train_data + 'BraTS20_Training_001/BraTS20_Training_001_seg.nii').get_fdata()
fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 5, figsize = (20, 10))
slice_w = 25
# FLAIR
ax1.imshow(test_image_flair[:,:,test_image_flair.shape[0]//2-slice_w], cmap = 'gray')
ax1.set_title('Image flair')
# T1
ax2.imshow(test_image_t1[:,:,test_image_t1.shape[0]//2-slice_w], cmap = 'gray')
ax2.set_title('Image t1')
# T1CE
ax3.imshow(test_image_t1ce[:,:,test_image_t1ce.shape[0]//2-slice_w], cmap = 'gray')
ax3.set_title('Image t1ce')
# T2
ax4.imshow(test_image_t2[:,:,test_image_t2.shape[0]//2-slice_w], cmap = 'gray')
ax4.set_title('Image t2')
# MASK
ax5.imshow(test_mask[:,:,test_mask.shape[0]//2-slice_w])
ax5.set_title('Mask')
# skip 50:-50 slices since there is not much to see
fig, ax1 = plt.subplots(1, 1, figsize = (15, 15))
ax1.imshow(rotate(montage(test_image_t1[50:-50,:,:]), 90, resize = True), cmap = 'gray')
# skip 50:-50 slices since there is not much to see
fig, ax1 = plt.subplots(1, 1, figsize = (15, 15))
ax1.imshow(rotate(montage(test_mask[50:-50,:,:]), 90, resize = True), cmap = 'gray')
# list of directories
train_val_directories = [f.path for f in os.scandir(train_data) if f.is_dir()]
# remove BraTS20_Training_355 since it has ill formatted name for seg.nii file
train_val_directories.remove(train_data + 'BraTS20_Training_355')
# function to convert list of paths into IDs
def pathListIntoIDs(dirList):
x = []
for i in range(0, len(dirList)):
x.append(dirList[i][dirList[i].rfind('/')+1:])
return x
ids = pathListIntoIDs(train_val_directories)
# split ids into train+test and validation
train_test_ids, val_ids = train_test_split(ids, test_size = 0.2, random_state = 42)
# split train+test into train and test
train_ids, test_ids = train_test_split(train_test_ids, test_size = 0.15, random_state = 42)
# function to display data distribution
def showDataLayout():
plt.bar(["Train","Valid","Test"],
[len(train_ids), len(val_ids), len(test_ids)], align='center',color=[ 'green','red', 'blue'])
plt.legend()
plt.ylabel('Number of images')
plt.title('Data distribution')
plt.show()
showDataLayout()
# define segmentation areas
SEGMENT_CLASSES = {
0 : 'NOT TUMOR',
1 : 'NECROTIC/CORE', # or NON-ENHANCING TUMOR CORE
2 : 'EDEMA',
3 : 'ENHANCING' # original 4 -> converted into 3 later
}
# there are 155 slices per volume
# to start at 5 and use 145 slices means we will skip the first 5 and last 5
VOLUME_SLICES = 100
VOLUME_START_AT = 22 # first slice of volume that we will include
IMG_SIZE = 128
# override keras sequence DataGenerator class
class DataGenerator(keras.utils.Sequence):
# generates data for Keras
def __init__(self, list_IDs, dim=(IMG_SIZE,IMG_SIZE), batch_size = 1, n_channels = 2, shuffle=True):
# Initialization
self.dim = dim
self.batch_size = batch_size
self.list_IDs = list_IDs
self.n_channels = n_channels
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
# denotes the number of batches per epoch
return int(np.floor(len(self.list_IDs) / self.batch_size))
def __getitem__(self, index):
# generate one batch of data
# Generate indexes of the batch
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
# Find list of IDs
Batch_ids = [self.list_IDs[k] for k in indexes]
# Generate data
X, y = self.__data_generation(Batch_ids)
return X, y
def on_epoch_end(self):
# updates indexes after each epoch
self.indexes = np.arange(len(self.list_IDs))
if self.shuffle == True:
np.random.shuffle(self.indexes)
def __data_generation(self, Batch_ids):
'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
# initialization
X = np.zeros((self.batch_size*VOLUME_SLICES, *self.dim, self.n_channels))
y = np.zeros((self.batch_size*VOLUME_SLICES, 240, 240))
Y = np.zeros((self.batch_size*VOLUME_SLICES, *self.dim, 4))
# Generate data
for c, i in enumerate(Batch_ids):
case_path = os.path.join(train_data, i)
data_path = os.path.join(case_path, f'{i}_flair.nii');
flair = nib.load(data_path).get_fdata()
data_path = os.path.join(case_path, f'{i}_t1ce.nii');
ce = nib.load(data_path).get_fdata()
data_path = os.path.join(case_path, f'{i}_seg.nii');
seg = nib.load(data_path).get_fdata()
for j in range(VOLUME_SLICES):
X[j +VOLUME_SLICES*c,:,:,0] = cv2.resize(flair[:,:,j+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE));
X[j +VOLUME_SLICES*c,:,:,1] = cv2.resize(ce[:,:,j+VOLUME_START_AT], (IMG_SIZE, IMG_SIZE));
y[j +VOLUME_SLICES*c] = seg[:,:,j+VOLUME_START_AT];
# Generate masks
y[y==4] = 3;
mask = tf.one_hot(y, 4);
Y = tf.image.resize(mask, (IMG_SIZE, IMG_SIZE));
#print("X size = {};\nY size = {}".format(X.shape, Y.shape))
return X/np.max(X), Y
# function to take in data and return a dictionary with client names as keys and values as data shards
def create_client(data, num_clients, initial = 'client'):
# create a list of client names
client_names = ['{}_{}'.format(initial, i+1) for i in range(num_clients)]
# size of data shard
# size = len(data)//num_clients
# create data shard for each client for i in [200, 40, 9]
shards = [data[0:200], data[200:240], data[240:249]]
print(len(data),len(shards), len(client_names))
print(len(shards[0]),len(shards[1]),len(shards[2]))
print(shards[0][0])
# number of clients must equal number of shards
assert(len(shards) == len(client_names))
return {client_names[i] : shards[i] for i in range(len(client_names))}
def weight_scaling_factor(data):
return len(data)/len(train_ids)
def scale_model_weights(weight, scalar):
'''function for scaling a models weights'''
weight_final = []
steps = len(weight)
for i in range(steps):
weight_final.append(scalar * weight[i])
return weight_final
def sum_scaled_weights(scaled_weight_list):
'''Return the sum of the listed scaled weights. The is equivalent to scaled avg of the weights'''
avg_grad = list()
#get the average grad accross all client gradients
for grad_list_tuple in zip(*scaled_weight_list):
layer_mean = tf.math.reduce_sum(grad_list_tuple, axis=0)
avg_grad.append(layer_mean)
return avg_grad
# function to evaluate the model on test data and print the current round and metrics
def evaluate_model(data, model, round):
test_generator = DataGenerator(data)
results = model.evaluate(test_generator, batch_size = batch_size, verbose = 1)
loss, accuracy = results[0], results[1]*100
print(f'round: {round} | loss: {loss} | accuracy: {accuracy:.2f}%')
# create clients
clients = create_client(train_ids,3)
valid_generator = DataGenerator(val_ids)
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
strategy = tf.distribute.MirroredStrategy()
print("Number of devices: {}".format(strategy.num_replicas_in_sync))
\[\mathscr(A)\]
Dice Score
# Losses
from keras_unet_collection import losses
# losses.dice, losses.dice_coef
# dice loss as defined above for 4 classes
def dice_coef_class(y_true, y_pred, smooth=1.0):
class_num = 4
for i in range(class_num):
y_true_f = K.flatten(y_true[:,:,:,i])
y_pred_f = K.flatten(y_pred[:,:,:,i])
intersection = K.sum(y_true_f * y_pred_f)
loss = ((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
# K.print_tensor(loss, message='loss value for class {} : '.format(SEGMENT_CLASSES[i]))
if i == 0:
total_loss = loss
else:
total_loss = total_loss + loss
total_loss = total_loss / class_num
# K.print_tensor(total_loss, message=' total dice coef: ')
return total_loss
# define per class evaluation of dice coef
# inspired by https://github.com/keras-team/keras/issues/9395
def dice_coef_necrotic(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,1] * y_pred[:,:,:,1]))
return (2. * intersection) / (K.sum(y_true[:,:,:,1]) + K.sum(y_pred[:,:,:,1]) + epsilon) # I dont like squre
def dice_coef_edema(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,2] * y_pred[:,:,:,2]))
return (2. * intersection) / (K.sum(y_true[:,:,:,2]) + K.sum(y_pred[:,:,:,2]) + epsilon)
def dice_coef_enhancing(y_true, y_pred, epsilon=1e-6):
intersection = K.sum(K.abs(y_true[:,:,:,3] * y_pred[:,:,:,3]))
return (2. * intersection) / (K.sum(y_true[:,:,:,3]) + K.sum(y_pred[:,:,:,3]) + epsilon)
# Computing Precision
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
# Computing Sensitivity
def sensitivity(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
return true_positives / (possible_positives + K.epsilon())
# Computing Specificity
def specificity(y_true, y_pred):
true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))
possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))
return true_negatives / (possible_negatives + K.epsilon())
# U-NET
def build_unet(inputs, ker_init, dropout):
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(inputs)
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv1)
pool = MaxPooling2D(pool_size=(2, 2))(conv1)
conv = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool)
conv = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv3)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool4)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv5)
drop5 = Dropout(dropout)(conv5)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(drop5))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv9)
up = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = (2,2))(conv9))
merge = concatenate([conv1,up], axis = 3)
conv = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge)
conv = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv)
conv10 = Conv2D(4, (1,1), activation = 'softmax')(conv)
return Model(inputs = inputs, outputs = conv10)
input_layer = Input((IMG_SIZE, IMG_SIZE, 2))
save_path = "/dbfs/FileStore/tables/BraTS2020/"
# add callback for training process
csv_logger = CSVLogger(f'{save_path}training_single_client.log', separator=',', append=False)
checkpoint_filepath = f'{save_path}checkpoint_signle_client'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
monitor='val_accuracy',
mode='max',
save_best_only=True)
callbacks = [
model_checkpoint_callback,
keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=2, min_lr=0.000001, verbose=1),
csv_logger
]
# TRAIN MODEL
BATCH_SIZE = 1
K.clear_session()
with strategy.scope():
model = build_unet(input_layer, 'he_normal', 0.2)
model.compile(
loss = "categorical_crossentropy",
optimizer = keras.optimizers.Adam(learning_rate = 0.001),
metrics = ['accuracy', tf.keras.metrics.MeanIoU(num_classes = 4), dice_coef_class, losses.dice, losses.dice_coef, precision, sensitivity, specificity, dice_coef_necrotic, dice_coef_edema, dice_coef_enhancing]
)
training_generator = DataGenerator(clients[list(clients.keys())[2]], batch_size = BATCH_SIZE * strategy.num_replicas_in_sync)
history = model.fit(training_generator,
epochs=30,
steps_per_epoch=len(training_generator),
callbacks= callbacks,
# validation_data = valid_generator
)
# evaluation
save_path = "/dbfs/FileStore/tables/BraTS2020/"
# load training history
history = pd.read_csv(f"{save_path}training_single_client.log", sep = ',', engine = 'python')
acc = history['accuracy']
epoch = range(len(acc))
loss = history['loss']
dice_class = history['dice_coef_class']
dice = history['dice_coef']
mean_iou = history['mean_io_u']
# visualize the training process
f, ax = plt.subplots(1, 5, figsize = (25, 8))
# ACCURACY
ax[0].plot(epoch, acc, 'b', label = 'Training Accuracy')
ax[0].legend()
# LOSS
ax[1].plot(epoch, loss, 'b', label = 'Training Loss')
ax[1].legend()
# CLASS DICE COEFFICIENT
ax[2].plot(epoch, dice_class, 'b', label = 'Training Class Dice Coefficient')
ax[2].legend()
# DICE COEFFICIENT
ax[3].plot(epoch, dice, 'b', label = 'Training Dice Coefficient')
ax[3].legend()
# Mean IoU
ax[4].plot(epoch, mean_iou, 'b', label = 'Training MeanIoU')
ax[4].legend()
plt.show()
input_layer = Input((IMG_SIZE, IMG_SIZE, 2))
save_path = "/dbfs/FileStore/tables/BraTS2020/"
# add callback for training process
csv_logger = CSVLogger(f'{save_path}training_single_client_1.log', separator=',', append=False)
checkpoint_filepath = f'{save_path}checkpoint_signle_client_1'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
monitor='val_accuracy',
mode='max',
save_best_only=True)
callbacks = [
model_checkpoint_callback,
keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=2, min_lr=0.000001, verbose=1),
csv_logger
]
# TRAIN MODEL
BATCH_SIZE = 1
K.clear_session()
with strategy.scope():
model = build_unet(input_layer, 'he_normal', 0.2)
model.compile(
loss = "categorical_crossentropy",
optimizer = keras.optimizers.Adam(learning_rate = 0.001),
metrics = ['accuracy', tf.keras.metrics.MeanIoU(num_classes = 4), dice_coef_class, losses.dice, losses.dice_coef, precision, sensitivity, specificity, dice_coef_necrotic, dice_coef_edema, dice_coef_enhancing]
)
training_generator = DataGenerator(clients[list(clients.keys())[0]], batch_size = BATCH_SIZE * strategy.num_replicas_in_sync)
history = model.fit(training_generator,
epochs=30,
steps_per_epoch=len(training_generator),
callbacks= callbacks,
# validation_data = valid_generator
)
# evaluation
save_path = "/dbfs/FileStore/tables/BraTS2020/"
# load training history
history = pd.read_csv(f"{save_path}training_single_client_1.log", sep = ',', engine = 'python')
acc = history['accuracy']
epoch = range(len(acc))
loss = history['loss']
dice_class = history['dice_coef_class']
dice = history['dice_coef']
mean_iou = history['mean_io_u']
# visualize the training process
f, ax = plt.subplots(1, 5, figsize = (25, 8))
# ACCURACY
ax[0].plot(epoch, acc, 'b', label = 'Training Accuracy')
ax[0].legend()
# LOSS
ax[1].plot(epoch, loss, 'b', label = 'Training Loss')
ax[1].legend()
# CLASS DICE COEFFICIENT
ax[2].plot(epoch, dice_class, 'b', label = 'Training Class Dice Coefficient')
ax[2].legend()
# DICE COEFFICIENT
ax[3].plot(epoch, dice, 'b', label = 'Training Dice Coefficient')
ax[3].legend()
# Mean IoU
ax[4].plot(epoch, mean_iou, 'b', label = 'Training MeanIoU')
ax[4].legend()
plt.show()
input_layer = Input((IMG_SIZE, IMG_SIZE, 2))
save_path = "/dbfs/FileStore/tables/BraTS2020/"
# add callback for training process
csv_logger = CSVLogger(f'{save_path}training_fl.log', separator=',', append=False)
checkpoint_filepath = f'{save_path}checkpoint_fl'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
monitor='val_accuracy',
mode='max',
save_best_only=True)
callbacks = [
model_checkpoint_callback,
keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=2, min_lr=0.000001, verbose=1),
csv_logger
]
def evaluate_model(data, model):
test_generator = DataGenerator(data)
results = model.evaluate(test_generator, batch_size = 32, verbose = 1)
for i in range(len(results)):
print("Metric_{}={}".format(i, results[i]))
return results
ROUNDS = 5
SELECTED_EACH_ROUND = 1
BATCH_SIZE = 1
EPOCHS_CLIENT = 10
# initialize global model
K.clear_session()
with strategy.scope():
global_model = build_unet(input_layer, 'he_normal', 0.2)
global_model.compile(
loss = "categorical_crossentropy",
optimizer = keras.optimizers.Adam(learning_rate = 0.001),
metrics = ['accuracy', tf.keras.metrics.MeanIoU(num_classes = 4), dice_coef_class, losses.dice, losses.dice_coef, precision, sensitivity, specificity, dice_coef_necrotic, dice_coef_edema, dice_coef_enhancing])
print("Begin Training")
# commence global training loop
for round in range(1, ROUNDS+1):
print(f'\nRound: {round}')
# get global model's weights
global_weights = global_model.get_weights()
# initial list to collect local model weights after scaling
scaled_local_weight_list = list()
# get client names
client_names= list(clients.keys())
random.shuffle(client_names)
count = 1
results = []
# results.append(evaluate_model(val_ids, global_model))
results.append(evaluate_model(clients[list(clients.keys())[2]], global_model))
# loop through each client and create new local model
for client in client_names[0:SELECTED_EACH_ROUND]:
print(f'Client {count}')
with strategy.scope():
local_model = build_unet(input_layer, 'he_normal', 0.2)
local_model.compile(
loss = "categorical_crossentropy",
optimizer = keras.optimizers.Adam(learning_rate = 0.001),
metrics = ['accuracy', tf.keras.metrics.MeanIoU(num_classes = 4), dice_coef_class, losses.dice, losses.dice_coef, precision, sensitivity, specificity, dice_coef_necrotic, dice_coef_edema, dice_coef_enhancing])
#set local model weight to the weight of the global model
local_model.set_weights(global_weights)
# get client data and pass it through a data generator
data = DataGenerator(clients[client], batch_size = BATCH_SIZE * trategy.num_replicas_in_sync )
# fit local model with client's data
local_model.fit(data, epochs=EPOCHS_CLIENT, steps_per_epoch = len(data), verbose = 1) #callbacks = callbacks, validation_data = valid_generator)
# scale the model weights and add to list
scaling_factor = weight_scaling_factor(data)
print(f'scaling_factor = {scaling_factor}')
scaled_weights = scale_model_weights(local_model.get_weights(), scaling_factor)
# not adding scaling
scaled_local_weight_list.append(local_model.get_weights()) # Here should be scaled_local_weight_list.append(scaled_weights)??
# scaled_local_weight_list.append(scaled_weights)
# clear session to free memory after each communication round
K.clear_session()
count += 1
#to get the average over all the local model, we simply take the sum of the scaled weights
print('len of scaled_local_weight_list = {}'.format(len(scaled_local_weight_list)))
average_weights = sum_scaled_weights(scaled_local_weight_list)
#update global model
global_model.set_weights(average_weights)
print('\nTraining Done!')
Discussion
This experiment is a simulation experiment, which simulates federated learning on a machine and implements the Federated averaging algorithm.
In this project, we investigate the unbalanced scenario of federated learning in which different clients have access to different amounts of data. A total of three clients are set up with significantly different amounts of data, e.g., 400 vs 40 vs 9. First, we tested the same model (a basic U-Net) on one client with nine training data and received a 0.36 DICE score (dicecoefclass). Then we tested if this client would benefit from federated learning. As a result, the DICE score is around 0.63, which represents a 0.3 improvement over 0.36.
Areas for further improvement: - Model: Only a basic U-Net was investigated. In the report, it is also suggested that the performance of federated learning would benefit from more advanced U-Nets; - Modality: Different clients might have different modalities that we could simulate in the further. - Hyperparameters: There are four main hyperparameters in federated learning: the round of federated learning procedure (ROUNDS); the selected client in each round (SELECTEDEACHROUND); the batch size of local training for a single client (BATCHSIZE); the epoch number of local training for a single client (EPOCHSCLIENT). In this project, we only present the result using a single setting: ROUNDS = 5, SELECTEDEACHROUND = 1, BATCHSIZE = 1, and EPOCHSCLIENT = 10. We have also tested other settings using another machine, but the results don't provide more information, so we chose only to present this one. - Other options for implementing federated learning: We also believe that investigating more options for implementing federated learning will be beneficial. We found the following other options to be of interest: Databricks+PyGitHub; TensorFlowFederated(TFF); Flower(PyTorch based); MONAI+NVIDIA: link1, link2; Ray(Pytorch).
ls /FileStore/tables
| path | name | size |
|---|---|---|
| dbfs:/FileStore/tables/000a_finance_utils.scala | 000a_finance_utils.scala | 2187.0 |
| dbfs:/FileStore/tables/BraTS2020/ | BraTS2020/ | 0.0 |
| dbfs:/FileStore/tables/GDELT_raw_data.scala | GDELT_raw_data.scala | 3820.0 |
| dbfs:/FileStore/tables/LT_accV.parquet | LT_accV.parquet | 365326.0 |
| dbfs:/FileStore/tables/LT_time_intervals | LT_time_intervals | 43839.0 |
| dbfs:/FileStore/tables/LTaccidents_id_date.parquet | LTaccidents_id_date.parquet | 89266.0 |
| dbfs:/FileStore/tables/LTcar_locations-2.csv | LTcar_locations-2.csv | 706938.0 |
| dbfs:/FileStore/tables/LTnodes.csv | LTnodes.csv | 587461.0 |
| dbfs:/FileStore/tables/UUnodes.csv | UUnodes.csv | 29335.0 |
| dbfs:/FileStore/tables/UUways.csv | UUways.csv | 906.0 |
| dbfs:/FileStore/tables/WheresCroc_1_2_2.zip | WheresCroc_1_2_2.zip | 22591.0 |
| dbfs:/FileStore/tables/albin/ | albin/ | 0.0 |
| dbfs:/FileStore/tables/anlociStoUlaOreb.csv | anlociStoUlaOreb.csv | 373172.0 |
| dbfs:/FileStore/tables/bcousd_20220512T084217Z_001.zip | bcousd_20220512T084217Z_001.zip | 2.8871314e7 |
| dbfs:/FileStore/tables/bitstampUSD_1_min_data_2012_01_01_to_2021_03_31_csv-1.zip | bitstampUSD_1_min_data_2012_01_01_to_2021_03_31_csv-1.zip | 1.05242372e8 |
| dbfs:/FileStore/tables/bitstampUSD_1_min_data_2012_01_01_to_2021_03_31_csv.zip | bitstampUSD_1_min_data_2012_01_01_to_2021_03_31_csv.zip | 1.05242372e8 |
| dbfs:/FileStore/tables/emptyPagesTable-1.csv | emptyPagesTable-1.csv | 275.0 |
| dbfs:/FileStore/tables/emptyPagesTable.csv | emptyPagesTable.csv | 275.0 |
| dbfs:/FileStore/tables/errors.txt | errors.txt | 802.0 |
| dbfs:/FileStore/tables/events_test.csv | events_test.csv | 447.0 |
| dbfs:/FileStore/tables/events_test_albin.csv/ | events_test_albin.csv/ | 0.0 |
| dbfs:/FileStore/tables/hd_audio_tar.gz | hd_audio_tar.gz | 3.37342212e8 |
| dbfs:/FileStore/tables/lithuania_coordinates_transf_osm_pbf_node.parquet | lithuania_coordinates_transf_osm_pbf_node.parquet | 7.36902938e8 |
| dbfs:/FileStore/tables/ltcar_reprojected.csv | ltcar_reprojected.csv | 752891.0 |
| dbfs:/FileStore/tables/mpn1000rnd20210902_2.csv | mpn1000rnd20210902_2.csv | 2.7386942e7 |
| dbfs:/FileStore/tables/mpn2.bz2 | mpn2.bz2 | 2.0507172e7 |
| dbfs:/FileStore/tables/over300all.txt | over300all.txt | 31334.0 |
| dbfs:/FileStore/tables/over300all_2.txt | over300all_2.txt | 31334.0 |
| dbfs:/FileStore/tables/social_media_usage.csv | social_media_usage.csv | 402147.0 |
| dbfs:/FileStore/tables/svwiki-redirects/ | svwiki-redirects/ | 0.0 |
| dbfs:/FileStore/tables/voronoi20191213uppsla1st.txt | voronoi20191213uppsla1st.txt | 2613.0 |
| dbfs:/FileStore/tables/voronoi20191213uppsla2d.txt | voronoi20191213uppsla2d.txt | 4924.0 |
| dbfs:/FileStore/tables/voronoi20191213uppsla3d.txt | voronoi20191213uppsla3d.txt | 7022.0 |
| dbfs:/FileStore/tables/voronoiUlaStoOreb_bbox_queen1.txt | voronoiUlaStoOreb_bbox_queen1.txt | 497069.0 |
| dbfs:/FileStore/tables/voronoiUlaStoOreb_bbox_queen2.txt | voronoiUlaStoOreb_bbox_queen2.txt | 1046400.0 |
| dbfs:/FileStore/tables/voronoiUlaStoOreb_bbox_queen3.txt | voronoiUlaStoOreb_bbox_queen3.txt | 1730809.0 |
| dbfs:/FileStore/tables/voronoi_input.csv | voronoi_input.csv | 6857.0 |
ls /FileStore/tables/BraTS2020/
| path | name | size |
|---|---|---|
| dbfs:/FileStore/tables/BraTS2020/BraTS2020_TrainingData/ | BraTS2020_TrainingData/ | 0.0 |
| dbfs:/FileStore/tables/BraTS2020/BraTS2020_ValidationData/ | BraTS2020_ValidationData/ | 0.0 |
The data has been uploaded to the dbfs on /FileStore/tables/BraTS2020/ !!
Scalable Bayesian optimization with distributed Gaussian processes and deep kernel learning
Project members:
- Carl Hvarfner, Lund University
- Leonard Papenmeier, Lund University
- Manu Upadhyaya, Lund University
Background
In many practical applications, one is challenged with optimizing functions that are expensive to evaluate and whose form is unknown.

One way of dealing with such functions is to learn a surrogate model of it, e.g., by using splines. However, many methods do not give an estimate of uncertainty, i.e., for a given point, it is not clear how confident one is in the surrogate model.
Gaussian processes (GPs) are surrogate models that provide a measure of uncertainty and allow for a exploration-exploitation-based optimization method: Bayesian optimization (BO).

BO chooses new points to evaluate by selecting points that have a high uncertainty and/or a high expected function value.
What did we do
We implement a scalable algorithm for high-dimensional Bayesian optimization. Our implementation maintains arbitrary many GPs in parallel. We further train a deep kernel on the aggregated data to improve optimization even further.
Why should anyone care?
You should care because, as shown above, optimization problems such as the ones we assume occur frequently. For example, in ML, BO is commonly used for hyperparameter optimization. When the optimization problem becomes too high-dimensional, the original BO algorithm struggles due to the curse of dimensionality. In such cases, more advanced methods need to be used that require more computational power. If you have a cluster at hand, you need a scalable implementation that distributes the work across multiple nodes. This is what we provide.
Why is this scalable?
Vanilla BO has a data bottleneck: the inference time grows cubically with the number of observations. To circumvent this problem, we use TurBO, a method that maintains several GPs in parallel, each working on their own data.
The original TurBO implementation did not actually provide code that can be run in parallel. Our implementation can be distributed on arbitrary many workers where each worker maintains one or more GPs.
- Each GP on a worker is initialized with a standard Matérn kernel.
- After TurBO finished its run on a worker, the data is aggregated and the DKL is learned.
- The DKL is then used as the kernel for the GPs on, again, several workers.
What are our contributions?
As far as we know, we provide the first Spark implementation of TuRBO. We changed the algorithm where suitable to improve scalability. For example, unlike the original TurBO implementation, we avoid any communication between the workers during the optimization run. We furthermore use TurBO with a deep kernel which is an extension of the original TurBO algorithm. We show an evaluation of our algorithm on two commonly used benchmarks.
Notebook organization
Notebooks 1-4 provide the background of our work:
- Introduction to BO
- Introduction to GPs
- Acquisition function for BO
- Scalable BO with TurBO
Notebook 5
Bayesian optimization
The goal of Bayesian optimization (BO) is to solve
\[ \mathbf{x}^* \in \arg \max_{\mathbf{x}\in \mathcal{X}} f(\mathbf{x}), \]
where : - \(f: \mathcal{X} \rightarrow \mathbb{R}\) is the objective function. - \(\mathcal{X} \subseteq\mathbb{R}^{D}\) is a compact constraint set. - The objective function \(f\) is expensive-to-evaluate (time or cost) and lacks known special structure - \(f\) is a “black box”. - Typically only function values \(f(x)\) can be sampled; no first- or second-order derivative information - known as “derivative-free” optimization. - Allows samples \(f(x)\) to be obscured by stochastic noise. - The focus is on finding a global rather than local optimum.
Applications
Bayesian optimization has been applied to solve a wide range of problems, including: - learning to rank, - computer graphics and visual design, - robotics, - sensor networks, - automatic algorithm configuration, - automatic machine learning toolboxes, - reinforcement learning, - planning, - visual attention, - architecture configuration in deep learning, - static program analysis, - experimental particle physics, - chemistry, - material design, and - drug development.
Basic algorithm for Bayesian optimization
BO consists of two main components: 1. A model able to calibrate uncertainty of the objective function called the surrogate model, and 2. an acquisition function for deciding where to sample next.
Suppose that the budget is \(N\) function evaluations, typically \(N\leq 1000\).
Roughly speaking, BO algorithms uses the following steps: - Observe \(f\) at \(n_0\) points, \({x_i}{i=1}^{n_0}\), according to an initial space-filling experimental design. - Set \(n = n_0\). - While \(n < N\): - Update the surrogate model of \(f\) using all available data \({x_i}{i=1}^{n}\). - Find next point \(x_{n+1}\) to evaluate by optimizing an acquisition function \(\alpha_n: \mathcal{X} \rightarrow \mathbb{R}\). I.e., \[ x_{n+1} \in \arg \max_{\mathbf{x}\in \mathcal{X}} \alpha_{n}\left(\mathbf{x}\right) \] - Evaluate \(f\) at the next point \(x_{n+1}\). - Increment \(n\) by one.
Return a solution.
Optimizing the acquisition function
- Note that optimizing the objective function \(f\) is replaced by optimizing a sequence of acquisition functions.
- Acquisition function design trades-off between exploration and exploitation.
- Regions in the search space \(\mathcal{X}\) that were not evaluated often get higher priority, and, at the same time, regions with promising function values get high priority.
- The acquisition function is cheap to evaluate.
- First- or second-order derivative information is typically available.
- Standard techniques from nonlinear optimization can be used.
Gaussian processes
Gaussian processes (GPs) are often used to create surrogate models of the expensive-to-evaluate function we wish to optimize.
Gaussian processes can be seen as an infinite-dimensional generalization of multivariate normal distributions.
In particular, a Gaussian process is a stochastic process \({f(x)}{x\in\mathcal{X}}\), denoted \(f \sim \mathcal{GP}(\mu{0},k)\), such that given any finite collections of points \(x_{1:n} := (x_1,\ldots,x_n) \in \mathcal{X}^{n}\), it holds that
\[ f(x_{1:n}):=\left(f(x_1),\ldots,f(x_n)\right) \sim \mathcal{N}{n}(\mu{0}(x_{1:n}),\Sigma_{0}(x_{1:n})), \]
where \(\mu_{0}(x_{1:n})\) is defined as
\[ \mu_{0}(x_{1:n}) := (\mu_{0}(x_{1}),\ldots,\mu_{0}(x_{n})) \]
for some function \(\mu_{0}:\mathcal{X} \rightarrow \mathbb{R}\) called the mean function (typically set to zero in Bayesian optimization applications), and where \(\Sigma_{0}(x_{1:n})\in\mathbb{S}^{n}_{+}\) is defined as
\[ [\Sigma_{0}(x_{1:n})]_{i,j} := k(x_i,x_j) \]
for each \(i,j=1,\ldots,n\), where \(k:\mathcal{X}\times\mathcal{X} \rightarrow \mathbb{R}\) is called the kernel function (chosen such that the resulting covariance matrix \(\Sigma_{0}(x_{1:n})\) is positive semidefinite, regardless of the collection of points \(x_{1:n}\) chosen).
Examples of kernal functions
- Power exponential/Gaussian kernel: \(k(x_i,x_j) = \sigma^2 \exp\left(-\frac{\lVert x_i-x_j \rVert^2}{2}\right)\) where \(\sigma>0\).
- Matérn-\(\nu\) kernel: \(k(x_i,x_j) = \sigma^2 \frac{2^{1-\nu}}{\Gamma(\nu)} \left( \sqrt{2\nu} \lVert x_i-x_j \rVert\right)^{\nu}K_{\nu}\left( \sqrt{2\nu} \lVert x_i-x_j \rVert \right)\) where \(\sigma,\nu>0\), \(\Gamma\) is the Gamma function, and \(K_{\nu}\) is the modified Bessel function of the second kind of order \(\nu\).
In both cases, the norm \(\lVert x\rVert\) is defined by \[ \lVert x \rVert^2 = x^{T}L^{-2}x, \]
where \(L = \text{diag}(\ell_1,\ldots,\ell_D) \succ 0\).
Note that the kernal functions introduces hyper-parameters, e.g., \(\ell_{1:D}\) and \(\sigma\) above. These hyper-parameters are found by, e.g., maximizing \(p(f(x_{1:n}) \mid \sigma,\ell_{1:D}, x_{1:n})\) (maximum likelihood estimation) or \(p(\sigma,\ell_{1:D} \mid f(x_{1:n}), x_{1:n})\) (maximum a posteriori estimation).
Why use Gaussian processes?
Properties that make Gaussian distributions easy to work with,
- sums and linear transformations of Gaussians are Gaussian,
- marginal distributions of Gaussians are still Gaussian, and
- conditional distributions of joint Gaussians are still Gaussian,
translate into properties that make Gaussian processes easy to work with.
Moreover, Gaussian processes come equipped with the uncertainty in their prediction!
In particular, suppose we are given observations \(x_{1:n},f(x_{1:n})\) as above. Then the posterior distribution for a test point \(x \in \mathcal{X}\) is given by
\[ f(x) \mid f(x_{1:n}) \sim \mathcal{N}(\mu_{n}(x),\sigma_{n}^{2}(x)), \]
where
\[ \mu_{n}(x) = \Sigma_{0}(x,x_{1:n})\Sigma_{0}(x_{1:n},x_{1:n})^{-1}(f(x_{1:n}) - \mu_{0}(x_{1:n})) + \mu_{0}(x) \]
and
\[ \sigma_{n}^{2}(x) = k(x,x) - \Sigma_{0}(x,x_{1:n})\Sigma_{0}(x_{1:n},x_{1:n})^{-1}\Sigma_{0}(x_{1:n},x), \]
where
\[ \Sigma_{0}(x,x_{1:n}) = [k(x,x_1) \;\ldots\; k(x,x_n)] \]
and
\[ \Sigma_{0}(x_{1:n},x) = [k(x_1,x) \;\ldots\; k(x_n,x)]^{T}. \]
This posterior distribution \(f(x) \mid f(x_{1:n})\) is the surrogate model used in Bayesian optimization!
A visual exploration of Gaussian processes
The following blog post contains a visual introduction to Gaussian processes that compliments the one above.
Acquisition functions
Each acquisition balances exploration-exploitation in a different way. There is no universal best method.
Below we give a few examples of commonly used acquisition functions. These are based on the surrogate models contructed via Gaussian processes described in the previous notebook.
Expected improvement
\[ \alpha_{n}(x) = \mathbb{E}\left[\max(0,f(x)-f_{n}^{\star})\mid x_{1:n},f(x_{1:n})\right] = \max(0,f(x)-f_{n}^{\star}) + \sigma_{n}(x)\phi\left(\frac{\mu_{n}(x)-f_{n}^{\star}}{\sigma_{n}(x)}\right)-\left|\mu_{n}(x)-f_{n}^{\star}\right|\Phi\left(\frac{\mu_{n}(x)-f_{n}^{\star}}{\sigma_{n}(x)}\right), \]
where \(f_{n}^{\star} = \max_{i=1,\ldots,n}f(x_{i})\), and \(\phi\) the probability density function (PDF) and \(\Phi\) the cumulative distribution function (CDF) of the standard normal distribution.
Upper confidence bounds (UCB)
\[ \alpha_{n}(x) = \mu_{n}(x) + \beta_{n} \sigma_{n}(x), \] where \(\beta_n\geq0\) is a scaler that explicity lets us balance exploration and exploitation.
Thompson sampling
\[ \alpha_{n}(x) = g(x), \] where \(g\sim\mathcal{GP}(\mu_n,c_n)\) and \[ c_n(x,y) = k(x,y) - \Sigma_{0}(x,x_{1:n})\Sigma_{0}(x_{1:n},x_{1:n})^{-1}\Sigma_{0}(x_{1:n},y). \] Later when optimizing this acquisition function, two alternatives are: - Sample \(g\) on a finte set of points and find the \(\arg\max\), or - use Fourier features to get a continuous/differentiable sample \(g\) and then optimize. See [Rahimi and B. Recht, 2007].
Scalable Bayesian optimization
Batch Bayesian optimization/parallel function evaluations
- Note that in the basic algorithm for Bayesian optimization described in the previous notebook, new points \(x_{n+1}\) are evaluated sequentially.
- Using multiple computing resources allows for obtaining multiple function evaluations at new points \({x_{n+1}^{(1)},\ldots,x_{n+1}^{(q)}}\) in parallel, at each iteration.
- In such a case, the acquisition function needs to be modified to handle batch acquisition of new points \({x_{n+1}^{(1)},\ldots,x_{n+1}^{(q)}}\).
- In our work, we utilize parallel function evaluations as a first way of scalability. Details are described in the subsection below.
Distributed Gaussian processes via trust regions
GPs are a popular choice for the surrogate model but they have limitations: - They show nice properties and are often sample-efficient. - However, they do not scale well to higher dimensions (large \(D\)) and break down in more than 20-30 dimensions [Frazier, 2018]. - Reason: - They rely on the distance between point which is large in high dimensions. - In particular, large portions of the space receive high uncertainty. - As a result, the algorithm focuses on these regions of the space and makes no progress in a reasonable evaluation budget.
This motivates the design of a collection of local GP models restricted to small regions of the search space called trust regions (TRs). - This scheme was first presented in [Eriksson, et al., 2019] in an algorithm called TuRBO. - In our work, we utilize the same idea of multiple trust regions with its own independent GP surrogate model. - Note that this approach runs multiple trust regions \(\text{TR}_1,\ldots,\text{TR}_m\) in parallel, giving a second way of scalability. - Moreover, its empirically observed in [Eriksson, et al., 2019] that using multiple trust regions allows to optimize in higher dimensions \(D\) compared to nominal methods, giving a third way of scalability.
UPDATE BELOW!!!
One way of dealing with this problem are trust regions (TRs). ref Here, the support for possible candidates is restricted to a small portion of the search space. TRs are centered on the incumbent point and shrink as the optimization progresses.
TuRBO is an algorithm to make GPs scalable. It uses trust regions, i.e., the support for candidates is restricted to a small region of the search space. This way, the regions of high uncertainty become managable and the algorithm can make progress with low evaluation budget. One problem of trust regions is that they can run in local maxima. To deal with this, TuRBO can run multiple trust regions in parallel. Each TR has its own surrogate model such that no data have to be shared between TRs. Furthermore, TuRBO supports parallel function evaluations that give another way of scalability.
In summary, TuRBO achieves scalability in three ways: 1. TuRBO allows to optimize in higher dimensions. 2. TuRBO can be run in parallel. 3. TuRBO supports parallel function evaluations.
In the original TuRBO implementation, a batch of points to be evaluated is found by taking the points with maximum acquisiton function value across the different TRs. We change the implementation such that every TR fills its own batch. While this leads to more function evaluations, it avoids communication between nodes and is acceptable in a high-compute setting.
Effective latent representation via deep kernel learning
The idea of deep kernel learning (DKL) is to learn a representation of the inputs \(x\) that are more meaningful for the GP. For example, GP kernels most commonly assume that the function is stationary. If this assumption is violated, the performance of GPs can decrease significantly. By using DKL, GPs could restore stationarity.
More specifically, given a kernel function \(k(x,y)\), the DKL kernel has the form \(k(\phi(x|\theta),\phi(y|\theta))\) where \(\phi\) is a neural network parametrized by \(\theta\). In the likelihood optimization of the GP, \(\theta\) is optimized alongside the other GP hyperparameters.
Our implementation
We packaged our implementation to deal with serialization issues of Spark. Therefore, we show the implementation in markdown cells as the code in the notebooks is quite short and incomprehensible.
Optimization problem
We first define a helper function that projects points from the GP search space \([0,1]^D\) to the actual support of the function \(f\).
from botorch.utils.transforms import unnormalize
def eval_objective(x, fun):
"""This is a helper function we use to unnormalize a point from [0, 1]^D and evaluate it"""
return fun(unnormalize(x, fun.bounds))
We define a generic class for optimization problems.
from abc import ABC, abstractmethod
import torch
import numpy as np
class OptimizationProblem(ABC):
"""
This is an abstract class for generic optimization problems we consider
"""
@abstractmethod
def __init__(self, dim: int):
"""
implemented by subclasses
"""
raise NotImplementedError()
@abstractmethod
def __call__(self, x: torch.Tensor):
"""
implemented by subclasses
"""
raise NotImplementedError()
@abstractmethod
def lb(self) -> np.ndarray:
"""
returns the lower bound of the problem
"""
raise NotImplementedError()
@abstractmethod
def ub(self) -> np.ndarray:
"""
returns the upper bound of the problem
"""
raise NotImplementedError()
@abstractmethod
def dim(self) -> int:
"""
returns the dimensionality of the problem
"""
raise NotImplementedError()
We define the Ackley and Griewank functions as a subclass for OptimizationProblem:
from botorch.test_functions import Ackley as _Ackley
class Ackley(OptimizationProblem):
def __init__(self, dim: int):
self._dim = dim
self._ackley = _Ackley(dim=dim, negate=True)
def __call__(self, x: torch.Tensor):
return eval_objective(x, self._ackley)
def lb(self) -> np.ndarray:
return self._ackley.bounds[0]
def ub(self) -> np.ndarray:
return self._ackley.bounds[1]
def dim(self) -> int:
return self._dim
class Griewank(OptimizationProblem):
def __init__(self, dim: int):
self._dim = dim
self._griewank = _Griewank(dim=dim, negate=True)
self._name = f'Griewank-{dim}'
def __call__(self, x: torch.Tensor):
return eval_objective(x, self._griewank)
def lb(self) -> np.ndarray:
return self._griewank.bounds[0]
def ub(self) -> np.ndarray:
return self._griewank.bounds[1]
def dim(self) -> int:
return self._dim
TuRBO state
TuRBO is an algorithm for scalable high-dimensional BO. We first define the state of a TuRBO instance:
@dataclass
class TurboState:
dim: int # dimensionality of the search space
batch_size: int # number of parallel function evaluations
length: float = 0.8 # initial TR base length
length_min: float = 0.5 ** 7 # min TR base length
length_max: float = 1.6 # max TR base length
failure_counter: int = 0 # number of times we did not make progress in optimizing the function
failure_tolerance: int = float("nan") # Note: Post-initialized, after more than failure_tolerance failures, we shrink the TR
success_counter: int = 0 # number of times we made progress in optimizing the function
success_tolerance: int = 10 # after that many successes, we increase the TR size
best_value: float = -float("inf") # best value observed
restart_triggered: bool = False # whether we want to restart this TR
def __post_init__(self):
# the failure tolerance increases with the dimensionality of the problem
# and decreases with batch size as batches correspond to parallel
# function evaluations
self.failure_tolerance = math.ceil(
max([4.0 / self.batch_size, float(self.dim) / self.batch_size])
)
Depending on the \(y\)-value of the next point evaluated, we want to update the state;
def update_state(state, y_next):
# if we made progress in optimizing the function
if max(y_next) > state.best_value + 1e-3 * math.fabs(state.best_value):
state.success_counter += 1
state.failure_counter = 0
else:
state.success_counter = 0
state.failure_counter += 1
# if we made state.success_tolerance many progresses
if state.success_counter == state.success_tolerance: # Expand trust region
state.length = min(2.0 * state.length, state.length_max)
state.success_counter = 0
elif state.failure_counter == state.failure_tolerance: # Shrink trust region
state.length /= 2.0
state.failure_counter = 0
state.best_value = max(state.best_value, max(y_next).item())
if state.length < state.length_min:
state.restart_triggered = True
return state
Next, we define the optimize function of a TurboInstance:
def optimize(self):
# create a number of initial points by a sobol sequence
x_init = self.get_initial_points()
# evaluate function for initial points
y_init = torch.tensor([self.function(x) for x in x_init])
# maintain tensors of all observations
self.X = torch.cat((self.X, x_init), dim=0)
self.y = torch.cat((self.y, y_init))
# while the trust region isnt too smalle
while not self.state.restart_triggered:
# normalize function values
train_y = (self.y - self.y.mean()) / self.y.std()
# get model for training points (pass points if no deep kernel used, otherwise use deep kernel)
model = self.model(
self.X, train_y.unsqueeze(-1), **self.model_kwargs) if not hasattr(self.model,
'feature_extractor') else self.model
with gpytorch.settings.max_cholesky_size(float("inf")):
# Fit the model if not in deep kernel mode
if not hasattr(self.model, 'feature_extractor'):
mll = self.mll_opt(model.likelihood, model)
fit_gpytorch_torch(mll, options={'disp': False})
# Create a batch
x_next = generate_batch(
state=self.state,
model=model,
X=self.X,
Y=train_y,
batch_size=self.batch_size,
n_candidates=self.n_candidates,
num_restarts=self.num_restarts,
raw_samples=self.raw_samples,
acqf="ts",
)
# evaluate function for new points
y_next = torch.tensor([self.function(x) for x in x_next])
# update state based on new function values
self.state = update_state(self.state, y_next)
# append to local observations
self.X = torch.cat((self.X, x_next), dim=0)
self.y = torch.cat((self.y, y_next), dim=0)
print(
f"{self.identifier}: {len(self.X)}) Best value: {self.state.best_value:.3}, TR length: {self.state.length:.3f}"
)
self.has_run = True
return self.X, self.y
The initial points are simply drawn from a scrambled Sobol sequence:
def get_initial_points(self):
sobol = SobolEngine(dimension=self.dim, scramble=True, seed=self.seed)
X_init = sobol.draw(n=self.n_init)
return X_init
Deep Kernel Model
Next, we define the deep kernel as a kernel that prepends a neural network as a feature extractor. The NN is trained by maximum likelihood.
import gpytorch
import torch
from botorch.models.gpytorch import GPyTorchModel
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import MaternKernel, ScaleKernel, GridInterpolationKernel
from gpytorch.means import ConstantMean
from gpytorch.models import ExactGP
from gpytorch.utils.grid import ScaleToBounds
from torch.nn import Linear, ReLU
class FeatureExtractor(torch.nn.Sequential):
'''
The feature extractor for the inputs
'''
def __init__(self, data_dim, layer_depths = None):
super(FeatureExtractor, self).__init__()
self.add_module('linear1', Linear(data_dim, 128))
self.add_module('relu1', ReLU())
self.add_module('linear2', Linear(128, 64))
self.add_module('relu2', ReLU())
self.add_module('linear3', Linear(64, 16))
self.add_module('relu3', ReLU())
self.add_module('linear5', Linear(16, 2))
class DeepKernelGPRegressor(GPyTorchModel, ExactGP):
'''
Custom GP model that first calls the feature extractor and passes the outputs to the actual kernel.
'''
# Freeze everything but the last layer when training locally?
def __init__(self, train_x, train_y, likelihood, architecture):
super(DeepKernelGPRegressor, self).__init__(train_x, train_y, likelihood)
self.mean_module = ConstantMean()
self.covar_module = GridInterpolationKernel(
ScaleKernel(MaternKernel(ard_num_dims=2)),
num_dims=2, grid_size=100
)
self.feature_extractor = architecture
# This module will scale the NN features so that they're nice values
self.scale_to_bounds = ScaleToBounds(-1., 1.)
def __call__(self, *args, **kwargs):
with gpytorch.settings.debug(False):
return super().__call__(*args, **kwargs)
def forward(self, x):
# We're first putting our data through a deep net (feature extractor)
projected_x = self.feature_extractor(x)
projected_x = self.scale_to_bounds(projected_x) # Make the NN values "nice"
mean_x = self.mean_module(projected_x)
covar_x = self.covar_module(projected_x)
return MultivariateNormal(mean_x, covar_x)
Scalable Optimizer
The ScalableOptimizer maintains multiple TurboInstances:
import os
from logging import info
from typing import Optional, Dict
import torch
import tqdm
from botorch.models import SingleTaskGP
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.mlls import ExactMarginalLogLikelihood
from pyspark.sql import SparkSession
from torch import Tensor
from scalable_gps.dkl_model import FeatureExtractor, DeepKernelGPRegressor
from scalable_gps.objective import OptimizationProblem
from scalable_gps.turbo_state import TurboInstance
from scalable_gps.utils import save
class ScalableOptimizer:
def __init__(self,
objective: OptimizationProblem,
outer_iterations: int = 10,
num_parallel: int = 2,
num_total_iterations: int = -1,
batch_size: int = 2,
turbo_kwargs: Optional[Dict] = {},
use_dkl: bool = True,
name: str = 'TurBO-DKL',
save_path: str = os.getcwd()
):
# create spark session if it doesnt exist yet
self.spark = SparkSession.builder.getOrCreate()
self.sc = self.spark.sparkContext
self.batch_size = batch_size
self.num_total_iterations = num_total_iterations
self.num_parallel = num_parallel
self.batch_size = batch_size
self.turbo_kwargs = turbo_kwargs
self.objective = objective
self.dim = objective.dim()
self.outer_iterations = outer_iterations
self.name = name
self.use_dkl = use_dkl
self.save_path = save_path
def optimize(self):
# maintain tensors of all observations from all TurboInstances
x_global = torch.empty((0, self.dim))
y_global = torch.empty(0)
# we start with no deep kernel
deep_kernel_model = None
for i_outer in range(self.outer_iterations):
# for each outer iteration, define a list of TurboInstances
info(f"Starting outer iteration {i_outer + 1}")
self.turbo_processes = [
TurboInstance(
batch_size=self.batch_size,
function=self.objective,
model=deep_kernel_model if deep_kernel_model is not None else SingleTaskGP,
identifier=f"TR-{i}")
for i in range(self.num_parallel)
]
# parallelize the list
turbos = self.sc.parallelize(self.turbo_processes)
# define the optimization
res = turbos.map(lambda t: t.optimize())
# and collect the data
data = res.collect()
# append to global observations
x_aggregated = torch.cat([Tensor(proc_data[0])
for proc_data in data], axis=0)
y_aggregated = torch.cat([Tensor(proc_data[1])
for proc_data in data], axis=0)
x_global = torch.cat((x_global, x_aggregated), dim=0)
y_global = torch.cat((y_global, y_aggregated), dim=0)
y_global_normalized = (y_global - y_global.mean()) / y_global.std()
if self.use_dkl:
# train deep kernel
deep_kernel_model = self._train_deepkernel(
x_global, y_global_normalized)
# save optimization results
save(x_global, y_global, self.save_path, self.name, self.objective.name())
def _train_deepkernel(self, X: Tensor, y: Tensor, num_iters: int = 100):
'''
Train a feature extractor (neural net) for the inputs
'''
likelihood = GaussianLikelihood()
feature_extractor = FeatureExtractor(self.objective.dim())
dkl_model = DeepKernelGPRegressor(X, y, likelihood, feature_extractor)
optimizer = torch.optim.Adam([
{'params': dkl_model.feature_extractor.parameters()},
{'params': dkl_model.covar_module.parameters()},
{'params': dkl_model.mean_module.parameters()},
{'params': dkl_model.likelihood.parameters()},
], lr=0.01)
mll = ExactMarginalLogLikelihood(likelihood, dkl_model)
iterator = tqdm.tqdm(range(num_iters))
# on-the-fly SGD - should probably be implemented according to a paper on overfit in DKL
for i in iterator:
# Zero backprop gradients
optimizer.zero_grad()
# Get output from model
output = dkl_model(X)
# Calc loss and backprop derivatives
loss = -mll(output, y)
loss.backward()
iterator.set_postfix(loss=loss.item())
optimizer.step()
return dkl_model
Code notebook
This notebook contains the code that we ran in a notebook, everything else was written as a library (see previous notebook).
from botorch.models import SingleTaskGP
import shutil
from scalable_gps.objective import Ackley, Griewank, Rover
from scalable_gps.optimizer import ScalableOptimizer
SAVE_PATH = "/dbfs/FileStore/df/"
objectives = [Ackley(4), Griewank(10)]
for objective in objectives:
for use_dkl in [True, False]:
for rep in range(5):
print(f"Repetition {rep}")
name = "TurBO-DKL" if use_dkl else "TurBO"
turbo_kwargs = {
'model': SingleTaskGP,
}
so = ScalableOptimizer(objective, num_parallel=4, turbo_kwargs={}, name=name, use_dkl=use_dkl, outer_iterations=5, save_path=SAVE_PATH) #<--- This makes the files permanent
so.optimize()
import os
os.listdir("/dbfs/FileStore/df/")
basedir = SAVE_PATH
algo_benchs = {}
for path, subdirs, files in os.walk(basedir):
for name in files:
bench = path.split("/")[-2]
algo = path.split("/")[-1]
y = pd.read_csv(os.path.join(path, name)).to_numpy()[:, -1]
y = np.array([max(y[:i]) for i in range(1,len(y)+1)])
if not bench in algo_benchs:
algo_benchs[bench] = {}
if not algo in algo_benchs[bench]:
algo_benchs[bench][algo] = []
algo_benchs[bench][algo].append(y)
fig, axs = plt.subplots(1, len(algo_benchs), )
for i, bench in enumerate(algo_benchs.keys()):
ax = axs[i] if len(algo_benchs)>1 else axs
for algo, y in algo_benchs[bench].items():
minlen = min([len(yy) for yy in y])
y = [yy[:minlen] for yy in y]
mean = np.mean(np.array(y), axis=0)
ax.plot(np.arange(len(mean)), mean, label=algo)
ax.set_title(bench)
ax.legend()
import os
from copy import copy
from glob import glob
from os.path import join
from typing import List
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import sem
# plot configuration
COLORS = {
'TurBO': 'crimson',
'TurBO-DKL': 'dodgerblue',
}
REGRETS = {
'Branin': -0.397887,
'Hartmann-3': 3.86278,
'Hartmann-6': 3.32237,
'Ackley-4': 0,
'Griewank-10': 0,
"Roverplanning": 5,
}
INIT_NBRS = {
'Branin': 3,
'Hartmann-3': 4,
'Hartmann-6': 7,
'Ackley-4': 9,
'Griewank-10': 11,
"Roverplanning":61
}
NAMES = {
'TurBO': 'TurBO',
'TurBO-DKL': 'TurBO-DKL',
}
PLOT_LAYOUT = dict(linewidth=2, markevery=20, markersize=8, markeredgewidth=5)
plt.rcParams['font.family'] = 'serif'
def process_funcs_args_kwargs(input_tuple):
'''
Helper function for preprocessing to assure that the format of (func, args, kwargs is correct)
'''
if len(input_tuple) != 3:
raise ValueError(
f'Expected 3 elements (callable, list, dict), got {len(input_tuple)}')
if not callable(input_tuple[0]):
raise ValueError('Preprocessing function is not callable.')
if type(input_tuple[1]) is not list:
raise ValueError(
'Second argument to preprocessing function is not a list.')
if type(input_tuple[2]) is not dict:
raise ValueError(
'Third argument to preprocessing function is not a dict.')
return input_tuple
def filter_paths(all_paths, included_names=None):
'''
Filters the provided paths by the included names, if specified.
Returns a tuple of the filtered paths and their names.
'''
all_names = [benchmark_path.split('/')[-1]
for benchmark_path in all_paths]
if included_names is not None:
used_paths = []
used_names = []
for path, name in zip(all_paths, all_names):
if name in included_names:
used_paths.append(path)
used_names.append(name)
return used_paths, used_names
return all_paths, all_names
def get_files_from_experiment(experiment_name, benchmarks=None, acquisitions=None):
'''
For a specific experiment, gets a dictionary of all the {benchmark: {method: [output_file_paths]}}
as a dict, including all benchmarks and acquisition functions unless specified otherwise in
the arguments.
'''
paths_dict = {}
all_benchmark_paths = glob(join(experiment_name, '*'))
filtered_benchmark_paths, filtered_benchmark_names = filter_paths(
all_benchmark_paths, benchmarks)
# *ensures hidden files are not included
for benchmark_path, benchmark_name in zip(filtered_benchmark_paths, filtered_benchmark_names):
paths_dict[benchmark_name] = {}
all_acq_paths = glob(join(benchmark_path, '*'))
print(join(benchmark_path, '*'))
filtered_acq_paths, filtered_acq_names = filter_paths(
all_acq_paths, acquisitions)
for acq_path, acq_name in zip(filtered_acq_paths, filtered_acq_names):
run_paths = glob(join(acq_path, '*'))
paths_dict[benchmark_name][acq_name] = run_paths
print(paths_dict)
return paths_dict
def get_dataframe(paths, funcs_args_kwargs=None, idx=0):
'''
For a given benchmark and acquisition function (i.e. the relevant list of paths),
creates the dataframe that includes the relevant metrics.
Parameters:
paths: The paths to the experiments that should be included in the dataframe
funcs_args_kwargs: List of tuples of preprocessing arguments,
'''
# ensure we grab the name from the right spot in the file structure
names = [path.split('/')[-1].split('.')[0] for path in paths]
# just create the dataframe and set the column names
complete_df = pd.DataFrame(columns=names)
# tracks the maximum possible length of the dataframe
max_length = None
for path, name in zip(paths, names):
per_run_df = pd.read_csv(path)
# this is where we get either the predictions or the true values
if funcs_args_kwargs is not None:
for func_arg_kwarg in funcs_args_kwargs:
func, args, kwargs = process_funcs_args_kwargs(func_arg_kwarg)
per_run_df = func(per_run_df, name, *args, **kwargs)
complete_df.loc[:, name] = per_run_df.iloc[:, 0]
return complete_df
def get_min(df, run_name, metric, minimize=False):
'''
Make sure we only print the best values
'''
if minimize:
min_observed = np.inf
else:
min_observed = -np.inf
mins = np.zeros(len(df))
for r, row in enumerate(df[metric]):
if minimize:
if row < min_observed:
min_observed = row
mins[r] = min_observed
else:
if row > min_observed:
min_observed = row
mins[r] = min_observed
return pd.DataFrame(mins, columns=[run_name])
def compute_regret(df, run_name, regret, log=True):
'''
Compute regret depending on the function
'''
if log:
mins = df.iloc[:, 0].apply(lambda x: x)
else:
mins = df.iloc[:, 0].apply(lambda x: x)
return pd.DataFrame(mins)
def plot_optimization(data_dict, preprocessing=None, title='benchmark', xlabel='X', ylabel='Y', fix_range=None,
only_plot=-1, names=None, predictions=False, init=2, n_markers=20, n_std=1, show_ylabel=True,
maxlen=-1, plot_ax=None, first=True, show_noise=None):
'''
Plot all the optimization runs for the different benchmarks and optimization algorithms.
'''
if plot_ax is None:
fig, ax = plt.subplots(figsize=(25, 16))
else:
ax = plot_ax
min_ = np.inf
for run_name, files in data_dict.items():
plot_layout = copy(PLOT_LAYOUT)
plot_layout['c'] = COLORS.get(run_name, 'k')
plot_layout['label'] = NAMES.get(run_name, 'Nameless Run')
if plot_layout['label'] == 'Nameless Run':
continue
plot_layout['marker'] = '*'
plot_layout['markersize'] = 10
plot_layout['markeredgewidth'] = 2
# preprocess the data for the set of runs
result_dataframe = get_dataframe(files, preprocessing)
# convert to array and plot
data_array = result_dataframe.to_numpy()
markevery = np.floor(maxlen / n_markers).astype(int)
plot_layout['markevery'] = markevery
if maxlen > 0:
data_array = data_array[0:maxlen, :]
y_mean = data_array.mean(axis=1)
y_std = sem(data_array, axis=1)
X = np.arange(1, maxlen + 1)
if fix_range is not None:
ax.set_ylim(fix_range)
ax.plot(X, y_mean, **plot_layout)
ax.fill_between(X, y_mean - n_std * y_std, y_mean + n_std *
y_std, alpha=0.1, color=plot_layout['c'])
ax.plot(X, y_mean - n_std * y_std, alpha=0.5, color=plot_layout['c'])
ax.plot(X, y_mean + n_std * y_std, alpha=0.5, color=plot_layout['c'])
min_ = min((y_mean - n_std * y_std).min(), min_)
ax.axvline(x=init, color='k', linestyle=':', linewidth=4)
ax.tick_params(axis='x', labelsize=18)
ax.tick_params(axis='y', labelsize=18)
ax.set_xlabel(xlabel, fontsize=24)
ax.set_title(title, fontsize=30)
if show_ylabel:
ax.set_ylabel(ylabel, fontsize=24)
ax.legend()
return ax
def plot(algos: List[str], functions: List[str], save_path: str, experiment_name: str, maxlen: int = 750):
files = get_files_from_experiment(
f'{os.getcwd()}/results', functions, algos)
num_benchmarks = len(files)
if num_benchmarks == 0:
raise ValueError('No files')
fig, ax = plt.subplots(1, num_benchmarks, figsize=(25, 9))
if num_benchmarks == 1:
ax = [ax]
for benchmark_idx, (benchmark_name, paths) in enumerate(files.items()):
preprocessing = [(get_min, [], {'metric': 'y'}), (compute_regret, [], {
'log': True, 'regret': REGRETS[benchmark_name]})]
plot_optimization(paths,
xlabel='Iteration',
ylabel='Log Regret',
n_std=2,
preprocessing=preprocessing,
maxlen=maxlen,
plot_ax=ax[benchmark_idx],
first=benchmark_idx == 0,
n_markers=10,
init=INIT_NBRS[benchmark_name],
title=benchmark_name,
show_ylabel=False,
)
plt.tight_layout()
plt.savefig(f"{os.getcwd()}/{experiment_name}_{'_'.join(functions)}.pdf")
plt.show()
plot(["TurBO-DKL"],["Ackley-4", "Griewank-10"], save_path=SAVE_PATH, experiment_name="plot_name", axlen=750)
Distributed Training of the deep kernel
We tried to distribute the learning of the deep kernel (neural network optimization) in a distributed manner. However, we did not succeed in finishing this on time. We argue that our code is still scalable since the training of the neural network is only done once for all trust regions.
For sake of completeness, we provide the code for the distributed training below. It uses sparktorch which allows for training torch model with spark. The code is a method of ScalableOptimizer:
def _train_deepkernel(self, X: Tensor, y: Tensor, num_iters: int = 100):
likelihood = GaussianLikelihood()
feature_extractor = FeatureExtractor(self.objective.dim())
dkl_model = DeepKernelGPRegressor(X, y, likelihood, feature_extractor)
optimizer = torch.optim.Adam
# make all the parameter generators into lists
parameters = [{'params': [p for p in dkl_model.feature_extractor.parameters()]},
{'params': [
p for p in dkl_model.covar_module.parameters()]},
{'params': [p for p in dkl_model.mean_module.parameters()]},
{'params': [p for p in dkl_model.likelihood.parameters()]},
]
mll = ExactMarginalLogLikelihood(likelihood, dkl_model)
torch_obj = serialize_torch_obj(
model=dkl_model,
# need to return a scalar, returns a vector of inidividual losses
criterion=lambda output, y_train: mll(output, y_train).sum(),
optimizer=optimizer,
lr=1e-3
)
data = self.sc.parallelize(
torch.cat((y.unsqueeze(-1), X), axis=1).detach().numpy().tolist())
df = data.toDF()
vector_assembler = VectorAssembler(
inputCols=df.columns[1:self.dim + 1], outputCol='features')
# Setup features
stm = SparkTorch(
inputCol='features',
labelCol='_1', # this tells SparkTorch to consider the first column as the label column
predictionCol='predictions',
torchObj=torch_obj,
verbose=0,
iters=30,
miniBatch=16
)
print('Training DKL...')
# Can be used in a pipeline and saved.
p = Pipeline(stages=[vector_assembler, stm]).fit(df)
pt_model = p.stages[1].getPytorchModel()
print('Trained.')
return pt_model
Experiments with ZerO initialisation
Project members:
- FisrtName LastName, Institution
- FisrtName LastName, Institution
Overview
In this project, we empirically investigate the claims of a recent paper ZerO Initialization: Initializing Neural Networks with only Zeros and Ones by Jiawei Zhao, Florian Tobias Schaefer, and Anima Anandkumar (hereafter: the authors).
In particular, we will compare the proposed initialisation method to the standard Kaiming initialisation while training: - a ResNet-18 on CIFAR-10 (as in the paper); - a Transformer on WikiText-2 (as in the paper); and - a denoising diffusion model on CIFAR-10 (a new experiment).
Background
Standard initialisation techniques
It is a widely known fact that the performance of deep neural networks can heavily depend on what values their weights are initialised to. For example, setting every weight to a constant value (such as 0) before training enforces identical weights throughout training, or may even completely prevent learning due to every gradients being zero.
Therefore, the standard way to initialise neural networks is to randomly sample "small" values around 0 for every weight \(w\):
\[ w \sim \mathcal{U(0-\varepsilon, 0 + \varepsilon)}\ .\]
One of the common methods, called Xavier initialisation, sets \(\varepsilon\) such that
\[ w \sim \mathcal{U\Bigg(-\frac{\sqrt{6}}{\sqrt{n + m}}, -\frac{\sqrt{6}}{\sqrt{n + m}}\Bigg)}\ ,\]
where \(n\) and \(m\) are the size of the layer \(w\) is in, and the size of the next layer, respectively. Another well-known method is the Kaiming or He initialisation, which (reusing the definition of \(n\) from earlier) samples the weights as follows:
\[ w \sim \mathcal{N}\Bigg(0, \frac{2}{n}\Bigg)\ .\]
Deep learning libraries such as Pytorch and Tensorflow automatically apply variants of these two methods.
Drawbacks and the role of BatchNorm
Even though Xavier and He initialisation are widely adopted to this day, research in the past few years has shown that they are not optimal in many scenarios. For example, with the default initialisation strategies, saturating nonlinearities (such as sigmoid or tanh) are often difficult to train with, and lower learning rates must be used in general (Ioffe and Szegedy, 2015) to avoid unstable learning curves. Batch normalisation (Ioffe and Szegedy, 2015) is an essential technique that allows one to use larger learning rates (speeding up the training process significantly) and enhances generalisation. However, batch normalisation has several drawbacks: it is computationally expensive; it can limit the expressivity of the model (Karras et al., 2019); and it introduces an undesired (probabilistic) dependency between datapoints of the same batch.
Later, Zhang et al. proposed Fixup initialisation, which eliminates the above problems simply by changing the initialisation slightly. Furthermore, they find that they can match the performance of batch-normalised networks by adding a scalar multiplier and a scalar bias variable in place of the normalisation.
Method
The authors propose ZerO initialisation: a fully deterministic technique that fills up the weight matrices with only zeros and ones. They claim that networks initialised with ZerO can match (even outperform) the performance of networks initialisated with the default methods, and batch normalisation can be replaced with the aforementioned two scalar variables as well. Additionally, by the virtue of being deterministic, ZerO enables better reproducibility of the training process and the authors report that the method results in low-rank, sparse representations.
Mathematically, ZerO uses two concepts from linear algebra. First, the partial identity matrix \(\mathbf{I}^\star \in \mathbb{R}^{n \times m}\), defined as:

We implement a function that returns the partial identity matrix of a given size:
import torch
from torch import nn
from scipy.linalg import hadamard
import numpy as np
def partial_identity(out_dim, in_dim):
"""Return the partial identity matrix with shape `out_dim` x `in_dim`."""
if out_dim < in_dim:
I = torch.eye(out_dim)
O = torch.zeros(out_dim, (in_dim - out_dim))
return torch.cat((I, O), 1)
elif out_dim == in_dim:
return torch.eye(out_dim)
else:
I = torch.eye(in_dim)
O = torch.zeros((out_dim - in_dim), in_dim)
return torch.cat((I, O), 0)
The second prerequisite concept for ZerO is the Hadamard matrix, which is a matrix consisting of 1 and -1 entries, defined recursively as follows:

with \(\mathbf{H}_0 := 1\).
Note that \(\mathbf{H}\) must be an n-by-n matrix where n is a square number. To construct a hadamard matrix, we will simply use the scipy library.
from scipy.linalg import hadamard
Now have everything we need to define ZerO initialisation:

def zerO_init_conv_layer_(weight):
"""
In-place initialise the given convolutional layer with zerO-init
using the following equation:
---------------------------------
W[:,:,n,n] := c * I_p * H_m * I_p
---------------------------------
where W: out_dim x in_dim x n_filters
I_p: out_dim x m (partial identity)
H_m: m x m (Hadamard matrix)
I_p: m x in_dim (partial identity)
"""
out_dim, in_dim, k = weight.shape[:3]
n = int(np.floor(k / 2))
if out_dim == in_dim:
weight.data[..., n, n] = torch.eye(in_dim)
elif out_dim < in_dim:
weight.data[..., n, n] = partial_identity(out_dim, in_dim).type_as(weight)
else:
m = int(np.ceil(np.log2(out_dim)))
c = 2 ** (-(m - 1) / 2)
H = lambda dim: torch.tensor(hadamard(dim)).type_as(weight)
I = lambda outd, ind: partial_identity(outd, ind).type_as(weight)
# NOTE: scipy's hadamard function differs from the paper's definition
# in that we need to pass 2^m as its size input instead of m
weight.data[..., n, n] = (
c * I(out_dim, 2**m) @ H(2**m) @ I(2**m, in_dim)
)
In this notebook, we will compare ZerO with the default initialisation using ResNet-18 and CIFAR-10. For this architecture, the authors initialised all convolutional layers (except the last one in in each residual block) with ZerO and the linear classification layer with 0 values when using ZerO. We follow the instructions in our implementation below:
from torchvision import models
def create_model(init_mode: str):
"""
The original ResNet-18 was adapted to ImageNet 1k; we need to change the number of classes to 10,
change the first convolutional layer to a 3x3 convolution with a stride and padding of 1.
Moreover, the layers should be initialized according to the initialization method requested.
In Torchvision, the default initialization of ResNet-18's convolutional layers is kaiming_uniform_.
"""
model = models.resnet18(weights=None, num_classes=10)
model.conv1 = nn.Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
model.maxpool = nn.Identity()
for m in model.modules():
if isinstance(m, (nn.Conv2d, nn.Linear)):
if init_mode == "kaiming":
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif init_mode == "xavier":
nn.init.xavier_uniform_(m.weight)
elif init_mode == "zerO":
# NOTE: we will apply ZerO at the end of this function
nn.init.zeros_(m.weight)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Apply ZerO to convolutional layers
if init_mode == "zerO":
for name, layer in model.named_modules():
if isinstance(layer, nn.Conv2d):
# Ignore last conv layer in each residual block
if not name.endswith(".conv2"):
zerO_init_conv_layer_(layer.weight)
return model
Dataset and model wrapper classes
To run the experiments, we need to implement standard boilerplate code for training the model and handling the CIFAR-10 dataset.
from torchvision import transforms
from torchvision.datasets import CIFAR10
import pytorch_lightning as pl
from torch.utils.data import random_split, DataLoader
import torchmetrics
import torch.nn.functional as F
import time
import horovod as hvd
import datetime
class CIFAR10DataModule(pl.LightningDataModule):
def __init__(
self,
batch_size: int = 256,
data_dir: str = "/dbfs/ml/Group_7/cifar10/",
seed: int = 42,
):
super().__init__()
self.data_dir = data_dir
self.batch_size = batch_size
self.split = [45000, 5000]
self.seed = seed
# default normalization process for CIFAR-10
self.train_transforms = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))
])
self.test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))
])
def prepare_data(self):
# download dataset
CIFAR10(self.data_dir, train=True, download=True)
CIFAR10(self.data_dir, train=False, download=True)
def setup(self, stage=None):
# Create train/val datasets
if stage == 'fit' or stage is None:
cifar_full_train = CIFAR10(self.data_dir, train=True, transform=self.train_transforms)
self.cifar_train, _ = random_split(cifar_full_train, self.split,
generator=torch.Generator().manual_seed(self.seed))
# The validation dataset uses different transformations so we construct it
# separately, but a proper split is ensured by fixing the random seed
cifar_full_val = CIFAR10(self.data_dir, train=True, transform=self.test_transforms)
_, self.cifar_val = random_split(cifar_full_val, self.split,
generator=torch.Generator().manual_seed(self.seed))
# Create test dataset
if stage == 'test' or stage is None:
self.cifar_test = CIFAR10(self.data_dir, train=False, transform=self.test_transforms)
def train_dataloader(self):
return DataLoader(self.cifar_train, batch_size=self.batch_size, shuffle=True, num_workers=4)
def val_dataloader(self):
return DataLoader(self.cifar_val, batch_size=self.batch_size, num_workers=4)
def test_dataloader(self):
return DataLoader(self.cifar_test, batch_size=self.batch_size, num_workers=4)
# ----------------------------------------------------------------------
class LitModel(pl.LightningModule):
def __init__(self, model, learning_rate, use_lr_warmup):
super().__init__()
self.model = model
self.learning_rate = learning_rate
self.accuracy = torchmetrics.Accuracy("multiclass", num_classes=10)
self.use_lr_warmup = use_lr_warmup
def forward(self, x):
return F.log_softmax(self.model(x), dim=-1)
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
# training metrics
preds = torch.argmax(logits, dim=1)
acc = self.accuracy(preds, y)
self.log('train_loss', loss, on_step=True, on_epoch=True, logger=True)
self.log('train_acc', acc, on_step=True, on_epoch=True, logger=True)
# ResNet paper re. LR: "divide it by 10 at 32k and 48k iterations"
# We found that the model can be trained for half the number of iterations
# without a major hit to the accuracy; we apply this change to save time
if self.trainer.global_step == 8_000 or self.trainer.global_step == 12_000:
for g in self.optimizers().param_groups:
g['lr'] /= 10
if self.trainer.global_step % 500 == 0:
print(f"[{datetime.datetime.now()}] Step {self.trainer.global_step}, loss = {loss:.2f}, acc = {acc*100:.2f}")
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
# validation metrics
preds = torch.argmax(logits, dim=1)
acc = self.accuracy(preds, y)
self.log('val_loss', loss)
self.log('val_acc', acc)
return loss
def test_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
# test metrics
preds = torch.argmax(logits, dim=1)
acc = self.accuracy(preds, y)
self.log('test_loss', loss)
self.log('test_acc', acc)
return loss
def configure_optimizers(self):
# Default optimizer configuration
optimizer = torch.optim.SGD(self.parameters(), lr=self.learning_rate, momentum=0.9, weight_decay=0.0001)
# Warmup as suggested by the zerO init paper
if self.use_lr_warmup:
scheduler = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.001, total_iters=10)
return [optimizer], [scheduler]
else:
return [optimizer]
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks import ModelCheckpoint, TQDMProgressBar, LearningRateMonitor
from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
import datetime
import horovod as hvd
def train(model, dm, log_folder, n_steps, do_test: bool = False, use_lr_warmup = False):
model = LitModel(model, learning_rate=0.05, use_lr_warmup=use_lr_warmup)
logger = TensorBoardLogger(log_folder)
# Initialize a trainer
trainer = pl.Trainer(max_steps=n_steps,
strategy="horovod",
accelerator='gpu',
devices=1,
callbacks=[
# EarlyStopping(monitor="val_acc", min_delta=0.00, patience=3, verbose=False, mode="max"),
# TQDMProgressBar(refresh_rate=10),
ModelCheckpoint(monitor='val_acc', mode='max'),
LearningRateMonitor(logging_interval='epoch'),
],
logger=logger,
enable_progress_bar=False
)
# Train the model
trainer.fit(model, dm)
# Evaluate the model on the validation set
if do_test:
# Evaluate the model on the test set
trainer.test(ckpt_path='best', datamodule=dm)
return trainer.callback_metrics["test_acc"]
else:
trainer.validate(ckpt_path='best', datamodule=dm)
return trainer.callback_metrics["val_acc"]
def run(dm, seed: int, n_steps: int, do_test: bool):
pl.seed_everything(seed)
torch.manual_seed(seed)
accs = dict()
# Init our model
model = create_model(init_mode="kaiming")
pl.seed_everything(seed)
torch.manual_seed(seed)
accs["kaiming"] = train(model, dm, log_folder="/dbfs/ml/Group_7/logs/resnet/kaiming", n_steps=n_steps, do_test=do_test).item()
pl.seed_everything(seed)
torch.manual_seed(seed)
model = create_model(init_mode="xavier")
pl.seed_everything(seed)
torch.manual_seed(seed)
accs["xavier"] = train(model, dm, log_folder="/dbfs/ml/Group_7/logs/resnet/xavier", n_steps=n_steps, do_test=do_test).item()
pl.seed_everything(seed)
torch.manual_seed(seed)
model = create_model(init_mode="zero")
pl.seed_everything(seed)
torch.manual_seed(seed)
accs["zero"] = train(model, dm, log_folder="/dbfs/ml/Group_7/logs/resnet/zero", n_steps=n_steps, do_test=do_test, use_lr_warmup=True).item()
return accs
import horovod.torch as hvd
from sparkdl import HorovodRunner
import json
def run_horovod_job():
hvd.init()
torch.cuda.set_device(hvd.local_rank())
seeds = [42, 151, 464, 3584, 6846]
for seed in seeds:
print("Random seed used:", seed)
pl.seed_everything(seed)
torch.manual_seed(seed)
dm = CIFAR10DataModule(batch_size=128, data_dir='data-%d'% hvd.rank())
dm.prepare_data()
dm.setup()
print("ZerO Init")
accs = run(dm, seed=seed, n_steps = 16_000, do_test = True)
if hvd.rank() == 0:
print(accs)
with open("results.txt", 'a') as out_file:
out_file.write(f"RANDOM SEED: {seed}")
out_file.write(json.dumps(accs))
hr = HorovodRunner(np=2, driver_log_verbosity='all')
hr.run(run_horovod_job)
tensorboard
%tensorboard --logdir /dbfs/ml/Group_7/logs/
install wget
# !wget https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/train.txt
# !wget https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/valid.txt
# !wget https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/test.txt
# !mkdir /dbfs/ml/Group_7/wikitext-2/
# !mv train.txt valid.txt test.txt /dbfs/ml/Group_7/wikitext-2/
import torch
from torch import nn
from scipy.linalg import hadamard
import numpy as np
def partial_identity(out_dim, in_dim):
"""Return the partial identity matrix with shape `out_dim` x `in_dim`."""
if out_dim < in_dim:
I = torch.eye(out_dim)
O = torch.zeros(out_dim, (in_dim - out_dim))
return torch.cat((I, O), 1)
elif out_dim == in_dim:
return torch.eye(out_dim)
else:
I = torch.eye(in_dim)
O = torch.zeros((out_dim - in_dim), in_dim)
return torch.cat((I, O), 0)
def zerO_init_conv_layer_(weight):
"""
In-place initialise the given convolutional layer with zerO-init
using the following equation:
---------------------------------
W[:,:,n,n] := c * I_p * H_m * I_p
---------------------------------
where W: out_dim x in_dim x n_filters
I_p: out_dim x m (partial identity)
H_m: m x m (Hadamard matrix)
I_p: m x in_dim (partial identity)
"""
out_dim, in_dim, k = weight.shape[:3]
n = int(np.floor(k / 2))
if out_dim == in_dim:
weight.data[..., n, n] = torch.eye(in_dim)
elif out_dim < in_dim:
weight.data[..., n, n] = partial_identity(out_dim, in_dim).type_as(weight)
else:
m = int(np.ceil(np.log2(out_dim)))
c = 2 ** (-(m - 1) / 2)
H = lambda dim: torch.tensor(hadamard(dim)).type_as(weight)
weight.data = (
c * H(2**m)[:out_dim, :in_dim]
)
We also apply ZerO to Transformer and evaluate it on WikiText-2 dataset (Vaswani et al., 2017). In each Transformer layer, we use ZerO to initialize both multi-head attention and feed-forward layers. Because the embedding size is fixed in the multi-head attention, we initialize the projection matrix of queries WQ as identity and the projection matrices of keys and values WK, W_V at zero. For the feed-forward layers, we initialize the connection matrices according to their hidden dimensions using Algorithm 1.
def zerO_init_multihead_attention(name, p):
if name.endswith(".q_proj_weight"):
nn.init.eye_(p)
if name.endswith(".k_proj_weight") or name.endswith(".v_proj_weight"):
nn.init.zeros_(p)
def zerO_init_model(model):
for name, p in model.named_parameters():
zerO_init_multihead_attention(name, p)
for name, m in model.named_modules():
if isinstance(m, nn.Linear):
zerO_init(m.weight)
return model
# Modified version of MultiheadAttention. There will be three separate matrices for queries, keys and values, regardless of dimensionality.
# We need this because the queries are initialized differently than the keys and values.
from torch.nn import MultiheadAttention
from typing import Optional
from torch.nn.parameter import Parameter
from torch.nn import Linear
# From torch/nn/modules/linear.py
class NonDynamicallyQuantizableLinear(Linear):
def __init__(self, in_features: int, out_features: int, bias: bool = True,
device=None, dtype=None) -> None:
super().__init__(in_features, out_features, bias=bias,
device=device, dtype=dtype)
# Based on torch.nn.MultiheadAttention
class ModifiedMultiheadAttention(MultiheadAttention):
__constants__ = ['batch_first']
bias_k: Optional[torch.Tensor]
bias_v: Optional[torch.Tensor]
def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False,
kdim=None, vdim=None, batch_first=False, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self._qkv_same_embed_dim = False # Changed here
self.num_heads = num_heads
self.dropout = dropout
self.batch_first = batch_first
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
if not self._qkv_same_embed_dim:
self.q_proj_weight = Parameter(torch.empty((embed_dim, embed_dim), **factory_kwargs))
self.k_proj_weight = Parameter(torch.empty((embed_dim, self.kdim), **factory_kwargs))
self.v_proj_weight = Parameter(torch.empty((embed_dim, self.vdim), **factory_kwargs))
self.register_parameter('in_proj_weight', None)
else:
self.in_proj_weight = Parameter(torch.empty((3 * embed_dim, embed_dim), **factory_kwargs))
self.register_parameter('q_proj_weight', None)
self.register_parameter('k_proj_weight', None)
self.register_parameter('v_proj_weight', None)
if bias:
self.in_proj_bias = Parameter(torch.empty(3 * embed_dim, **factory_kwargs))
else:
self.register_parameter('in_proj_bias', None)
self.out_proj = NonDynamicallyQuantizableLinear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
if add_bias_kv:
self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
self.bias_v = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
self._reset_parameters()
from typing import Union, Callable
from torch import Tensor
from torch.nn import Dropout
from torch.nn import Linear
from torch.nn import LayerNorm
from torch.nn import functional as F
from torch.nn import TransformerEncoderLayer
# From torch/nn/modules/transformer.py
def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]:
if activation == "relu":
return F.relu
elif activation == "gelu":
return F.gelu
raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
# Based on torch.nn.TransformerEncoderLayer
class ModifiedTransformerEncoderLayer(TransformerEncoderLayer):
__constants__ = ['batch_first', 'norm_first']
def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 2048, dropout: float = 0.1,
activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
layer_norm_eps: float = 1e-5, batch_first: bool = False, norm_first: bool = False,
device=None, dtype=None, zero_init=False) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super(TransformerEncoderLayer, self).__init__()
if zero_init: # Changed here
self.self_attn = ModifiedMultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first,
**factory_kwargs)
else:
self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first,
**factory_kwargs)
# Implementation of Feedforward model
self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
self.dropout = Dropout(dropout)
self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)
self.norm_first = norm_first
self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
self.dropout1 = Dropout(dropout)
self.dropout2 = Dropout(dropout)
# Legacy string support for activation function.
if isinstance(activation, str):
activation = _get_activation_fn(activation)
# We can't test self.activation in forward() in TorchScript,
# so stash some information about it instead.
if activation is F.relu or isinstance(activation, torch.nn.ReLU):
self.activation_relu_or_gelu = 1
elif activation is F.gelu or isinstance(activation, torch.nn.GELU):
self.activation_relu_or_gelu = 2
else:
self.activation_relu_or_gelu = 0
self.activation = activation
##### Source: https://github.com/pytorch/examples/tree/main/word_language_model
# data.py
import os
from io import open
import torch
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
def add_word(self, word):
if word not in self.word2idx:
self.idx2word.append(word)
self.word2idx[word] = len(self.idx2word) - 1
return self.word2idx[word]
def __len__(self):
return len(self.idx2word)
class Corpus(object):
def __init__(self, path):
self.dictionary = Dictionary()
self.train = self.tokenize(os.path.join(path, 'train.txt'))
self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
self.test = self.tokenize(os.path.join(path, 'test.txt'))
def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r', encoding="utf8") as f:
for line in f:
words = line.split() + ['<eos>']
for word in words:
self.dictionary.add_word(word)
# Tokenize file content
with open(path, 'r', encoding="utf8") as f:
idss = []
for line in f:
words = line.split() + ['<eos>']
ids = []
for word in words:
ids.append(self.dictionary.word2idx[word])
idss.append(torch.tensor(ids).type(torch.int64))
ids = torch.cat(idss)
return ids
# model.py (with slight modification to incorporate the changes above)
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
# Temporarily leave PositionalEncoding module here. Will be moved somewhere else.
class PositionalEncoding(nn.Module):
r"""Inject some information about the relative or absolute position of the tokens in the sequence.
The positional encodings have the same dimension as the embeddings, so that the two can be summed.
Here, we use sine and cosine functions of different frequencies.
.. math:
\text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
\text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
\text{where pos is the word position and i is the embed idx)
Args:
d_model: the embed dim (required).
dropout: the dropout value (default=0.1).
max_len: the max. length of the incoming sequence (default=5000).
Examples:
>>> pos_encoder = PositionalEncoding(d_model)
"""
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
r"""Inputs of forward function
Args:
x: the sequence fed to the positional encoder model (required).
Shape:
x: [sequence length, batch size, embed dim]
output: [sequence length, batch size, embed dim]
Examples:
>>> output = pos_encoder(x)
"""
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
class TransformerModel(pl.LightningModule):
"""Container module with an encoder, a recurrent or transformer module, and a decoder."""
def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.2, learning_rate=20, zero_init=False):
super(TransformerModel, self).__init__()
try:
from torch.nn import TransformerEncoder
except:
raise ImportError('TransformerEncoder module does not exist in PyTorch 1.1 or lower.')
self.learning_rate = learning_rate
self.model_type = 'Transformer'
self.src_mask = None
self.pos_encoder = PositionalEncoding(ninp, dropout)
encoder_layers = ModifiedTransformerEncoderLayer(ninp, nhead, nhid, dropout, zero_init=zero_init)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
self.encoder = nn.Embedding(ntoken, ninp)
self.ninp = ninp
self.decoder = nn.Linear(ninp, ntoken)
self.ntokens = ntoken
self.init_weights()
self.loss = nn.NLLLoss()
def _generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def init_weights(self):
initrange = 0.1
nn.init.uniform_(self.encoder.weight, -initrange, initrange)
nn.init.zeros_(self.decoder.bias)
nn.init.uniform_(self.decoder.weight, -initrange, initrange)
def forward(self, src, has_mask=True):
if has_mask:
device = src.device
if self.src_mask is None or self.src_mask.size(0) != len(src):
mask = self._generate_square_subsequent_mask(len(src)).to(device)
self.src_mask = mask
else:
self.src_mask = None
src = self.encoder(src) * math.sqrt(self.ninp)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, self.src_mask)
output = self.decoder(output)
return F.log_softmax(output, dim=-1)
def training_step(self, batch, batch_idx):
data, target = batch
data.squeeze_(0)
target.squeeze_(0)
output = self(data).view(-1, self.ntokens)
loss = self.loss(output, target)
self.log("train_loss", loss)
if not batch_idx % 965:
print(f"EPOCH {self.trainer.current_epoch} BATCH {batch_idx} LOSS {self.trainer.callback_metrics['train_loss']}")
return loss
# def validation_step(self, batch, batch_idx):
# data, target = batch
# data.squeeze_(0)
# target.squeeze_(0)
# output = self(data).view(-1, self.ntokens)
# loss = self.loss(output, target) * len(data)
# self.log("val_loss", loss)
# return loss
# def on_validation_epoch_end(self):
# print(f"VALIDATION LOSS: {self.trainer.callback_metrics['val_loss']}")
def test_step(self, batch, batch_idx):
data, target = batch
data.squeeze_(0)
target.squeeze_(0)
output = self(data).view(-1, self.ntokens)
loss = self.loss(output, target) * len(data)
self.log("test_loss", loss)
return loss
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.parameters(), lr=self.learning_rate)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [10], gamma=0.25)
return [optimizer], [scheduler]
!wget https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/train.txt
!wget https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/valid.txt
!wget https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/test.txt
!mkdir /dbfs/ml/Group_7/wikitext-2/
!mv train.txt valid.txt test.txt /dbfs/ml/Group_7/wikitext-2/
import pytorch_lightning as pl
import time
###############################################################################
# Load data
###############################################################################
# Starting from sequential data, batchify arranges the dataset into columns.
# For instance, with the alphabet as the sequence and batch size 4, we'd get
# ┌ a g m s ┐
# │ b h n t │
# │ c i o u │
# │ d j p v │
# │ e k q w │
# └ f l r x ┘.
# These columns are treated as independent by the model, which means that the
# dependence of e. g. 'g' on 'f' can not be learned, but allows more efficient
# batch processing.
class WikiDataset(torch.utils.data.Dataset):
def __init__(self, corpus_data, batch_size, bptt = 35):
self.bptt = bptt
self.data = self.batchify(corpus_data, batch_size)
def batchify(self, data, bsz):
# Work out how cleanly we can divide the dataset into bsz parts.
nbatch = data.size(0) // bsz
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, nbatch * bsz)
# Evenly divide the data across the bsz batches.
data = data.view(bsz, -1).t().contiguous()
return data
def __getitem__(self, i):
i *= self.bptt
seq_len = min(self.bptt, len(self.data) - 1 - i)
data = self.data[i:i+seq_len]
target = self.data[i+1:i+1+seq_len].view(-1)
return data, target
def __len__(self):
return (len(self.data) - 1) // self.bptt
class WikiDataModule(pl.LightningDataModule):
def __init__(self, train_batch_size, eval_batch_size = 10, bptt = 35, data_dir = "./wikitext-2/"):
super().__init__()
self.corpus = Corpus(data_dir)
self.train_data = WikiDataset(self.corpus.train, train_batch_size, bptt)
# self.val_data = WikiDataset(self.corpus.valid, eval_batch_size, bptt)
self.test_data = WikiDataset(self.corpus.test, eval_batch_size, bptt)
self.train_batch_size = train_batch_size
self.eval_batch_size = eval_batch_size
def train_dataloader(self):
return torch.utils.data.DataLoader(self.train_data, batch_size=1, num_workers=4)
# def val_dataloader(self):
# return torch.utils.data.DataLoader(self.val_data, batch_size=1, num_workers=4)
def test_dataloader(self):
return torch.utils.data.DataLoader(self.test_data, batch_size=1, num_workers=4)
###############################################################################
# Build the model
###############################################################################
###############################################################################
# Training code
###############################################################################
def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
# get_batch subdivides the source data into chunks of length args.bptt.
# If source is equal to the example output of the batchify function, with
# a bptt-limit of 2, we'd get the following two Variables for i = 0:
# ┌ a g m s ┐ ┌ b h n t ┐
# └ b h n t ┘ └ c i o u ┘
# Note that despite the name of the function, the subdivison of data is not
# done along the batch dimension (i.e. dimension 1), since that was handled
# by the batchify function. The chunks are along dimension 0, corresponding
# to the seq_len dimension in the LSTM.
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import os
import wget
def get_batch(source, i):
seq_len = min(args.bptt, len(source) - 1 - i)
data = source[i:i+seq_len]
target = source[i+1:i+1+seq_len].view(-1)
return data, target
def evaluate(data_source):
# Turn on evaluation mode which disables dropout.
model.eval()
total_loss = 0.
ntokens = len(corpus.dictionary)
with torch.no_grad():
for i in range(0, data_source.size(0) - 1, args.bptt):
data, targets = get_batch(data_source, i)
output = model(data)
output = output.view(-1, ntokens)
total_loss += len(data) * criterion(output, targets).item()
return total_loss / (len(data_source) - 1)
def train(emsize=200, nhead=2, nhid=200, nlayers=2, dropout=0.2, zero_init=False, max_epochs=20, learning_rate=20):
# Turn on training mode which enables dropout.
start_time = time.time()
if not os.path.isfile("./wikitext-2/train.txt"):
wget.download("https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/train.txt")
wget.download("https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/valid.txt")
wget.download("https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/test.txt")
os.makedirs("wikitext-2")
os.rename("train.txt", "./wikitext-2/train.txt")
os.rename("valid.txt", "./wikitext-2/valid.txt")
os.rename("test.txt", "./wikitext-2/test.txt")
dm = WikiDataModule(train_batch_size=20)
ntokens = len(dm.corpus.dictionary)
model = TransformerModel(ntokens, emsize, nhead, nhid, nlayers, dropout, learning_rate=learning_rate, zero_init=zero_init)
logger = TensorBoardLogger(save_dir=f"/dbfs/ml/Group_7/logs/transformer/{'zeroinit' if zero_init else 'default'}")
trainer = pl.Trainer(max_epochs=20,
strategy="horovod",
accelerator='gpu',
num_sanity_val_steps=0,
devices=1,
gradient_clip_val = 0.25,
callbacks=[
# EarlyStopping(monitor="val_acc", min_delta=0.00, patience=3, verbose=False, mode="max"),
# TQDMProgressBar(refresh_rate=10),
ModelCheckpoint(monitor='val_loss', mode='min'),
LearningRateMonitor(logging_interval='epoch'),
],
logger=logger,
enable_progress_bar=False
)
trainer.fit(model, dm)
if hvd.rank() == 0:
trainer.test(ckpt_path='best', datamodule=dm)
test_loss = trainer.callback_metrics["test_loss"]
with open("/dbfs/ml/Group_7/transformer_results.txt", "a") as f:
f.write(f"TEST LOSS: {test_loss} with nlayers = {nlayers} and zeroinit {zero_init}")
print(test_loss)
import horovod.torch as hvd
from sparkdl import HorovodRunner
import json
numproc = 2
assert numproc in (1,2)
hr = HorovodRunner(np=numproc, driver_log_verbosity='all')
hr.run(train, nlayers=2, zero_init=True, max_epochs=20//numproc, learning_rate=20//numproc)
install nvgpu
Smart Search in Wikipedia
Project members:
- David Mohlin, INST
- Erik Englesson, INST
- Fereidoon Zangeneh, INST
Introduction
- Swedish Wikipedia is the foruth largest language edition of Wikipedia.
- Nearly 2.600.000 articles.
- Many article titles contain similar keywords.
- A user looking for some information needs to sift through different articles to find the most relevant one.
Problem Definition
- We need a mechanism to suggest the most relevant page(s) to the user based on a query keyword.
Method
- We use PageRank [1] to gauge the importance of different articles.
- Upon entering a query keyword, the highest rated articles are shown to the user.
[1] https://en.wikipedia.org/wiki/PageRank
![]()
Input data
Input data available from wikimedia (https://dumps.wikimedia.org) corresponding to the wikipedia data corresponding to the different languages. We chose to use the swedish wikipedia as reference. Data is available as several sql databases.
pages.sql
Schema:
| page_id | page_namespace | page_title | pageisredirect |
|---|---|---|---|
| 1 | 0 | Amager | 0 |
| 2 | 0 | Afrika | 0 |
| 6 | 0 | April_30 | 1 |
pageid: A globablly unique id for pages pagenamespace: Enumerates the type of page, for example 0:Article, 1:Talk, 2:User etc pagetitle: String corresponding to both url of page and name of page. pageis_redirect: If 1 page will redirect to another page.
pagelinks.sql
Schema: | plfrom | plnamespace | pltitle | plfrom_namespace | |---------|--------------|------------|-------------------| | 494921 | 0 | 'Paris' | 0 | | 550301 | 0 | 'vänstern' | 0 |
plfrom: pageid where the link starts plnamespace: namespace where the link leads to pltitle: Title which the link leads to plfromnamespace: namespace where the link starts
redirects.sql
| rd_from | rd_namespace | rd_title | rd_interwiki | rd_fragment |
|---|---|---|---|---|
| 6 | 0 | 30_april | '' | '' |
| 48 | 0 | Agnosticism | '' | '' |
rdfrom: pageid which gets redirected rdnamespace: Nmespace of the redirection rdtitle: Title which the page redirects to rdinterwiki: If not empty the redirect goes to either wiktionary or another language of wikipedia rdfragment: if redirect goes to subsection of another article
for example here pageid 6, with title April30, redirects to 30_april
Problems
String format
Most strings are encoded in utf-8. Some older entries from ~2005 are encoded in some iso-8859 variant. For example 'ä' is encoded as the byte 0xe4 in iso-8859, utf-8 expects this to be a prefix for a 3 byte special character, where every following byte in this character start with the bits '10'. This is not the case in these databases.\ As a result the .sql file as a whole is not a valid utf-8 document. A solution is to decode it as a iso-8859 document, check if a given string is valid utf-8, then decoding it as such if that is the case
redirects
Redirects can redirect to redirected pages. To find out where a redirect chain ends one need to recursively follow the cycle. Luckily there are no cycles of redirects.
pagelinks
First off: iterwiki links were ignored, if they are to be respected many other wikimedia projects have to be included as well. Secondly: pages link to titles, not to other pages. For example links to unwritten articles are a pagelink, but there is no page with a valid page_id corresponding to this link pagelinks can have a redirected title as destination. namespaces matter: For example 'Sverige' is an entry in namespace 0 (article), 1 (talk), 10 (template), 11 (template talk), 14 (Category) and so on. These pages are not the same. For example Sverige (Article) has 240k incoming links, Sverige (Template) has 25
import json
def parse_string(data, cur):
if data[cur:cur+4] == 'NULL':
return None, cur+4
start = cur
while True:
cur = data.find('\'',cur+1)
if cur == -1:
raise Exception("end of file when trying to find end of string")
even = True
backward_i = 1
while data[cur-backward_i] == '\\':
even = not(even)
backward_i += 1
if even:
strret = data[start+1:cur]
try:
strret = str(bytes(data[start+1:cur], encoding='iso-8859-1'), encoding='utf-8')
except UnicodeDecodeError:
print(data[start+1:cur])
return strret, cur+1
def parse_int(data, cur, end_char):
if data[cur:cur+4] == 'NULL':
return None, cur+4
cur_new = data.find(end_char, cur+1)
val = int(data[cur:cur_new])
return val, cur_new
def parse_float(data, cur, end_char):
cur_new = data.find(end_char, cur+1)
val = float(data[cur:cur_new])
return val, cur_new
def parse_redirect(data, cur):
types = (int, int, str, str, str)
return parse_entry(data, cur, types)
def parse_redirects():
db_name = 'svwiki-latest-redirect.sql'
insert_start = 'INSERT INTO `redirect` VALUES '
return parse_sql(parse_redirect, db_name, insert_start)
def parse_link_col(data, cur):
types = (int, int, str, int)
return parse_entry(data, cur, types)
def parse_sql(parse_fun, db_name, insert_start):
with open(db_name, 'r', encoding='iso-8859-1') as f:
commands = f.read()
commands = commands.replace('\n', '')
ret = []
cur = 0
lc = len(commands)
while cur < lc:
print('{}%'.format(cur*100/lc))
end_of_command = commands.find(';', cur)
if commands[cur:cur+len(insert_start)] == insert_start:
cur = cur+len(insert_start)
while commands[cur] != ';':
data, cur = parse_fun(commands, cur)
ret.append(data)
if commands[cur] == ',':
cur += 1
else:
break
cur+= 1
elif end_of_command == -1:
break
else:
cur = end_of_command+1
return ret
def parse_links():
db_name = 'svwiki-latest-pagelinks.sql'
insert_start = 'INSERT INTO `pagelinks` VALUES '
return parse_sql(parse_link_col, db_name, insert_start)
def parse_entry(data, cur, types, debug=False):
ret = []
for t in types[:-1]:
if t == int:
v, cur = parse_int(data, cur+1, ',')
ret.append(v)
if t == float:
v, cur = parse_float(data, cur+1, ',')
ret.append(v)
elif t == str:
v, cur = parse_string(data, cur+1)
ret.append(v)
if debug:
print(v)
if types[-1] == int:
v, cur = parse_int(data, cur+1, ')')
ret.append(v)
elif types[-1] == float:
v, cur = parse_float(data, cur+1, ')')
ret.append(v)
elif types[-1] == str:
v, cur = parse_string(data, cur+1)
ret.append(v)
return tuple(ret), cur+1
def parse_page_col(data, cur):
types = (int, int, str, int,int, float, str, str, int, int, str, str)
try:
ret, cur = parse_entry(data, cur, types)
except Exception as e:
print(e)
print(data[cur:cur+1000])
ret, cur = parse_entry(data, cur, types, debug=True)
raise e
return ret, cur
def parse_pages():
db_name = 'svwiki-latest-page.sql'
insert_start = 'INSERT INTO `page` VALUES '
return parse_sql(parse_page_col, db_name, insert_start)
def create_page_to_idx_mapping(pages):
values = [(x[0], x[1]) for x in pages] # id, namespace
values = sorted(values,key=lambda x: (x[1], x[0]))
mapping = {values[i]:i for i in range(len(values))}
return mapping
def create_namespace_title_to_page_mapping(pages, redirects):
plain_name_to_id = {}
for p in pages:
plain_name_to_id[(p[1], p[2])] = p[0]
redirect_map = {}
for r in redirects:
if len(r[3]):
continue # redirects to other wiki, such as other language or wiktionary
try:
dst_page = plain_name_to_id[(r[1], r[2])]
redirect_map[r[0]] = dst_page
except KeyError:
pass
redirect_recursive_removed = {}
for k,v in redirect_map.items():
while v in redirect_map:
v = redirect_map[v]
redirect_recursive_removed[k] = v
title_to_page = {}
page_to_namespace_title = {}
for p in pages:
if p[0] in redirect_recursive_removed:
title_to_page[(p[1], p[2])] = redirect_recursive_removed[p[0]]
else:
page_to_namespace_title[p[0]] = [p[1], p[2]]
title_to_page[(p[1], p[2])] = p[0]
return title_to_page, page_to_namespace_title, redirect_recursive_removed
def check_uniqness(pages):
all_guids = {}
for v in pages:
if v[0] in all_guids:
print(all_guids[v[0]])
print(v)
all_guids[v[0]] = v
def create_link_mapping(pages, links, namespace_title2page, redirect_map, fallback=2):
retdict = {}
for p in pages:
if p[0] in redirect_map:
continue
retdict[p[0]] = set()
for l in links:
if l[0] in redirect_map:
page_id = redirect_map[l[0]]
else:
page_id = l[0]
try:
linklist = retdict[page_id]
except KeyError as e:
print('key error {}'.format(l[0]))
print(l[0] in redirect_map)
continue
try:
res = namespace_title2page[(l[1], l[2])]
linklist.add(res)
except Exception as e:
if fallback == 1:
print('could not resolve')
print(l)
elif fallback == 2:
pass
else:
raise e
retdict_list = {}
for k, v in retdict.items():
retdict_list[k] = list(v)
return retdict_list
def main():
redirects = parse_redirects()
pages = parse_pages()
print('create nt2p')
namespace_title2page, page_to_namespace_title, redirection_map = create_namespace_title_to_page_mapping(pages, redirects)
with open('page_to_namespace_title.json', 'w') as f:
json.dump(page_to_namespace_title, f)
with open('page_to_namespace_title.txt', 'w') as f:
for key, val in data.items():
f.write(f"{key}:{','.join([str(i) for i in val])}\n")
exit(0)
name_to_page = {}
for k, v in namespace_title2page.items():
name_to_page[k[1]] = v
with open('name_to_page.json', 'w') as f:
json.dump(name_to_page, f)
with open('name_to_page.txt', 'w') as f:
for key, val in tqdm(data.items()):
f.write(f"{key}:{val}\n")
links = parse_links()
link_mapping = create_link_mapping(pages, links, namespace_title2page, redirection_map)
print('I will save now')
with open('link_mapping.json', 'w') as f:
json.dump(link_mapping, f)
with open('link_mapping.txt', 'w') as f:
for key, val in tqdm(data.items()):
f.write(f"{key}:{','.join([str(i) for i in val])}\n")
print('link mapping')
if __name__ == '__main__':
main()
println("hej!")
// toy graph
// 4 nodes
// 1->2, 2->4, 4->1 3->2
// d = 0.1
// N = 4
// A = 0,0,0,1,
// 1,0,1,0
// 0,0,0,0
// 0,1,0,0
// B = d/N + (1-d)*A
// solution:
// 0.56291896, 0.5909094, 0.B =, 0.5761776
hej!
SETUP DATA
# import json
# import os
# with open('/dbfs/FileStore/tables/link_mapping.json', 'r') as f:
# links = json.load(f)
dummy_df = spark.read.option("multiline","true").json('/FileStore/tables/vis_camera.json') # test json by Fereidoon
#df = spark.read.option("multiline", "true").json('/FileStore/tables/link_mapping.json') #
#rddfile = sc.textFile('/FileStore/tables/link_mapping.json').map(lambda x: json.loads(x))
#rddfile.take(5)
rdd = sc.textFile('/FileStore/tables/link_mapping.txt').cache()
def convert_string_to_int(string):
try:
v = int(string)
return v
except ValueError:
return -1
id_to_outgoing_rdd = rdd.map(lambda x: (int(x.split(":")[0]), [convert_string_to_int(v) for v in x.split(":")[1].split(",") if convert_string_to_int(v) != -1])).filter(lambda x: len(x[1])>0).cache()
#id_to_outgoing_rdd = rdd.map(lambda x: (int(x.split(":")[0]), [convert_string_to_int(v) for v in x.split(":")[1].split(",") if convert_string_to_int(v) != -1])).cache()
print(id_to_outgoing_rdd.count())
#id_to_outgoing_rdd.take(1)
from pyspark.sql.types import StructField, StructType, IntegerType, StringType, ArrayType
schema = StructType([
StructField('from_id', IntegerType(), False),
# StructField('to_ids', StringType(), False),
# StructField('to_ids', StructType([
# StructField('to_id', StringType(), False)
# ]), False)
StructField('to_ids', ArrayType(IntegerType()))
])
df = spark.read.schema(schema).json('/FileStore/tables/link_mapping.json')
df.show(n=2)
rdd = sc.textFile('/FileStore/tables/link_mapping.json')
rddfile = rdd.map(lambda x: json.loads(x))
rddfile.map(lambda x: type(x)).take(1)
links = {int(k): list(map(int, v)) for k, v in links.items()}
#rddfile = spark.read.option("multiline","true").json('/FileStore/tables/link_mapping.json').rdd.repartition(10)
#rddfile.take(1)
#rddfile = spark.read.option("multiline","true").json('/FileStore/tables/vis_camera.json').rdd.repartition(10)
#rddfile.take(1)
rdd = sc.textFile('/FileStore/tables/link_mapping.json', minPartitions=8)
rdd.take(1)
import numpy as np
A = np.array([[0, 0, 0, 0, 1],
[0.5, 0, 0, 0, 0],
[0.5, 0, 0, 0, 0],
[0, 1, 0.5, 0, 0],
[0, 0, 0.5, 1, 0]])
d = 0.15
N = 5
v1 = np.ones(N) / float(5)
B = ((1-d) * A + d/float(5))
for i in range(10):
v1 = B @ v1
print("Desired result:", v1)
import numpy as np
#id_to_outgoing = {4: [0], 0: [1, 2], 1: [3], 2: [3, 4], 3: [4]}
id_to_outgoing = {}
nodes = 400
edges_per_node = 30
for i in range(nodes):
id_to_outgoing[i] = list(np.unique(np.random.randint(nodes, size=(edges_per_node))))
from operator import add
num_partitions = 10
#keys = sc.parallelize(id_to_outgoing, num_partitions)
#N = len(id_to_outgoing)
N = id_to_outgoing_rdd.keys().count()
print("N:", N)
d = 0.15
#id_to_outgoing_rdd = keys.map(lambda x: (x, id_to_outgoing[x]))
#print(keys.take(10))
#print(id_to_outgoing_rdd.take(10))
#v2 = keys.map(lambda x: (x, 1.0 / float(N)))
v2 = id_to_outgoing_rdd.keys().map(lambda x: (x, 1.0 / float(N))).cache()
def map_fn(to_from_rank):
node_from, (node_to_list, rank) = to_from_rank
value = rank / len(node_to_list)
#value = rank / (N-1) if len(node_to_list) == 0 else rank / len(node_to_list) # or is it value = 0 as we get + d/float(N) in the last mapValues in the loop?
return [(node_to, value) for node_to in node_to_list]
id_to_outgoing_rdd = id_to_outgoing_rdd.cache()
print("num_partitions:", num_partitions)
for i in range(30):
v2 = id_to_outgoing_rdd.join(v2, num_partitions).flatMap(map_fn).reduceByKey(add).mapValues(lambda x: x * (1-d) + d / float(N))
if N > 100:
print(v2.take(1))
else:
print(v2.collect())
#print(v2.collect())
#print(v2.getNumPartitions())
rankings = v2.collect()
for i in range(10):
print(rankings[i])
with open('/dbfs/FileStore/tables/page_to_rank.txt', 'w+') as f:
for id, rank in rankings:
f.write(f'{id}:{rank}\n')
println("hej2")
//id_to_outgoing = Map((4, Array(0)), (0, Array(1, 2)), (1, Array(3)), (2, Array(3, 4)), (3, Array(4)))}
When reading the saved resutls there are some corrupted cases where some keys miss any values. We define a function to handle such cases by putting a -1 instead of the expected integer.
def convert_string_to_int(string: str) -> int:
"""Convert a string to integer. Return -1 if input is invalid.
Args:
string: Input string.
Returns:
Input string case as integer.
"""
try:
v = int(string)
return v
except ValueError:
return -1
We reading the mapping of the page links, of which pages a query page links to.
link_mapping = sc.textFile('/FileStore/tables/link_mapping.txt').cache()
link_mapping = link_mapping.map(lambda x: (int(x.split(":")[0]), [convert_string_to_int(v) for v in x.split(":")[1].split(",") if convert_string_to_int(v) != -1])).filter(lambda x: len(x[1])>0).cache()
# (from_page: int, [to_page: int, ...])
We read the mapping of page rankings, of what importance ranking a query page has.
page_to_rank = sc.textFile('/FileStore/tables/page_to_rank.txt')
page_to_rank = page_to_rank.map(lambda x: (int(x.split(":")[0]), float(x.split(":")[1]))).cache()
# (page: int, rank: float)
We read the mapping of page namespaces and titles, of what namespace and title a query page has.
page_to_namespace_title = sc.textFile('/FileStore/tables/page_to_namespace_title.txt')
page_to_namespace_title = page_to_namespace_title.map(lambda x: (int(x.split(":")[0]),
int(x.split(":")[1].split(",")[0]), x.split(":")[1].split(",")[1]
)).cache()
# (page: int, namespace: int, title: str)
We define function that returns the page IDs, whose title matches the query keywords.
from pyspark.rdd import PipelinedRDD
def find_pages(
page_to_namespace_title: PipelinedRDD,
query: str,
search_method: str = "relaxed"
) -> PipelinedRDD:
"""Find the pages in an RRD, whose title matches a query keyword.
Args:
page_to_namespace_title:
RDD that contains 3-tuples of page ID (int), namespace (int), title (str).
query: Query string.
search_method:
Flag for how the search is performed; "literal" finds pages with exact title as the query,
"relaxed" finds pages, whose title contains the query phrase. Both cases are case insensitive.
Returns:
The RDD of page IDs that match the query phrase.
"""
if search_method not in ("literal", "relaxed"):
raise NotImplementedError("Only 'literal' and 'relaxed' search methods are implemented")
if search_method == "literal":
return page_to_namespace_title.filter(lambda x: query.lower() == x[2].lower()).map(lambda x: x[0])
if search_method == "relaxed":
return page_to_namespace_title.filter(lambda x: query.lower() in x[2].lower()).map(lambda x: x[0])
We find the related pages for a given query.
matched_pages = find_pages(page_to_namespace_title, "laser", "relaxed").collect()
print(f"Found {len(matched_pages)} matching articles.")
We find the top n pages according to their PageRank importance for the given query.
n = min(5, len(matched_pages))
top_n_pages = page_to_rank.filter(lambda x: x[0] in matched_pages).takeOrdered(n, key=lambda x: -x[1])
top_n_pages = [v[0] for v in top_n_pages]
We print the titles and the namespaces of the top n pages.
top_n_namespaces_and_titles = page_to_namespace_title.filter(lambda x: x[0] in top_n_pages).map(lambda x: (x[1], x[2])).collect()
print(f"Top {n} search results are:")
for i, (namespace, title) in enumerate(top_n_namespaces_and_titles):
print(f"{i + 1}. {title} in namespace {namespace}")
Distributed Ensembles for 3D Human Pose Estimation
Project members:
- Xixi Liu, Chalmers University of Technology
- Yaroslava Lochman, Chalmers University of Technology
- Hampus Gummesson Svensson, Chalmers University of Technology
- Erik Wallin, Chalmers University of Technology
Introduction
Our task is to perform 3D human pose estimation in video, where the objective is to predict 3D positions of keypoints from 2D positions in video sequences. This is done in a setting where both labelled and unlabelled data is available. In paricular, the size of unlabled data is larger than labeled data. For this task, we create a distributed ensembles of temporal convolutional networks for semi-supervised learning. The temporal convolutional network developed by Pavllo, Dario, et al. [1] is employed.
Semi-supervised learning
Anotating data is not only costly but time-consumping, implying that the amount of labeled data is limited in many practical applications. Given a dataset consisting of both unlabeled and labeled instances, semi-supervised learning aims to use both the labeled data and unlabeled data to improve the model performance, compared to just using labeled instances. Specifically, the models is first trained by labebled data. Then the trained model is further utilized to predict pseudo-labels for the unlabeled data, which is incoporated into the training data for further training.
Semi-supervised learning with pseudolabels using an ensemble
One way to obtain pseudo-labels in semi-supervised learning is to use the prediction from an ensemble of supervised models. Specifically, each model is trained independetly on all or a part of the available labeled data. After training, each model makes predictions for the unlabeled instances. The predictions on each instance is then aggregated to provide so called pseudolabels that can be used for supervised training, either training a single model or again an ensemble of models to predict labels of unseen data. There are several ways to aggragate the predictions from each model. We use the mean of predictions from each model as the pseudo-label.
3D Human Pose Estimation
3D human pose estimation involves predicting 3D locations of keypoints,e.g., head, hands and elbows, given 2D locations of these keypoints. 2D locations can be given by a video sequence, while the 3D locations are often provided by a motion capture system. For instance, in the sequence below, we see 2D locations of keypoints to the left and the 3D pose to the right. Provided the 2D locations of keypoints to the left, the task is to predict the true 3D locations to the right, which then can provide the full 3D pose (as seen to the right). 
Our contributions
In this work, we train a distributed ensemble on labeled data for 3D human pose estimation in video. We use the HumanEva-I dataset [2] for training and evaluation, where the training data is split up into labeled and unlabeled data. The distributed ensemble is to compute pseudovalues of unlabeled data. We aggregate the pseudovalues of each ensmeble to one single value, that is used for training on labeled and unlabeled data. We incrementally increase the amount of unlabeled data with pseudovalues that is used for training.
References
[1] Pavllo, Dario, et al. "3d human pose estimation in video with temporal convolutions and semi-supervised training." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
[2] Sigal, Leonid, Alexandru O. Balan, and Michael J. Black. "Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion." International journal of computer vision 87.1 (2010): 4-27.
The HumanEva-I (or simply HumanEva) dataset is used to evaluate our distributed ensemble models. HumanEva-I consists of 4 subjects performing a set of 6 pre-defined actions including Walk, Jog, Throw/catch, Gesture, Box, and Combo. Ground-truth 3D motions are captured using a commercial motion capture system provided by ViconPeack. Videos data is recorded via using two commerical video capture systems. Originally, there are 56 sequences and approximately 80000 frames in total. 15-joint sekeleton is adopted, giving 15 keypoints, and the provided train/test split is used. Our approach of distributed ensembles can be applied on other datasets for 3D pose estimation, where locations of 2D and 3D are provided. * frame visualization should be added here.
HumanEva-I is pre-processed using Matlab and stored in the .CSV format, which Apache Spark supports. The train and test data are loaded as DataFrames. The columns consists of frame index, subject index, action name, camera index, and 2D and 3D positions of each joint (keypoint). For brevity, the subject indices, action names and camera indices are grouped together to a single column.
The data and model checkpoints are saved under dbfs:/VideoPose3D.
ls dbfs:/VideoPose3D
| path | name | size |
|---|---|---|
| dbfs:/VideoPose3D/checkpoint/ | checkpoint/ | 0.0 |
| dbfs:/VideoPose3D/h36m/ | h36m/ | 0.0 |
| dbfs:/VideoPose3D/humaneva/ | humaneva/ | 0.0 |
| dbfs:/VideoPose3D/saved_models/ | saved_models/ | 0.0 |
First we load the two .npz files with the 2D and 3D keypoints of the HumanEva dataset, respectively.
pip install pip --upgrade --quiet
pip install gdown --quiet
cd /dbfs/VideoPose3D/humaneva
gdown 1EngBymOjXWPntjfNVaGZhBX7sNCNg9pu # data_2d_humaneva15_gt.npz
gdown 1ErTRudqF8ugAwopL3ieral0YMEtE28Dd # data_3d_humaneva15.npz
data_2d_humaneva15_gt.npzcontainspos2dwith 2D keypoint locations of the joints of moving humans in various video sequences. The format is as following:- it is a dictionary with keys corresponding to different subjects:
S1,S2, andS3 - since it was pre-split into train-validation data by the dataset authors, the keys we see are
Train/S1...Valiation/S1..., however we ignore that split - each subject contains another dictionary with keys corresponding to different actions:
Jog,Box,Walking,Gestures,ThrowCatch. - again, since it was pre-split, instead of the full videos we get the chunks of videos,
Jog chunk0... - for each video, we have three views (
cameracan be0,1, or2), since three cameras were looking at the moving subjects during data collection
- it is a dictionary with keys corresponding to different subjects:
data_3d_humaneva15.npzcontainspos3dwhich has the same structure aspos2d, but instead of the 2D keypoint locations, it contains the ground-truth 3D keypoint locations, and also it doesn't have three different views.
We transform the .npz files into the .csv files that will be used further when working with RDDs. We split the data into train and test subsets for convenience and make sure that both contain a reasonable portion of data for each subject and each action.
import numpy as np
import pandas as pd
ROOTDIR = '/dbfs/VideoPose3D'
path2d = f'{ROOTDIR}/humaneva/data_2d_humaneva15_gt.npz'
path3d = f'{ROOTDIR}/humaneva/data_3d_humaneva15.npz'
pos2d = np.load(path2d, allow_pickle=True)['positions_2d'].item()
pos3d = np.load(path3d, allow_pickle=True)['positions_3d'].item()
pos_data = []
pos_data_train = []
pos_data_test = []
assert(pos2d.keys() == pos3d.keys())
for subject in pos2d.keys():
print(f'{subject}: {sum([pos2d[subject][action][0].shape[0] for action in pos2d[subject].keys()])} frames in total')
assert(pos2d[subject].keys() == pos3d[subject].keys())
print(list(pos2d[subject].keys()))
# Train-Test split
actions_for_test = [[a for a in pos2d[subject].keys() if action_name in a] for action_name in ['Jog', 'Box', 'Walking', 'Gestures', 'ThrowCatch']]
actions_for_test = [a[1] for a in actions_for_test if len(a)>1]
# Add to full data
for action in pos2d[subject].keys():
for camera in [0,1,2]:
n_frames = pos2d[subject][action][camera].shape[0]
assert(n_frames==pos3d[subject][action].shape[0])
frames = np.hstack([
pos2d[subject][action][camera].reshape(n_frames,-1),
pos3d[subject][action].reshape(n_frames,-1)])
row = [[subject, action, camera, *frame] for frame in frames]
pos_data.extend(row)
# Add to train data
for action in set(pos2d[subject].keys()) - set(actions_for_test):
for camera in [0,1,2]:
n_frames = pos2d[subject][action][camera].shape[0]
assert(n_frames==pos3d[subject][action].shape[0])
frames = np.hstack([
pos2d[subject][action][camera].reshape(n_frames,-1),
pos3d[subject][action].reshape(n_frames,-1)])
row = [[subject, action, camera, *frame] for frame in frames]
pos_data_train.extend(row)
# Add to test data
for action in actions_for_test:
for camera in [0,1,2]:
n_frames = pos2d[subject][action][camera].shape[0]
assert(n_frames==pos3d[subject][action].shape[0])
frames = np.hstack([
pos2d[subject][action][camera].reshape(n_frames,-1),
pos3d[subject][action].reshape(n_frames,-1)])
row = [[subject, action, camera, *frame] for frame in frames]
pos_data_test.extend(row)
print('Creating full dataframe...')
pos_df = pd.DataFrame(pos_data, columns=['Subject','Action','Camera'] + (','.join([f'x{i+1},y{i+1}' for i in range(15)])).split(',') + (','.join([f'X{i+1},Y{i+1},Z{i+1}' for i in range(15)])).split(','))
print('Creating train dataframe...')
pos_df_train = pd.DataFrame(pos_data_train, columns=['Subject','Action','Camera'] + (','.join([f'x{i+1},y{i+1}' for i in range(15)])).split(',') + (','.join([f'X{i+1},Y{i+1},Z{i+1}' for i in range(15)])).split(','))
print('Creating test dataframe...')
pos_df_test = pd.DataFrame(pos_data_test, columns=['Subject','Action','Camera'] + (','.join([f'x{i+1},y{i+1}' for i in range(15)])).split(',') + (','.join([f'X{i+1},Y{i+1},Z{i+1}' for i in range(15)])).split(','))
SAVE = False
if SAVE:
pos_df.to_csv(f'{ROOTDIR}/humaneva/humaneva15.csv')
pos_df_train.to_csv(f'{ROOTDIR}/humaneva/humaneva15_train.csv')
pos_df_test.to_csv(f'{ROOTDIR}/humaneva/humaneva15_test.csv')
print('Done.')
We also experimented with 2D keypoint detections produced by Mask-RCNN, which we load in the cell below.
cd /dbfs/VideoPose3D/humaneva/
wget https://dl.fbaipublicfiles.com/video-pose-3d/data_2d_humaneva15_detectron_pt_coco.npz
We pre-save the skeleton data for HumanEva dataset.
humaneva_skeleton = {
'parents': [-1, 0, 1, 2, 3, 1, 5, 6, 0, 8, 9, 0, 11, 12, 1],
'joints_left': [2, 3, 4, 8, 9, 10],
'joints_right': [5, 6, 7, 11, 12, 13],
}
np.savez(f'{ROOTDIR}/humaneva/humaneva_skeleton.npz', data=humaneva_skeleton)
Let's plot the first 1000 frames of the video sequence in our dataset to understand, what kind of input the neural network will expect (more details on that in the next noteook).
from IPython.display import HTML
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
desc_list = []
pos2d_list = []
for row in pos_df.iterrows():
if row[1].values[2]==0:
desc_list.append(': '.join(row[1].values[:2]) + f' (Cam {row[1].values[2]})')
pos2d_list.append(np.array(row[1].values[3:3+15*2]).reshape(15,2))
pos2d_list = np.array(pos2d_list)
figure, ax = plt.subplots()
xmin, xmax = pos2d_list[:,:,0].min(), pos2d_list[:,:,0].max()
ymin, ymax = pos2d_list[:,:,1].min(), pos2d_list[:,:,1].max()
def animation_function(i):
ax.clear()
# Setting title as subject + action + camera
ax.set_title(desc_list[i])
# Setting limits for x and y axis
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.invert_yaxis()
# Plotting the 2D keypoints
indices = humaneva_skeleton['joints_left']
x = pos2d_list[i,indices,0]
y = pos2d_list[i,indices,1]
plt.scatter(x, y)
animation = FuncAnimation(figure, animation_function, interval=10, frames=10)
HTML(animation.to_jshtml());
humaneva_cameras_intrinsic_params = [
{
'id': 'C1',
'res_w': 640,
'res_h': 480,
'azimuth': 0, # Only used for visualization
},
{
'id': 'C2',
'res_w': 640,
'res_h': 480,
'azimuth': -90, # Only used for visualization
},
{
'id': 'C3',
'res_w': 640,
'res_h': 480,
'azimuth': 90, # Only used for visualization
},
]
np.savez(f'{ROOTDIR}/humaneva/humaneva_cameras_intrinsic_params.npz', data=humaneva_cameras_intrinsic_params)
humaneva_cameras_extrinsic_params = {
'S1': [
{
'orientation': [0.424207, -0.4983646, -0.5802981, 0.4847012],
'translation': [4062.227, 663.2477, 1528.397],
},
{
'orientation': [0.6503354, -0.7481602, -0.0919284, 0.0941766],
'translation': [844.8131, -3805.2092, 1504.9929],
},
{
'orientation': [0.0664734, -0.0690535, 0.7416416, -0.6639132],
'translation': [-797.67377, 3916.3174, 1433.6602],
},
],
'S2': [
{
'orientation': [ 0.4214752, -0.4961493, -0.5838273, 0.4851187 ],
'translation': [ 4112.9121, 626.4929, 1545.2988],
},
{
'orientation': [ 0.6501393, -0.7476588, -0.0954617, 0.0959808 ],
'translation': [ 923.5740, -3877.9243, 1504.5518],
},
{
'orientation': [ 0.0699353, -0.0712403, 0.7421637, -0.662742 ],
'translation': [ -781.4915, 3838.8853, 1444.9929],
},
],
'S3': [
{
'orientation': [ 0.424207, -0.4983646, -0.5802981, 0.4847012 ],
'translation': [ 4062.2271, 663.2477, 1528.3970],
},
{
'orientation': [ 0.6503354, -0.7481602, -0.0919284, 0.0941766 ],
'translation': [ 844.8131, -3805.2092, 1504.9929],
},
{
'orientation': [ 0.0664734, -0.0690535, 0.7416416, -0.6639132 ],
'translation': [ -797.6738, 3916.3174, 1433.6602],
},
],
'S4': [
{},
{},
{},
],
}
np.savez(f'{ROOTDIR}/humaneva/humaneva_cameras_extrinsic_params.npz', data=humaneva_cameras_extrinsic_params)
Human36m Dataset (not used)
h36m_cameras_intrinsic_params = [
{
'id': '54138969',
'center': [512.54150390625, 515.4514770507812],
'focal_length': [1145.0494384765625, 1143.7811279296875],
'radial_distortion': [-0.20709891617298126, 0.24777518212795258, -0.0030751503072679043],
'tangential_distortion': [-0.0009756988729350269, -0.00142447161488235],
'res_w': 1000,
'res_h': 1002,
'azimuth': 70, # Only used for visualization
},
{
'id': '55011271',
'center': [508.8486328125, 508.0649108886719],
'focal_length': [1149.6756591796875, 1147.5916748046875],
'radial_distortion': [-0.1942136287689209, 0.2404085397720337, 0.006819975562393665],
'tangential_distortion': [-0.0016190266469493508, -0.0027408944442868233],
'res_w': 1000,
'res_h': 1000,
'azimuth': -70, # Only used for visualization
},
{
'id': '58860488',
'center': [519.8158569335938, 501.40264892578125],
'focal_length': [1149.1407470703125, 1148.7989501953125],
'radial_distortion': [-0.2083381861448288, 0.25548800826072693, -0.0024604974314570427],
'tangential_distortion': [0.0014843869721516967, -0.0007599993259645998],
'res_w': 1000,
'res_h': 1000,
'azimuth': 110, # Only used for visualization
},
{
'id': '60457274',
'center': [514.9682006835938, 501.88201904296875],
'focal_length': [1145.5113525390625, 1144.77392578125],
'radial_distortion': [-0.198384091258049, 0.21832367777824402, -0.008947807364165783],
'tangential_distortion': [-0.0005872055771760643, -0.0018133620033040643],
'res_w': 1000,
'res_h': 1002,
'azimuth': -110, # Only used for visualization
},
]
np.savez(f'{ROOTDIR}/h36m/h36m_cameras_intrinsic_params.npz', data=h36m_cameras_intrinsic_params)
h36m_cameras_extrinsic_params = {
'S1': [
{
'orientation': [0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088],
'translation': [1841.1070556640625, 4955.28466796875, 1563.4454345703125],
},
{
'orientation': [0.6157187819480896, -0.764836311340332, -0.14833825826644897, 0.11794740706682205],
'translation': [1761.278564453125, -5078.0068359375, 1606.2650146484375],
},
{
'orientation': [0.14651472866535187, -0.14647851884365082, 0.7653023600578308, -0.6094175577163696],
'translation': [-1846.7777099609375, 5215.04638671875, 1491.972412109375],
},
{
'orientation': [0.5834008455276489, -0.7853162288665771, 0.14548823237419128, -0.14749594032764435],
'translation': [-1794.7896728515625, -3722.698974609375, 1574.8927001953125],
},
],
'S2': [
{},
{},
{},
{},
],
'S3': [
{},
{},
{},
{},
],
'S4': [
{},
{},
{},
{},
],
'S5': [
{
'orientation': [0.1467377245426178, -0.162370964884758, -0.7551892995834351, 0.6178938746452332],
'translation': [2097.3916015625, 4880.94482421875, 1605.732421875],
},
{
'orientation': [0.6159758567810059, -0.7626792192459106, -0.15728192031383514, 0.1189815029501915],
'translation': [2031.7008056640625, -5167.93310546875, 1612.923095703125],
},
{
'orientation': [0.14291371405124664, -0.12907841801643372, 0.7678384780883789, -0.6110143065452576],
'translation': [-1620.5948486328125, 5171.65869140625, 1496.43701171875],
},
{
'orientation': [0.5920479893684387, -0.7814217805862427, 0.1274748593568802, -0.15036417543888092],
'translation': [-1637.1737060546875, -3867.3173828125, 1547.033203125],
},
],
'S6': [
{
'orientation': [0.1337897777557373, -0.15692396461963654, -0.7571090459823608, 0.6198879480361938],
'translation': [1935.4517822265625, 4950.24560546875, 1618.0838623046875],
},
{
'orientation': [0.6147197484970093, -0.7628812789916992, -0.16174767911434174, 0.11819244921207428],
'translation': [1969.803955078125, -5128.73876953125, 1632.77880859375],
},
{
'orientation': [0.1529948115348816, -0.13529130816459656, 0.7646096348762512, -0.6112781167030334],
'translation': [-1769.596435546875, 5185.361328125, 1476.993408203125],
},
{
'orientation': [0.5916101336479187, -0.7804774045944214, 0.12832270562648773, -0.1561593860387802],
'translation': [-1721.668701171875, -3884.13134765625, 1540.4879150390625],
},
],
'S7': [
{
'orientation': [0.1435241848230362, -0.1631336808204651, -0.7548328638076782, 0.6188824772834778],
'translation': [1974.512939453125, 4926.3544921875, 1597.8326416015625],
},
{
'orientation': [0.6141672730445862, -0.7638262510299683, -0.1596645563840866, 0.1177929937839508],
'translation': [1937.0584716796875, -5119.7900390625, 1631.5665283203125],
},
{
'orientation': [0.14550060033798218, -0.12874816358089447, 0.7660516500473022, -0.6127139329910278],
'translation': [-1741.8111572265625, 5208.24951171875, 1464.8245849609375],
},
{
'orientation': [0.5912848114967346, -0.7821764349937439, 0.12445473670959473, -0.15196487307548523],
'translation': [-1734.7105712890625, -3832.42138671875, 1548.5830078125],
},
],
'S8': [
{
'orientation': [0.14110587537288666, -0.15589867532253265, -0.7561917304992676, 0.619644045829773],
'translation': [2150.65185546875, 4896.1611328125, 1611.9046630859375],
},
{
'orientation': [0.6169601678848267, -0.7647668123245239, -0.14846350252628326, 0.11158157885074615],
'translation': [2219.965576171875, -5148.453125, 1613.0440673828125],
},
{
'orientation': [0.1471444070339203, -0.13377119600772858, 0.7670128345489502, -0.6100369691848755],
'translation': [-1571.2215576171875, 5137.0185546875, 1498.1761474609375],
},
{
'orientation': [0.5927824378013611, -0.7825870513916016, 0.12147816270589828, -0.14631995558738708],
'translation': [-1476.913330078125, -3896.7412109375, 1547.97216796875],
},
],
'S9': [
{
'orientation': [0.15540587902069092, -0.15548215806484222, -0.7532095313072205, 0.6199594736099243],
'translation': [2044.45849609375, 4935.1171875, 1481.2275390625],
},
{
'orientation': [0.618784487247467, -0.7634735107421875, -0.14132238924503326, 0.11933968216180801],
'translation': [1990.959716796875, -5123.810546875, 1568.8048095703125],
},
{
'orientation': [0.13357827067375183, -0.1367100477218628, 0.7689454555511475, -0.6100738644599915],
'translation': [-1670.9921875, 5211.98583984375, 1528.387939453125],
},
{
'orientation': [0.5879399180412292, -0.7823407053947449, 0.1427614390850067, -0.14794869720935822],
'translation': [-1696.04345703125, -3827.099853515625, 1591.4127197265625],
},
],
'S11': [
{
'orientation': [0.15232472121715546, -0.15442320704460144, -0.7547563314437866, 0.6191070079803467],
'translation': [2098.440185546875, 4926.5546875, 1500.278564453125],
},
{
'orientation': [0.6189449429512024, -0.7600917220115662, -0.15300633013248444, 0.1255258321762085],
'translation': [2083.182373046875, -4912.1728515625, 1561.07861328125],
},
{
'orientation': [0.14943228662014008, -0.15650227665901184, 0.7681233882904053, -0.6026304364204407],
'translation': [-1609.8153076171875, 5177.3359375, 1537.896728515625],
},
{
'orientation': [0.5894251465797424, -0.7818877100944519, 0.13991211354732513, -0.14715361595153809],
'translation': [-1590.738037109375, -3854.1689453125, 1578.017578125],
},
],
}
np.savez('./data/h36m/h36m_cameras_extrinsic_params.npz', data=h36m_cameras_extrinsic_params)
h36m_skeleton = {
'parents': [-1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12, 13, 14, 12, 16, 17, 18, 19, 20, 19, 22, 12, 24, 25, 26, 27, 28, 27, 30],
'joints_left': [6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23],
'joints_right': [1, 2, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31],
}
np.savez('./data/h36m/h36m_skeleton.npz', data=h36m_skeleton)
#cd /dbfs/VideoPose3D
#mkdir checkpoint
#cd checkpoint
#wget https://dl.fbaipublicfiles.com/video-pose-3d/pretrained_h36m_cpn.bin
#wget https://dl.fbaipublicfiles.com/video-pose-3d/pretrained_humaneva15_detectron.bin
Training Distributed Ensembles for 3D Human Pose Estimation
Here we provide the necessary code for creating and training a distributed ensemble of temporal convolutional network for semi-supervised learning with pseudolabels.
import numpy as np
import torch
import pyspark.sql.functions as F
from pyspark.sql import Window
from pyspark.sql.functions import collect_list, size, udf
from pyspark.ml.feature import VectorAssembler
from pyspark.sql.types import BooleanType
from pyspark.sql.functions import udf
from itertools import groupby
from pyspark.rdd import PipelinedRDD
from pathlib import Path
import os
import matplotlib.pyplot as plt
from pyspark.sql.types import StructType, StringType, DoubleType, IntegerType
humaneva_train_path = "/VideoPose3D/humaneva/humaneva15_train.csv"
humaneva_test_path = "/VideoPose3D/humaneva/humaneva15_test.csv"
def load_data_from_csv(file_location):
"""Load and preprocess HumanEva data
Args:
file_location: file location from which to load the data
Returns:
df: spark DataFrame
"""
file_type = "csv"
infer_schema = "true"
first_row_is_header = False
delimiter = ","
schema = StructType() \
.add("Idx",IntegerType(),True) \
.add("Subject",StringType(),True) \
.add("Action",StringType(),True) \
.add("Camera",StringType(),True)
for i in range(15):
schema = schema.add(f"u{i}",DoubleType(),True).add(f"v{i}",DoubleType(),True)
for i in range(15):
schema = schema.add(f"X{i}",DoubleType(),True).add(f"Y{i}",DoubleType(),True).add(f"Z{i}",DoubleType(),True)
# Load the data from file
df = spark.read.csv(file_location, header=True, schema=schema, sep=',')
return df
df_train = load_data_from_csv(humaneva_train_path).withColumn("Group", F.concat_ws(', ', "Subject", "Action", "Camera")).drop("Subject", "Action", "Camera")
df_test = load_data_from_csv(humaneva_test_path).withColumn("Group", F.concat_ws(', ', "Subject", "Action", "Camera")).drop("Subject", "Action", "Camera")
Visualization of train DataFrame. X, Y and Z represents the coordinate of the 3D locations; while u and v represent the coordinates of the 2D locations.
display(df_train)
Assemble feature and target columns
We us VectorAssembler to transoform the 2D (feature) and 3d (target) locations to vectors.
feature_names = []
target_names = []
n_keypoints = 15
for i in range(n_keypoints):
feature_names.append("u{}".format(i))
feature_names.append("v{}".format(i))
target_names.append("X{}".format(i))
target_names.append("Y{}".format(i))
target_names.append("Z{}".format(i))
# feature corresponds to the 2D positions.
# target corresponds to the 3D positions.
feature_assembler = VectorAssembler(inputCols=feature_names, outputCol="features") # merge u and v into a vector column.
target_assembler = VectorAssembler(inputCols=target_names, outputCol="targets")
def assemble_vectors(df):
df = feature_assembler.transform(df)
df = target_assembler.transform(df)
df = df.drop(*feature_names).drop(*target_names)
return df
df_train = assemble_vectors(df_train)
df_test = assemble_vectors(df_test)
Create receptive fields
The employed temporal convolutional network uses the temporal information, which means the 3D pose prediction of the current frame depends on the previous frame and the future frames. Since the data is provided per frame, to reduce the computational load of data pre-processing in the worker node, we first encapsulate any sequential 27 frames into one feature sequence. 27 denotes the receptive field. Each feature contains the 2D positions of the 15 joints (keypoints). Each feature sequence therefore consists of the 2D positions of 27 frames. The target of a sequence is the 3D pose of the middle frame. This data is used for training and evaluation instead of the individual positions.
receptive_field = 27
w = Window.orderBy("Idx").partitionBy(["Group"]).rowsBetween(Window.currentRow-receptive_field//2, Window.currentRow+receptive_field//2)
def create_receptive_fields(df):
df = df.withColumn("feature_sequence", collect_list("features").over(w))
df = df.withColumn("group_sequence", collect_list("Group").over(w))
df = df.filter(size(df.group_sequence) == receptive_field)
return df
df_train_receptive = create_receptive_fields(df_train).drop("features")
df_test_receptive = create_receptive_fields(df_test).drop("features")
Visualisation of receptive field data
display(df_train_receptive)
Split training set into labeled and unlabeled based on chunks
In the project, we are exploring semi-supervised learning with psuedolabels, which requires both labeled and unlabeled training data. However, the original HumanEva-I dataset does not provide pre-defined sets of labeled and unlabeled data. Therefore, we randomly split the data, with respect to the group, into an unlabeled and labeled set. To have a realistic semi-supervised setting, we assume that the unlabeled training data is slighly larger than the labeled training data. The targets are droped for the unlabeled training set.
from random import sample, seed
## find right random seed to compensate for the different size of each chunk
seed(0) # seed 0 gives ok split
chunks = df_train_receptive.select("Group").distinct().collect()
chunks = [x["Group"] for x in chunks]
num_chunks = len(chunks)
num_unlabeled = int(num_chunks*0.6)
unlabeled_chunks = sample(chunks, num_unlabeled)
labeled_chunks = [x for x in chunks if x not in unlabeled_chunks]
df_train_receptive_unlabeled = df_train_receptive.filter(df_train_receptive.Group.isin(unlabeled_chunks))
df_train_receptive_unlabeled = df_train_receptive_unlabeled.drop("targets")
df_train_receptive_labeled = df_train_receptive.filter(~df_train_receptive.Group.isin(unlabeled_chunks))
Convert dataframes to torch tensors
Here we create RDDs for training and test from the corresponding DataFrames to RDDs. Thereafter, we map the vectors to Tensor enable training using PyTorch.
### We do not have targets for unlabelled dataset
def toTensorLabeled(x):
fs = x["feature_sequence"]
target = x["targets"]
feature_tensor = []
for f in fs:
feature_tensor.append(f)
xx = torch.tensor(feature_tensor,dtype=torch.float)
yy = torch.tensor(target,dtype=torch.float)
return xx.view(27, 15, 2), yy.view(1, 15, 3)
def toTensorUnlabeled(x):
fs = x["feature_sequence"]
feature_tensor = []
for f in fs:
feature_tensor.append(f)
xx = torch.tensor(feature_tensor, dtype=torch.float)
return xx.view(27, 15, 2)
labeled_tensor_rdd = df_train_receptive_labeled.rdd.map(toTensorLabeled)
unlabeled_tensor_rdd = df_train_receptive_unlabeled.rdd.map(toTensorUnlabeled)
test_tensor_rdd = df_test_receptive.rdd.map(toTensorLabeled)
Dataset split
Here we provide functions for * Train/Test split * Labeled/Unlabeled split * Dataset split for each member. Note that we provide two functions. The one is splitforensemble(), which guarantees that each member accesses the unique data. The other is sampledatafor_ensemble() simply randomly sample the same size of training data for each memeber meaning that there might be some resused data over different members.
def get_labeled_subset(labeled_tensor_rdd, full_size):
# Data is loaded into driver's memory
data = labeled_tensor_rdd.takeSample(True, full_size)
x, y = zip(*data)
return torch.stack(x), torch.stack(y)
def get_unlabeled_subset(unlabeled_tensor_rdd, full_size):
# Data is loaded into driver's memory
data = unlabeled_tensor_rdd.takeSample(True, full_size)
return torch.stack(data)
def split_for_ensemble(x,y, n_models, full_size):
'''
Splits data so that each member acesses unique data for training
'''
full_size = x.shape[0]
split_size = full_size//n_models +1
x = torch.split(x, split_size)
y = torch.split(y, split_size)
return list(zip(x, y))
def sample_data_for_ensemble(x,y, n_models, subset_size):
'''
Randomly sample a subset of training data
'''
x_ =[]
y_ =[]
subset_size = np.amin([subset_size, x.size(0)])
for i in range(n_models):
perm = torch.randperm(x.size(0))
idx = perm[:subset_size]
x_.append(x[idx])
y_.append(y[idx])
return list(zip(x_, y_))
Define model
Here we define the 3D pose estimation model with temporal convolutions and corresponding hyperparameters. Each ensemble will use this model to train on labeled data (including pseudolabels) and make predictions on unlabeled data.
from torch import nn
class Args:
# Data arguments
num_joints = 15
# Model arguments
stride = 1 # chunk size to use during training
epochs = 10 # 100 # number of training epochs
batch_size = 128 # batch size in terms of predicted frames
dropout = 0.25 # dropout probability
learning_rate = 0.001 # initial learning rate
lr_decay = 0.996 # learning rate decay per epoch
data_augmentation = True # disable train-time flipping
test_time_augmentation = True # disable test-time flipping
architecture = '3,3,3' # filter widths separated by comma
channels = 1024 # number of channels in convolution layers
args = Args()
filter_widths = [int(x) for x in args.architecture.split(',')]
receptive_field = np.prod(filter_widths) # model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
hyperparams = [args.num_joints, 2, args.num_joints, filter_widths, args.dropout, args.channels]
class TemporalModelBase(nn.Module):
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, dropout, channels):
super().__init__()
# Validate input
for fw in filter_widths:
assert fw % 2 != 0, 'Only odd filter widths are supported'
self.num_joints_in = num_joints_in
self.in_features = in_features
self.num_joints_out = num_joints_out
self.filter_widths = filter_widths
self.drop = nn.Dropout(dropout)
self.relu = nn.ReLU(inplace=True)
self.pad = [ filter_widths[0] // 2 ]
self.expand_bn = nn.BatchNorm1d(channels, momentum=0.1)
self.shrink = nn.Conv1d(channels, num_joints_out*3, 1)
def set_bn_momentum(self, momentum):
self.expand_bn.momentum = momentum
for bn in self.layers_bn:
bn.momentum = momentum
def forward(self, pos2D):
assert len(pos2D.shape) == 4 # pos2D: B x 27 x 15 x 2
assert pos2D.shape[-2] == self.num_joints_in # 15
assert pos2D.shape[-1] == self.in_features # 2
sz = pos2D.shape[:3] # B x 27 x 15
pos2D = pos2D.view(pos2D.shape[0], pos2D.shape[1], -1) # B x 27 x 15 * 2
pos2D = pos2D.permute(0, 2, 1) # B x 15 * 2 x 27
pos3D = self._forward_blocks(pos2D)
pos3D = pos3D.permute(0, 2, 1)
pos3D = pos3D.view(sz[0], -1, self.num_joints_out, 3)
return pos3D
class TemporalModel(TemporalModelBase):
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, dropout=0.25, channels=1024):
"""
Reference 3D pose estimation model with temporal convolutions.Initialize this model.
Arg:
num_joints_in -- number of input joints (i.e. 15 for HumanEva-I)
in_features -- number of input features for each joint (typically 2 for 2D input)
num_joints_out -- number of output joints (can be different than input)
filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
dropout -- dropout probability
channels -- number of convolution channels
"""
super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, dropout, channels)
self.expand_conv = nn.Conv1d(num_joints_in*in_features, channels, filter_widths[0], bias=False)
layers_conv = []
layers_bn = []
next_dilation = filter_widths[0] # 3
for i in range(1, len(filter_widths)):
self.pad.append((filter_widths[i] - 1)*next_dilation // 2) # [1, 3, 9]
layers_conv.append(nn.Conv1d(channels, channels,
filter_widths[i],
dilation=next_dilation,
bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
next_dilation *= filter_widths[i] # 3, 9, 27
self.layers_conv = nn.ModuleList(layers_conv)
self.layers_bn = nn.ModuleList(layers_bn)
def _forward_blocks(self, pos2D):
# pos2D: B x 15 * 2 x 27
x = self.drop(self.relu(self.expand_bn(self.expand_conv(pos2D)))) # B x 1024 x 25
for i in range(len(self.pad) - 1):
pad = self.pad[i+1] # 3, 9
res = x[:, :, pad : x.shape[2] - pad] # B x 1024 x 19, B x 1024 x 1
x = self.drop(self.relu(self.layers_bn[2*i](self.layers_conv[2*i](x)))) # B x 1024 x 19, B x 1024 x 1
x = res + self.drop(self.relu(self.layers_bn[2*i + 1](self.layers_conv[2*i + 1](x))))
pos3D = self.shrink(x) # B x 15*3 x 1
return pos3D
@staticmethod
def from_state_dict(params, hyperparams):
net = TemporalModel(*hyperparams)
net.load_state_dict(params)
return net
Loss
Here we define the loss used for training and evaluation. There are three metrics by convention. * Mean per-joint postion error (MPJPE), which is the mean Euclidean distance between predicted jlint postions and ground-truth joint postions; * The error after alignment with the ground truth in translation, rotation, and scale (P-MPJPE); * predicted poses with the ground-truth only in scale (N-MPJPE).
Here MPJPE is adopted for loss and evaluation.
def mpjpe(predicted, target):
"""
Mean per-joint position error (i.e. mean Euclidean distance),
often referred to as "Protocol #1" in many papers.
"""
assert predicted.shape == target.shape
return torch.mean(torch.norm(predicted - target, dim=len(target.shape)-1))
class DataSet(torch.utils.data.Dataset):
def __init__(self, pos2D, pos3D):
self.pos2D = pos2D # self.pos2D: B x 27 x 15 * 2
self.pos3D = pos3D # self.pos3D: B x 1 x 15 * 3
def __len__(self):
return self.pos2D.shape[0]
def __getitem__(self, ind):
pos2D = self.pos2D[ind] # pos2D: B x 27 x 15 * 2 -> 27 x 15 * 2
pos3D = self.pos3D[ind] # pos2D: B x 1 x 15 * 3 -> 1 x 15 * 2
return pos2D, pos3D
Train and predict models
The train and prediction models for each member are defined here. Note that Spark enables distribute these functions on the work node automatically.
def train(params, hyperparams, pos2D, pos3D, args):
model = TemporalModel.from_state_dict(params, hyperparams)
model.train()
lr = args.learning_rate
lr_decay = args.lr_decay
train_data = DataSet(pos2D, pos3D)
dataloader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True)
opt = torch.optim.Adam(model.parameters(), lr=lr, amsgrad=True)
initial_momentum = 0.1
final_momentum = 0.001
losses_3d_train = []
for epoch in range(args.epochs):
epoch_loss_3d_train = 0
N = 0
for batch in dataloader:
inputs_2d, inputs_3d = batch
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
model = model.cuda()
inputs_3d[:, :, 0] = 0
# Predict 3D poses
predicted_3d_pos = model(inputs_2d)
# Calcuclate MPJPE loss
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_train += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
loss_total = loss_3d_pos
opt.zero_grad()
loss_total.backward()
# Make one optimization step on batch
opt.step()
losses_3d_train.append(epoch_loss_3d_train / N)
print('[%d] lr %f 3d_train %f' % (
epoch + 1,
lr,
losses_3d_train[-1] * 1000))
# Decay learning rate exponentially
lr *= lr_decay
for param_group in opt.param_groups:
param_group['lr'] *= lr_decay
err = mpjpe(model(pos2D.cuda()), pos3D.cuda())
lossval = float(err.detach().cpu().numpy())
return model.state_dict(), lossval
def predict(params, hyperparams, x):
model = TemporalModel.from_state_dict(params, hyperparams)
model.eval()
if torch.cuda.is_available():
x = x.cuda()
model.cuda()
return model(x).detach().cpu()
Train ensemble models in parallel
Train_ensemble() is defined to enable the training of ensemble models in parallel. Note that the training is performed in work nodes.
def train_ensemble(n_models, model_params, data, hyperparams):
"""
n_models: number of ensemble members
model_params: number of learnable parameters
data: a list of training dataset for each member
hyperprams: the pre-defined parameters of the model
"""
model_data = []
args = Args()
assert len(model_params) == n_models
assert len(data) == n_models, f"Lenght mismatch, lenght of data is {len(data)}, while number of models are {n_models}"
for i, (x, y) in enumerate(data):
model_data.append((model_params[i], hyperparams, x, y, args)) # Pairs of model parameters, hyperparamers, training data, and arguments for each member.
# create an RDD
model_data_rdd = sc.parallelize(model_data)
# each memeber is trained using their own data
models_trained = model_data_rdd.map(lambda t: train(*t))
# after training, the trained models and loss values are sent to the driver node
models_trained = models_trained.collect()
print(f"Training losses: {[x[1] for x in models_trained]}")
return [x[0] for x in models_trained],[x[1] for x in models_trained]
Ensemble predictions
Note that the prediction is done in driver node.
def ensemble_predictions(models, hyperparams, test_x):
pred_iter = _pred_models_iter(models, hyperparams, test_x)
return pred_iter.map(lambda t: predict(*t))
def ensemble_predictions_reduced(models, hyperparams, test_x, reduce_fn):
return ensemble_predictions(models, hyperparams, test_x).reduce(reduce_fn)
def _pred_models_iter(models, hyperparams, test_x):
if isinstance(models, PipelinedRDD):
return models.map(lambda model: (model, test_x))
elif isinstance(models, list): # our case
models_and_data = [(params, hyperparams, test_x) for params in models]
return sc.parallelize(models_and_data)
else:
raise TypeError("'models' must be an RDD or a list")
def evaluate_avg_on_set(models, hyperparams, dataset, n_models):
predictions_sum = ensemble_predictions_reduced(models, hyperparams, dataset, lambda x, y: x + y) # Tensor output
predictions_avg = predictions_sum/n_models
return predictions_avg
def save_models(models_state_dict,save_models_dir: Path, iter: int, n_member : int) -> None:
"""
Save models after training of iteration
Args:
models_state_dict: list of state dicts of pytorch nn.Module models to be saved
save_models_dir: Path to dir where models are being saved
iter: iteration
n_member: number of members in the current ensemble model
"""
# Create saving path if it does not exist
save_models_dir.mkdir(parents=True, exist_ok=True)
for i_model, model_state_dict in enumerate(models_state_dict):
torch.save(model_state_dict, os.path.join(save_models_dir,f"{n_member}_members_ensemble{i_model}_iter{iter}.ckpt"))
Training loop (for supervised baseline)
We first establish the baseline, where the ensemble model is trained in a distrbuted way. Specifcially, each member is trained in a different work node in parallel. The hypothesis is that the prediction should be more accurate than the single model. Moreover, each member is limited to access a subset of the trainining data stored in the driver node. It is a natural idea to send the same fraction of training data to the work node. However, to avoid the scenario that the work node might not have enough space to store the subset of traning data, we set the threshold for the maximum size of the data to be stored in the work node. Pratically, the size of the subset of training data is fixed to be N=1000.
#n_models_set = [1 , 2, 3, 5, 10] #number of members
n_models_set = [2, 3, 5, 10]
# collect test data
data_test = test_tensor_rdd.collect()
x_test, y_test = zip(*data_test)
x_test, y_test = torch.stack(x_test).detach(), torch.stack(y_test).detach()
subset_size = 1000 # subset of training data allocated to each work node.
n_iterations = 100
for n_models in n_models_set:
test_mpjpes_supervised = []
train_mpjpes_iteration_supervised = []
total_size = n_models * subset_size
for n_models in n_models_set:
models_supervised = []
# initiate models
for i in range(n_models):
model = TemporalModel(*hyperparams)
models_supervised.append(model.state_dict())
# train using only labeled data
for iteration in range(n_iterations):
x_l, y_l = get_labeled_subset(labeled_tensor_rdd, total_size)
models_supervised, train_mjpes_supervised = train_ensemble(n_models, models_supervised, split_for_ensemble(x_l, y_l,n_models, total_size), hyperparams)
train_mpjpes_iteration_supervised.append(train_mjpes_supervised)
saved_models_dir = Path("/dbfs/VideoPose3D/saved_models/humaneva/checkpoints/supervised")
save_models(models_supervised, saved_models_dir, iteration, n_models)
# Ealuate on test set
with torch.no_grad():
test_preds_supervised = evaluate_avg_on_set(models_supervised, hyperparams, x_test, n_models)
test_mpjpe_supervised = mpjpe(test_preds_supervised, y_test)
test_mpjpes_supervised.append(test_mpjpe_supervised)
print("MPJPE for test set (supervised baseline):")
print(test_mpjpes_supervised)
To Do
- a figure shows the MPJPE is decreasing with the increasing number of members.
- a figure shows that the test error is further reduced while incorporating the pseudo-labeled data.
%matplotlib inline
fig = plt.figure()
plt.plot(test_mpjpes_supervised)
plt.title("Supervised")
plt.xlabel("Iteration")
plt.ylabel("MPJPE test loss")
plt.show()
%matplotlib inline
fig = plt.figure()
for i_model, mpjpes in enumerate(zip(*train_mpjpes_iteration_supervised)):
plt.plot(mpjpes, label=f"Ensemble {i_model}")
plt.title("Supervised")
plt.xlabel("Iteration")
plt.ylabel("MPJPE train loss")
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
Training loop (for semi-supervised learning)
The hypothesis of training ensemble models in a distributed way is that we coud obtain better target estimation for the unllablelled. Sepcifically, the prediction of the test sample is obtained by avergaing the prediciton from each memeber. Moreover, incoporating the samples with pseudo labels predicted by ensemble models into the training data is expcted to further improve the perfromace becasue more information is contained in the training data.
n_models = 20
subset_size = 1000
total_size = n_models * subset_size
start_unlabelled_size = 100
# collect test data
data_test = test_tensor_rdd.collect()
x_test, y_test = zip(*data_test)
x_test, y_test = torch.stack(x_test).detach(), torch.stack(y_test).detach()
iterations = 10 # 10
models = []
# initiate models
for i in range(n_models):
model = TemporalModel(*hyperparams)
models.append(model.state_dict())
# Sample a small subset of the labeled data.
# All data is loaded into driver's memory.
x_l, y_l = get_labeled_subset(labeled_tensor_rdd, total_size)
print(f"Training distributed ensemble of {len(models)} models")
# train using only labeled data
#models, train_mjpes = train_ensemble(n_models,
#models,
#split_for_ensemble(x_l, y_l,n_models, total_size),
#hyperparams)
models, train_mjpes = train_ensemble(n_models,
models,
sample_data_for_ensemble(x_l, y_l, n_models, subset_size),
hyperparams)
# evaluate on test set
with torch.no_grad():
test_preds = evaluate_avg_on_set(models, hyperparams, x_test, n_models)
test_mpjpe = mpjpe(test_preds, y_test)
print(f"MPJPE for test set: {test_mpjpe}")
print("Labeled training iteration finished")
test_mpjpes = []
train_mpjpes_iteration = []
# train using labeled and unlabeled data
for i in range(iterations):
# evaluate on test set
with torch.no_grad():
test_preds = evaluate_avg_on_set(models, hyperparams, x_test, n_models)
test_mpjpe = mpjpe(test_preds, y_test)
test_mpjpes.append(test_mpjpe)
print(f"MPJPE for test set: {test_mpjpe}")
# use an adaptive total size for unlablled dataste
full_size = (i+1)* start_unlabelled_size
x_ul = get_unlabeled_subset(unlabeled_tensor_rdd, full_size)
# predict unlabeled data
unlabeled_preds = evaluate_avg_on_set(models,
hyperparams,
x_ul,
n_models)
# Random pick a subset of trainning data
x_l, y_l = get_labeled_subset(labeled_tensor_rdd, total_size)
# concat labeled and unlabeled data
x_cc = torch.concat([x_l, x_ul])
y_cc = torch.concat([y_l, unlabeled_preds])
# mix labeled and unlabeled data by shuffling
idx = torch.randperm(x_cc.shape[0])
x_cc, y_cc = x_cc[idx], y_cc[idx]
print("Running semi-supervised training iteration: {}".format(i+1))
# train using mix of labeled and pseudolabeled data
#models, train_mjpes = train_ensemble(n_models,
#models,
#split_for_ensemble(x_cc, y_cc, n_models, total_size),
#hyperparams)
models, train_mjpes = train_ensemble(n_models,
models,
sample_data_for_ensemble(x_cc, y_cc, n_models, subset_size),
hyperparams)
train_mpjpes_iteration.append(train_mjpes)
saved_models_dir = Path("/dbfs/VideoPose3D/saved_models/humaneva/checkpoints/semi-supervised")
save_models(models, saved_models_dir,i)
# evaluate on test set
with torch.no_grad():
test_preds = evaluate_avg_on_set(models, hyperparams, x_test, n_models)
test_mpjpe = mpjpe(test_preds, y_test)
test_mpjpes.append(test_mpjpe)
print(f"MPJPE for test set: {test_mpjpe}")
%matplotlib inline
fig = plt.figure()
plt.plot(test_mpjpes)
plt.title("Semi-supervised using pseudotargets")
plt.xlabel("Iteration")
plt.ylabel("MPJPE test loss")
plt.show()
plt.close()
print(len(test_mpjpes))
%matplotlib inline
fig = plt.figure()
for i_model, mpjpes in enumerate(zip(*train_mpjpes_iteration)):
plt.plot(mpjpes, label=f"Ensemble {i_model}")
plt.title("Semi-supervised using pseudotargets")
plt.xlabel("Iteration")
plt.ylabel("MPJPE train loss")
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
# evaluate on test set
test_mpjpes=[]
with torch.no_grad():
test_preds = evaluate_avg_on_set(models, hyperparams, x_test, n_models)
test_mpjpe = mpjpe(test_preds, y_test)
test_mpjpes.append(test_mpjpe)
print("MPJPE for test set:")
print(test_mpjpes)
def all_equal(iterable):
g = groupby(iterable)
return next(g, True) and not next(g, False)
udf_all_equal = udf(all_equal, BooleanType())
# Broadcast hyperparams of models
hyperparams_rdd = sc.broadcast(hyperparams)
model_params = []
for i in range(n_models):
model = TemporalModel(*hyperparams)
model_params.append(model.state_dict())
model_params_rdd = sc.parallelize(model_params)
def train_distributed(model_params, hyperparams):
pass
def train_ensemble_distributed(model_params, data, hyperparams):
pass
for m in len(model_params.count()):
data.mapParitions(lambda k: train_distributed(k, model_params, hyperparams))
# optional to do data partition
def get_partitioned_rdd(input_rdd, partition_size=1000):
"""Partition RDD
Args:
input_rdd: RDD to be partitioned
Returns:
Partitioned RDD
"""
return input_rdd.mapPartitions(lambda partition: partition_all(partition_size, partition))
Parts of this code are taken from VideoPose3D repository.
Copyright (c) 2018-present, Facebook, Inc.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
ls VideoPose3D/checkpoint
| path | name | size |
|---|---|---|
| dbfs:/VideoPose3D/checkpoint/epoch_10.bin | epoch_10.bin | 1.36668703e8 |
| dbfs:/VideoPose3D/checkpoint/epoch_20.bin | epoch_20.bin | 1.36668639e8 |
| dbfs:/VideoPose3D/checkpoint/epoch_30.bin | epoch_30.bin | 1.36668639e8 |
| dbfs:/VideoPose3D/checkpoint/epoch_40.bin | epoch_40.bin | 1.36668639e8 |
| dbfs:/VideoPose3D/checkpoint/epoch_50.bin | epoch_50.bin | 1.36668703e8 |
| dbfs:/VideoPose3D/checkpoint/epoch_60.bin | epoch_60.bin | 1.36668639e8 |
| dbfs:/VideoPose3D/checkpoint/pretrained_h36m_cpn.bin | pretrained_h36m_cpn.bin | 6.7889963e7 |
| dbfs:/VideoPose3D/checkpoint/pretrained_humaneva15_detectron.bin | pretrained_humaneva15_detectron.bin | 3.4242417e7 |
import torch as th
import horovod.torch as hvd
th.cuda.is_available()
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import os
import sys
import errno
from time import time
import copy
import hashlib
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, writers
import subprocess as sp
from tqdm import tqdm
assert(torch.cuda.is_available())
ROOTDIR = '/dbfs/VideoPose3D'
def wrap(func, *args, unsqueeze=False):
"""
Wrap a torch function so it can be called with NumPy arrays.
Input and return types are seamlessly converted.
"""
# Convert input types where applicable
args = list(args)
for i, arg in enumerate(args):
if type(arg) == np.ndarray:
args[i] = torch.from_numpy(arg)
if unsqueeze:
args[i] = args[i].unsqueeze(0)
result = func(*args)
# Convert output types where applicable
if isinstance(result, tuple):
result = list(result)
for i, res in enumerate(result):
if type(res) == torch.Tensor:
if unsqueeze:
res = res.squeeze(0)
result[i] = res.numpy()
return tuple(result)
elif type(result) == torch.Tensor:
if unsqueeze:
result = result.squeeze(0)
return result.numpy()
else:
return result
def qrot(q, v):
"""
Rotate vector(s) v about the rotation described by quaternion(s) q.
Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
where * denotes any number of dimensions.
Returns a tensor of shape (*, 3).
"""
assert q.shape[-1] == 4
assert v.shape[-1] == 3
assert q.shape[:-1] == v.shape[:-1]
qvec = q[..., 1:]
uv = torch.cross(qvec, v, dim=len(q.shape)-1)
uuv = torch.cross(qvec, uv, dim=len(q.shape)-1)
return (v + 2 * (q[..., :1] * uv + uuv))
def qinverse(q, inplace=False):
# We assume the quaternion to be normalized
if inplace:
q[..., 1:] *= -1
return q
else:
w = q[..., :1]
xyz = q[..., 1:]
return torch.cat((w, -xyz), dim=len(q.shape)-1)
def normalize_screen_coordinates(X, w, h):
assert X.shape[-1] == 2
# Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio
return X/w*2 - [1, h/w]
def image_coordinates(X, w, h):
assert X.shape[-1] == 2
# Reverse camera frame normalization
return (X + [1, h/w])*w/2
def world_to_camera(X, R, t):
Rt = wrap(qinverse, R) # Invert rotation
return wrap(qrot, np.tile(Rt, (*X.shape[:-1], 1)), X - t) # Rotate and translate
def camera_to_world(X, R, t):
return wrap(qrot, np.tile(R, (*X.shape[:-1], 1)), X) + t
from itertools import zip_longest
import numpy as np
class ChunkedGenerator:
"""
Batched data generator, used for training.
The sequences are split into equal-length chunks and padded as necessary.
Arguments:
batch_size -- the batch size to use for training
cameras -- list of cameras, one element for each video (optional, used for semi-supervised training)
poses_3d -- list of ground-truth 3D poses, one element for each video (optional, used for supervised training)
poses_2d -- list of input 2D keypoints, one element for each video
chunk_length -- number of output frames to predict for each training example (usually 1)
pad -- 2D input padding to compensate for valid convolutions, per side (depends on the receptive field)
causal_shift -- asymmetric padding offset when causal convolutions are used (usually 0 or "pad")
shuffle -- randomly shuffle the dataset before each epoch
random_seed -- initial seed to use for the random generator
augment -- augment the dataset by flipping poses horizontally
kps_left and kps_right -- list of left/right 2D keypoints if flipping is enabled
joints_left and joints_right -- list of left/right 3D joints if flipping is enabled
"""
def __init__(self, batch_size, cameras, poses_3d, poses_2d,
chunk_length, pad=0, causal_shift=0,
shuffle=True, random_seed=1234,
augment=False, kps_left=None, kps_right=None, joints_left=None, joints_right=None,
endless=False):
assert poses_3d is None or len(poses_3d) == len(poses_2d), (len(poses_3d), len(poses_2d))
assert cameras is None or len(cameras) == len(poses_2d)
# Build lineage info
pairs = [] # (seq_idx, start_frame, end_frame, flip) tuples
for i in range(len(poses_2d)):
assert poses_3d is None or poses_3d[i].shape[0] == poses_3d[i].shape[0]
n_chunks = (poses_2d[i].shape[0] + chunk_length - 1) // chunk_length
offset = (n_chunks * chunk_length - poses_2d[i].shape[0]) // 2
bounds = np.arange(n_chunks+1)*chunk_length - offset
augment_vector = np.full(len(bounds - 1), False, dtype=bool)
pairs += zip(np.repeat(i, len(bounds - 1)), bounds[:-1], bounds[1:], augment_vector)
if augment:
pairs += zip(np.repeat(i, len(bounds - 1)), bounds[:-1], bounds[1:], ~augment_vector)
# Initialize buffers
if cameras is not None:
self.batch_cam = np.empty((batch_size, cameras[0].shape[-1]))
if poses_3d is not None:
self.batch_3d = np.empty((batch_size, chunk_length, poses_3d[0].shape[-2], poses_3d[0].shape[-1]))
self.batch_2d = np.empty((batch_size, chunk_length + 2*pad, poses_2d[0].shape[-2], poses_2d[0].shape[-1]))
self.num_batches = (len(pairs) + batch_size - 1) // batch_size
self.batch_size = batch_size
self.random = np.random.RandomState(random_seed)
self.pairs = pairs
self.shuffle = shuffle
self.pad = pad
self.causal_shift = causal_shift
self.endless = endless
self.state = None
self.cameras = cameras
self.poses_3d = poses_3d
self.poses_2d = poses_2d
self.augment = augment
self.kps_left = kps_left
self.kps_right = kps_right
self.joints_left = joints_left
self.joints_right = joints_right
def num_frames(self):
return self.num_batches * self.batch_size
def random_state(self):
return self.random
def set_random_state(self, random):
self.random = random
def augment_enabled(self):
return self.augment
def next_pairs(self):
if self.state is None:
if self.shuffle:
pairs = self.random.permutation(self.pairs)
else:
pairs = self.pairs
return 0, pairs
else:
return self.state
def next_epoch(self):
enabled = True
while enabled:
start_idx, pairs = self.next_pairs()
for b_i in range(start_idx, self.num_batches):
chunks = pairs[b_i*self.batch_size : (b_i+1)*self.batch_size]
for i, (seq_i, start_3d, end_3d, flip) in enumerate(chunks):
start_2d = start_3d - self.pad - self.causal_shift
end_2d = end_3d + self.pad - self.causal_shift
# 2D poses
seq_2d = self.poses_2d[seq_i]
low_2d = max(start_2d, 0)
high_2d = min(end_2d, seq_2d.shape[0])
pad_left_2d = low_2d - start_2d
pad_right_2d = end_2d - high_2d
if pad_left_2d != 0 or pad_right_2d != 0:
self.batch_2d[i] = np.pad(seq_2d[low_2d:high_2d], ((pad_left_2d, pad_right_2d), (0, 0), (0, 0)), 'edge')
else:
self.batch_2d[i] = seq_2d[low_2d:high_2d]
if flip:
# Flip 2D keypoints
self.batch_2d[i, :, :, 0] *= -1
self.batch_2d[i, :, self.kps_left + self.kps_right] = self.batch_2d[i, :, self.kps_right + self.kps_left]
# 3D poses
if self.poses_3d is not None:
seq_3d = self.poses_3d[seq_i]
low_3d = max(start_3d, 0)
high_3d = min(end_3d, seq_3d.shape[0])
pad_left_3d = low_3d - start_3d
pad_right_3d = end_3d - high_3d
if pad_left_3d != 0 or pad_right_3d != 0:
self.batch_3d[i] = np.pad(seq_3d[low_3d:high_3d], ((pad_left_3d, pad_right_3d), (0, 0), (0, 0)), 'edge')
else:
self.batch_3d[i] = seq_3d[low_3d:high_3d]
if flip:
# Flip 3D joints
self.batch_3d[i, :, :, 0] *= -1
self.batch_3d[i, :, self.joints_left + self.joints_right] = \
self.batch_3d[i, :, self.joints_right + self.joints_left]
# Cameras
if self.cameras is not None:
self.batch_cam[i] = self.cameras[seq_i]
if flip:
# Flip horizontal distortion coefficients
self.batch_cam[i, 2] *= -1
self.batch_cam[i, 7] *= -1
if self.endless:
self.state = (b_i + 1, pairs)
if self.poses_3d is None and self.cameras is None:
yield None, None, self.batch_2d[:len(chunks)]
elif self.poses_3d is not None and self.cameras is None:
yield None, self.batch_3d[:len(chunks)], self.batch_2d[:len(chunks)]
elif self.poses_3d is None:
yield self.batch_cam[:len(chunks)], None, self.batch_2d[:len(chunks)]
else:
yield self.batch_cam[:len(chunks)], self.batch_3d[:len(chunks)], self.batch_2d[:len(chunks)]
if self.endless:
self.state = None
else:
enabled = False
class UnchunkedGenerator:
"""
Non-batched data generator, used for testing.
Sequences are returned one at a time (i.e. batch size = 1), without chunking.
If data augmentation is enabled, the batches contain two sequences (i.e. batch size = 2),
the second of which is a mirrored version of the first.
Arguments:
cameras -- list of cameras, one element for each video (optional, used for semi-supervised training)
poses_3d -- list of ground-truth 3D poses, one element for each video (optional, used for supervised training)
poses_2d -- list of input 2D keypoints, one element for each video
pad -- 2D input padding to compensate for valid convolutions, per side (depends on the receptive field)
causal_shift -- asymmetric padding offset when causal convolutions are used (usually 0 or "pad")
augment -- augment the dataset by flipping poses horizontally
kps_left and kps_right -- list of left/right 2D keypoints if flipping is enabled
joints_left and joints_right -- list of left/right 3D joints if flipping is enabled
"""
def __init__(self, cameras, poses_3d, poses_2d, pad=0, causal_shift=0,
augment=False, kps_left=None, kps_right=None, joints_left=None, joints_right=None):
assert poses_3d is None or len(poses_3d) == len(poses_2d)
assert cameras is None or len(cameras) == len(poses_2d)
self.augment = augment
self.kps_left = kps_left
self.kps_right = kps_right
self.joints_left = joints_left
self.joints_right = joints_right
self.pad = pad
self.causal_shift = causal_shift
self.cameras = [] if cameras is None else cameras
self.poses_3d = [] if poses_3d is None else poses_3d
self.poses_2d = poses_2d
def num_frames(self):
count = 0
for p in self.poses_2d:
count += p.shape[0]
return count
def augment_enabled(self):
return self.augment
def set_augment(self, augment):
self.augment = augment
def next_epoch(self):
for seq_cam, seq_3d, seq_2d in zip_longest(self.cameras, self.poses_3d, self.poses_2d):
batch_cam = None if seq_cam is None else np.expand_dims(seq_cam, axis=0)
batch_3d = None if seq_3d is None else np.expand_dims(seq_3d, axis=0)
batch_2d = np.expand_dims(np.pad(seq_2d,
((self.pad + self.causal_shift, self.pad - self.causal_shift), (0, 0), (0, 0)),
'edge'), axis=0)
if self.augment:
# Append flipped version
if batch_cam is not None:
batch_cam = np.concatenate((batch_cam, batch_cam), axis=0)
batch_cam[1, 2] *= -1
batch_cam[1, 7] *= -1
if batch_3d is not None:
batch_3d = np.concatenate((batch_3d, batch_3d), axis=0)
batch_3d[1, :, :, 0] *= -1
batch_3d[1, :, self.joints_left + self.joints_right] = batch_3d[1, :, self.joints_right + self.joints_left]
batch_2d = np.concatenate((batch_2d, batch_2d), axis=0)
batch_2d[1, :, :, 0] *= -1
batch_2d[1, :, self.kps_left + self.kps_right] = batch_2d[1, :, self.kps_right + self.kps_left]
yield batch_cam, batch_3d, batch_2d
class Skeleton:
def __init__(self, parents, joints_left, joints_right):
assert len(joints_left) == len(joints_right)
self._parents = np.array(parents)
self._joints_left = joints_left
self._joints_right = joints_right
self._compute_metadata()
def num_joints(self):
return len(self._parents)
def parents(self):
return self._parents
def has_children(self):
return self._has_children
def children(self):
return self._children
def remove_joints(self, joints_to_remove):
"""
Remove the joints specified in 'joints_to_remove'.
"""
valid_joints = []
for joint in range(len(self._parents)):
if joint not in joints_to_remove:
valid_joints.append(joint)
for i in range(len(self._parents)):
while self._parents[i] in joints_to_remove:
self._parents[i] = self._parents[self._parents[i]]
index_offsets = np.zeros(len(self._parents), dtype=int)
new_parents = []
for i, parent in enumerate(self._parents):
if i not in joints_to_remove:
new_parents.append(parent - index_offsets[parent])
else:
index_offsets[i:] += 1
self._parents = np.array(new_parents)
if self._joints_left is not None:
new_joints_left = []
for joint in self._joints_left:
if joint in valid_joints:
new_joints_left.append(joint - index_offsets[joint])
self._joints_left = new_joints_left
if self._joints_right is not None:
new_joints_right = []
for joint in self._joints_right:
if joint in valid_joints:
new_joints_right.append(joint - index_offsets[joint])
self._joints_right = new_joints_right
self._compute_metadata()
return valid_joints
def joints_left(self):
return self._joints_left
def joints_right(self):
return self._joints_right
def _compute_metadata(self):
self._has_children = np.zeros(len(self._parents)).astype(bool)
for i, parent in enumerate(self._parents):
if parent != -1:
self._has_children[parent] = True
self._children = []
for i, parent in enumerate(self._parents):
self._children.append([])
for i, parent in enumerate(self._parents):
if parent != -1:
self._children[parent].append(i)
class MocapDataset:
def __init__(self, fps, skeleton):
self._skeleton = skeleton
self._fps = fps
self._data = None # Must be filled by subclass
self._cameras = None # Must be filled by subclass
def remove_joints(self, joints_to_remove):
kept_joints = self._skeleton.remove_joints(joints_to_remove)
for subject in self._data.keys():
for action in self._data[subject].keys():
s = self._data[subject][action]
if 'positions' in s:
s['positions'] = s['positions'][:, kept_joints]
def __getitem__(self, key):
return self._data[key]
def subjects(self):
return self._data.keys()
def fps(self):
return self._fps
def skeleton(self):
return self._skeleton
def cameras(self):
return self._cameras
def supports_semi_supervised(self):
# This method can be overridden
return False
humaneva_skeleton_data = np.load(f'{ROOTDIR}/humaneva/humaneva_skeleton.npz', allow_pickle=True)['data'].item()
humaneva_skeleton = Skeleton(parents=humaneva_skeleton_data['parents'],
joints_left=humaneva_skeleton_data['joints_left'],
joints_right=humaneva_skeleton_data['joints_right'])
humaneva_cameras_intrinsic_params = list(np.load(f'{ROOTDIR}/humaneva/humaneva_cameras_intrinsic_params.npz', allow_pickle=True)['data'])
humaneva_cameras_extrinsic_params = np.load(f'{ROOTDIR}/humaneva/humaneva_cameras_extrinsic_params.npz', allow_pickle=True)['data'].item()
class HumanEvaDataset(MocapDataset):
def __init__(self, path):
super().__init__(fps=60, skeleton=humaneva_skeleton)
self._cameras = copy.deepcopy(humaneva_cameras_extrinsic_params)
for cameras in self._cameras.values():
for i, cam in enumerate(cameras):
cam.update(humaneva_cameras_intrinsic_params[i])
for k, v in cam.items():
if k not in ['id', 'res_w', 'res_h']:
cam[k] = np.array(v, dtype='float32')
if 'translation' in cam:
cam['translation'] = cam['translation']/1000 # mm to meters
for subject in list(self._cameras.keys()):
data = self._cameras[subject]
del self._cameras[subject]
for prefix in ['Train/', 'Validate/', 'Unlabeled/Train/', 'Unlabeled/Validate/', 'Unlabeled/']:
self._cameras[prefix + subject] = data
# Load serialized dataset
data = np.load(path, allow_pickle=True)['positions_3d'].item()
self._data = {}
for subject, actions in data.items():
self._data[subject] = {}
for action_name, positions in actions.items():
self._data[subject][action_name] = {
'positions': positions,
'cameras': self._cameras[subject],
}
h36m_skeleton_data = np.load(f'{ROOTDIR}/h36m/h36m_skeleton.npz', allow_pickle=True)['data'].item()
h36m_skeleton = Skeleton(parents=h36m_skeleton_data['parents'],
joints_left=h36m_skeleton_data['joints_left'],
joints_right=h36m_skeleton_data['joints_right'])
h36m_cameras_intrinsic_params = list(np.load(f'{ROOTDIR}/h36m/h36m_cameras_intrinsic_params.npz', allow_pickle=True)['data'])
h36m_cameras_extrinsic_params = np.load(f'{ROOTDIR}/h36m/h36m_cameras_extrinsic_params.npz', allow_pickle=True)['data'].item()
class Human36mDataset(MocapDataset):
def __init__(self, path, remove_static_joints=True):
super().__init__(fps=50, skeleton=h36m_skeleton)
self._cameras = copy.deepcopy(h36m_cameras_extrinsic_params)
for cameras in self._cameras.values():
for i, cam in enumerate(cameras):
cam.update(h36m_cameras_intrinsic_params[i])
for k, v in cam.items():
if k not in ['id', 'res_w', 'res_h']:
cam[k] = np.array(v, dtype='float32')
# Normalize camera frame
cam['center'] = normalize_screen_coordinates(cam['center'], w=cam['res_w'], h=cam['res_h']).astype('float32')
cam['focal_length'] = cam['focal_length']/cam['res_w']*2
if 'translation' in cam:
cam['translation'] = cam['translation']/1000 # mm to meters
# Add intrinsic parameters vector
cam['intrinsic'] = np.concatenate((cam['focal_length'],
cam['center'],
cam['radial_distortion'],
cam['tangential_distortion']))
# Load serialized dataset
data = np.load(path, allow_pickle=True)['positions_3d'].item()
self._data = {}
for subject, actions in data.items():
self._data[subject] = {}
for action_name, positions in actions.items():
self._data[subject][action_name] = {
'positions': positions,
'cameras': self._cameras[subject],
}
if remove_static_joints:
# Bring the skeleton to 17 joints instead of the original 32
self.remove_joints([4, 5, 9, 10, 11, 16, 20, 21, 22, 23, 24, 28, 29, 30, 31])
# Rewire shoulders to the correct parents
self._skeleton._parents[11] = 8
self._skeleton._parents[14] = 8
def supports_semi_supervised(self):
return True
class TemporalModelBase(nn.Module):
"""
Do not instantiate this class.
"""
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, causal, dropout, channels):
super().__init__()
# Validate input
for fw in filter_widths:
assert fw % 2 != 0, 'Only odd filter widths are supported'
self.num_joints_in = num_joints_in
self.in_features = in_features
self.num_joints_out = num_joints_out
self.filter_widths = filter_widths
self.drop = nn.Dropout(dropout)
self.relu = nn.ReLU(inplace=True)
self.pad = [ filter_widths[0] // 2 ]
self.expand_bn = nn.BatchNorm1d(channels, momentum=0.1)
self.shrink = nn.Conv1d(channels, num_joints_out*3, 1)
def set_bn_momentum(self, momentum):
self.expand_bn.momentum = momentum
for bn in self.layers_bn:
bn.momentum = momentum
def receptive_field(self):
"""
Return the total receptive field of this model as # of frames.
"""
frames = 0
for f in self.pad:
frames += f
return 1 + 2*frames
def total_causal_shift(self):
"""
Return the asymmetric offset for sequence padding.
The returned value is typically 0 if causal convolutions are disabled,
otherwise it is half the receptive field.
"""
frames = self.causal_shift[0]
next_dilation = self.filter_widths[0]
for i in range(1, len(self.filter_widths)):
frames += self.causal_shift[i] * next_dilation
next_dilation *= self.filter_widths[i]
return frames
def forward(self, x):
assert len(x.shape) == 4
assert x.shape[-2] == self.num_joints_in
assert x.shape[-1] == self.in_features
sz = x.shape[:3]
x = x.view(x.shape[0], x.shape[1], -1)
x = x.permute(0, 2, 1)
x = self._forward_blocks(x)
x = x.permute(0, 2, 1)
x = x.view(sz[0], -1, self.num_joints_out, 3)
return x
class TemporalModel(TemporalModelBase):
"""
Reference 3D pose estimation model with temporal convolutions.
This implementation can be used for all use-cases.
"""
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, causal=False, dropout=0.25, channels=1024, dense=False):
"""
Initialize this model.
Arguments:
num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
in_features -- number of input features for each joint (typically 2 for 2D input)
num_joints_out -- number of output joints (can be different than input)
filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
causal -- use causal convolutions instead of symmetric convolutions (for real-time applications)
dropout -- dropout probability
channels -- number of convolution channels
dense -- use regular dense convolutions instead of dilated convolutions (ablation experiment)
"""
super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
self.expand_conv = nn.Conv1d(num_joints_in*in_features, channels, filter_widths[0], bias=False)
layers_conv = []
layers_bn = []
self.causal_shift = [ (filter_widths[0]) // 2 if causal else 0 ]
next_dilation = filter_widths[0]
for i in range(1, len(filter_widths)):
self.pad.append((filter_widths[i] - 1)*next_dilation // 2)
self.causal_shift.append((filter_widths[i]//2 * next_dilation) if causal else 0)
layers_conv.append(nn.Conv1d(channels, channels,
filter_widths[i] if not dense else (2*self.pad[-1] + 1),
dilation=next_dilation if not dense else 1,
bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
next_dilation *= filter_widths[i]
self.layers_conv = nn.ModuleList(layers_conv)
self.layers_bn = nn.ModuleList(layers_bn)
def _forward_blocks(self, x):
x = self.drop(self.relu(self.expand_bn(self.expand_conv(x))))
for i in range(len(self.pad) - 1):
pad = self.pad[i+1]
shift = self.causal_shift[i+1]
res = x[:, :, pad + shift : x.shape[2] - pad + shift]
x = self.drop(self.relu(self.layers_bn[2*i](self.layers_conv[2*i](x))))
x = res + self.drop(self.relu(self.layers_bn[2*i + 1](self.layers_conv[2*i + 1](x))))
x = self.shrink(x)
return x
def mpjpe(predicted, target):
"""
Mean per-joint position error (i.e. mean Euclidean distance),
often referred to as "Protocol #1" in many papers.
"""
assert predicted.shape == target.shape
return torch.mean(torch.norm(predicted - target, dim=len(target.shape)-1))
def weighted_mpjpe(predicted, target, w):
"""
Weighted mean per-joint position error (i.e. mean Euclidean distance)
"""
assert predicted.shape == target.shape
assert w.shape[0] == predicted.shape[0]
return torch.mean(w * torch.norm(predicted - target, dim=len(target.shape)-1))
def p_mpjpe(predicted, target):
"""
Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
often referred to as "Protocol #2" in many papers.
"""
assert predicted.shape == target.shape
muX = np.mean(target, axis=1, keepdims=True)
muY = np.mean(predicted, axis=1, keepdims=True)
X0 = target - muX
Y0 = predicted - muY
normX = np.sqrt(np.sum(X0**2, axis=(1, 2), keepdims=True))
normY = np.sqrt(np.sum(Y0**2, axis=(1, 2), keepdims=True))
X0 /= normX
Y0 /= normY
H = np.matmul(X0.transpose(0, 2, 1), Y0)
U, s, Vt = np.linalg.svd(H)
V = Vt.transpose(0, 2, 1)
R = np.matmul(V, U.transpose(0, 2, 1))
# Avoid improper rotations (reflections), i.e. rotations with det(R) = -1
sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
V[:, :, -1] *= sign_detR
s[:, -1] *= sign_detR.flatten()
R = np.matmul(V, U.transpose(0, 2, 1)) # Rotation
tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)
a = tr * normX / normY # Scale
t = muX - a*np.matmul(muY, R) # Translation
# Perform rigid transformation on the input
predicted_aligned = a*np.matmul(predicted, R) + t
# Return MPJPE
return np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape)-1))
def n_mpjpe(predicted, target):
"""
Normalized MPJPE (scale only), adapted from:
https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py
"""
assert predicted.shape == target.shape
norm_predicted = torch.mean(torch.sum(predicted**2, dim=3, keepdim=True), dim=2, keepdim=True)
norm_target = torch.mean(torch.sum(target*predicted, dim=3, keepdim=True), dim=2, keepdim=True)
scale = norm_target / norm_predicted
return mpjpe(scale * predicted, target)
def mean_velocity_error(predicted, target):
"""
Mean per-joint velocity error (i.e. mean Euclidean distance of the 1st derivative)
"""
assert predicted.shape == target.shape
velocity_predicted = np.diff(predicted, axis=0)
velocity_target = np.diff(target, axis=0)
return np.mean(np.linalg.norm(velocity_predicted - velocity_target, axis=len(target.shape)-1))
class Args:
# General arguments
dataset = 'humaneva' # target dataset # h36m or humaneva
keypoints = 'cpn_ft_h36m_dbb' # 2D detections to use
subjects_train = 'Train/S1,Train/S2,Train/S3' # 'S1,S5,S6,S7,S8' # training subjects separated by comma
subjects_test = 'Validate/S1,Validate/S2,Validate/S3' # 'S9,S11' # test subjects separated by comma
subjects_unlabeled = '' # unlabeled subjects separated by comma for self-supervision
actions = 'Walk,Jog,Box' #'*' # actions to train/test on, separated by comma, or * for all
checkpoint = f'/{ROOTDIR}/checkpoint' # checkpoint directory
checkpoint_frequency = 10 # create a checkpoint every N epochs
resume = '' # checkpoint to resume (file name)
evaluate = '' # checkpoint to evaluate (file name)
render = False # visualize a particular video
by_subject = True # False # break down error by subject (on evaluation)
export_training_curves = False # save training curves as .png images
# Model arguments
stride = 1 # chunk size to use during training
epochs = 1000 # 60 # number of training epochs
batch_size = 128 # 1024 # batch size in terms of predicted frames
dropout = 0.25 # dropout probability
learning_rate = 0.001 # initial learning rate
lr_decay = 0.996 # 0.95 # learning rate decay per epoch
data_augmentation = True # disable train-time flipping
test_time_augmentation = True # disable test-time flipping
architecture = '3,3,3' # filter widths separated by comma
causal = False # use causal convolutions for real-time processing
channels = 1024 # number of channels in convolution layers
# Experimental
subset = 1 # reduce dataset size by fraction
downsample = 1 # downsample frame rate by factor (semi-supervised)
warmup = 1 # warm-up epochs for semi-supervision
no_eval = False # disable epoch evaluation while training (small speed-up)
dense = False # use dense convolutions instead of dilated convolutions
disable_optimizations = False # disable optimized model for single-frame predictions
linear_projection = False # use only linear coefficients for semi-supervised projection
bone_length = False # disable bone length term in semi-supervised settings
no_proj = False # disable projection for semi-supervised setting
bone_length_term = True
# Visualization
viz_subject = '' # subject to render
viz_action = '' # action to render
viz_camera = 0 # camera to render
viz_video = '' # path to input video
viz_skip = 0 # skip first N frames of input video
viz_output = '' # output file name (.gif or .mp4)
viz_export = '' # output file name for coordinates
viz_bitrate = 3000 # bitrate for mp4 videos
viz_no_ground_truth = False # do not show ground-truth poses
viz_limit = -1 # only render first N frames
viz_downsample = 1 # downsample FPS by a factor N
viz_size = 5 # image size
args = Args()
if args.resume and args.evaluate:
print('Invalid flags: --resume and --evaluate cannot be set at the same time')
exit()
if args.export_training_curves and args.no_eval:
print('Invalid flags: --export-training-curves and --no-eval cannot be set at the same time')
exit()
if args.dataset == 'h36m':
dataset_path = f'{ROOTDIR}/{args.dataset}/data_3d_{args.dataset}.npz'
keypoints_path = 'data/data_2d_' + args.dataset + '_' + args.keypoints + '.npz'
dataset = Human36mDataset(dataset_path)
elif args.dataset.startswith('humaneva'):
dataset_path = f'{ROOTDIR}/{args.dataset}/data_3d_{args.dataset}15.npz'
keypoints_path = f'{ROOTDIR}/{args.dataset}/data_2d_{args.dataset}15_gt.npz'
dataset = HumanEvaDataset(dataset_path)
else:
raise KeyError('Invalid dataset')
for subject in dataset.subjects():
for action in dataset[subject].keys():
anim = dataset[subject][action]
if 'positions' in anim:
positions_3d = []
for cam in anim['cameras']:
pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation'])
pos_3d[:, 1:] -= pos_3d[:, :1]
# Remove global offset, but keep trajectory in first position
positions_3d.append(pos_3d)
anim['positions_3d'] = positions_3d
keypoints = np.load(keypoints_path, allow_pickle=True)
keypoints_metadata = keypoints['metadata'].item()
keypoints_symmetry = keypoints_metadata['keypoints_symmetry']
kps_left, kps_right = list(keypoints_symmetry[0]), list(keypoints_symmetry[1])
joints_left, joints_right = list(dataset.skeleton().joints_left()), list(dataset.skeleton().joints_right())
keypoints = keypoints['positions_2d'].item()
for subject in dataset.subjects():
assert subject in keypoints, 'Subject {} is missing from the 2D detections dataset'.format(subject)
for action in dataset[subject].keys():
assert action in keypoints[subject], 'Action {} of subject {} is missing from the 2D detections dataset'.format(action, subject)
if 'positions_3d' not in dataset[subject][action]:
continue
for cam_idx in range(len(keypoints[subject][action])):
# We check for >= instead of == because some videos in H3.6M contain extra frames
mocap_length = dataset[subject][action]['positions_3d'][cam_idx].shape[0]
assert keypoints[subject][action][cam_idx].shape[0] >= mocap_length
if keypoints[subject][action][cam_idx].shape[0] > mocap_length:
# Shorten sequence
keypoints[subject][action][cam_idx] = keypoints[subject][action][cam_idx][:mocap_length]
assert len(keypoints[subject][action]) == len(dataset[subject][action]['positions_3d'])
for subject in keypoints.keys():
for action in keypoints[subject]:
for cam_idx, kps in enumerate(keypoints[subject][action]):
# Normalize camera frame
cam = dataset.cameras()[subject][cam_idx]
kps[..., :2] = normalize_screen_coordinates(kps[..., :2], w=cam['res_w'], h=cam['res_h'])
keypoints[subject][action][cam_idx] = kps
def fetch(subjects, action_filter=None, subset=1, parse_3d_poses=True):
out_poses_3d = []
out_poses_2d = []
out_camera_params = []
for subject in subjects:
for action in keypoints[subject].keys():
if action_filter is not None:
found = False
for a in action_filter:
if action.startswith(a):
found = True
break
if not found:
continue
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
if subject in dataset.cameras():
cams = dataset.cameras()[subject]
assert len(cams) == len(poses_2d), 'Camera count mismatch'
for cam in cams:
if 'intrinsic' in cam:
out_camera_params.append(cam['intrinsic'])
if parse_3d_poses and 'positions_3d' in dataset[subject][action]:
poses_3d = dataset[subject][action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
for i in range(len(poses_3d)): # Iterate across cameras
out_poses_3d.append(poses_3d[i])
if len(out_camera_params) == 0:
out_camera_params = None
if len(out_poses_3d) == 0:
out_poses_3d = None
stride = args.downsample
if subset < 1:
for i in range(len(out_poses_2d)):
n_frames = int(round(len(out_poses_2d[i])//stride * subset)*stride)
start = deterministic_random(0, len(out_poses_2d[i]) - n_frames + 1, str(len(out_poses_2d[i])))
out_poses_2d[i] = out_poses_2d[i][start:start+n_frames:stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][start:start+n_frames:stride]
elif stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_camera_params, out_poses_3d, out_poses_2d
subjects_train = args.subjects_train.split(',')
subjects_semi = [] if not args.subjects_unlabeled else args.subjects_unlabeled.split(',')
if not args.render:
subjects_test = args.subjects_test.split(',')
else:
subjects_test = [args.viz_subject]
action_filter = None if args.actions == '*' else args.actions.split(',')
if action_filter is not None:
print('Selected actions:', action_filter)
cameras_valid, poses_valid, poses_valid_2d = fetch(subjects_test, action_filter)
filter_widths = [int(x) for x in args.architecture.split(',')]
# Non-optimized version for non-single-frame preditictions
# Also the only possible option for stride > 1 and/or dense filters
model_pos_train = TemporalModel(poses_valid_2d[0].shape[-2],
poses_valid_2d[0].shape[-1],
dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
model_pos = TemporalModel(poses_valid_2d[0].shape[-2],
poses_valid_2d[0].shape[-1],
dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
receptive_field = model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
if args.causal:
print('INFO: Using causal convolutions')
causal_shift = pad
else:
causal_shift = 0
model_params = 0
for parameter in model_pos.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
model_pos_train = model_pos_train.cuda()
if args.resume or args.evaluate:
chk_filename = os.path.join(args.checkpoint, args.resume if args.resume else args.evaluate)
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
model_pos_train.load_state_dict(checkpoint['model_pos'])
model_pos.load_state_dict(checkpoint['model_pos'])
if args.evaluate and 'model_traj' in checkpoint:
# Load trajectory model if it contained in the checkpoint (e.g. for inference in the wild)
model_traj = TemporalModel(poses_valid_2d[0].shape[-2],
poses_valid_2d[0].shape[-1],
1,
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
if torch.cuda.is_available():
model_traj = model_traj.cuda()
model_traj.load_state_dict(checkpoint['model_traj'])
else:
model_traj = None
test_generator = UnchunkedGenerator(cameras_valid,
poses_valid,
poses_valid_2d,
pad=pad,
causal_shift=causal_shift,
augment=False,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
print('INFO: Testing on {} frames'.format(test_generator.num_frames()))
if not args.evaluate:
# This is just to confirm we really want to (re-) train the model
# Otherwise this cell will be skipped
cameras_train, poses_train, poses_train_2d = fetch(subjects_train, action_filter, subset=args.subset)
lr = args.learning_rate
optimizer = optim.Adam(model_pos_train.parameters(), lr=lr, amsgrad=True)
lr_decay = args.lr_decay
losses_3d_train = []
losses_3d_train_eval = []
losses_3d_valid = []
epoch = 0
initial_momentum = 0.1
final_momentum = 0.001
train_generator = ChunkedGenerator(args.batch_size//args.stride,
cameras_train,
poses_train,
poses_train_2d,
args.stride,
pad=pad,
causal_shift=causal_shift,
shuffle=True,
augment=args.data_augmentation,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
train_generator_eval = UnchunkedGenerator(cameras_train,
poses_train,
poses_train_2d,
pad=pad,
causal_shift=causal_shift,
augment=False)
print('INFO: Training on {} frames'.format(train_generator_eval.num_frames()))
if args.resume:
epoch = checkpoint['epoch']
if 'optimizer' in checkpoint and checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
train_generator.set_random_state(checkpoint['random_state'])
else:
print('WARNING: this checkpoint does not contain an optimizer state. The optimizer will be reinitialized.')
lr = checkpoint['lr']
print('** Note: reported losses are averaged over all frames and test-time augmentation is not used here.')
print('** The final evaluation will be carried out after the last training epoch.')
# Pos model only
while epoch < args.epochs:
start_time = time()
epoch_loss_3d_train = 0
epoch_loss_traj_train = 0
epoch_loss_2d_train_unlabeled = 0
N = 0
N_semi = 0
model_pos_train.train()
# Regular supervised scenario
for _, batch_3d, batch_2d in tqdm(train_generator.next_epoch()):
inputs_3d = torch.from_numpy(batch_3d.astype('float32'))
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_3d[:, :, 0] = 0
optimizer.zero_grad()
# Predict 3D poses
predicted_3d_pos = model_pos_train(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_train += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
loss_total = loss_3d_pos
loss_total.backward()
optimizer.step()
losses_3d_train.append(epoch_loss_3d_train / N)
# End-of-epoch evaluation
with torch.no_grad():
model_pos.load_state_dict(model_pos_train.state_dict())
model_pos.eval()
# if not args.no_eval:
# Evaluate on test set
epoch_loss_3d_valid = 0
epoch_loss_traj_valid = 0
epoch_loss_2d_valid = 0
N = 0
for cam, batch, batch_2d in tqdm(test_generator.next_epoch()):
inputs_3d = torch.from_numpy(batch.astype('float32'))
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
# Predict 3D poses
predicted_3d_pos = model_pos(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_valid += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
losses_3d_valid.append(epoch_loss_3d_valid / N)
# Evaluate on training set, this time in evaluation mode
epoch_loss_3d_train_eval = 0
epoch_loss_traj_train_eval = 0
epoch_loss_2d_train_labeled_eval = 0
N = 0
for cam, batch, batch_2d in tqdm(train_generator_eval.next_epoch()):
if batch_2d.shape[1] == 0:
# This can only happen when downsampling the dataset
continue
inputs_3d = torch.from_numpy(batch.astype('float32'))
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
# Compute 3D poses
predicted_3d_pos = model_pos(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_train_eval += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
losses_3d_train_eval.append(epoch_loss_3d_train_eval / N)
# Calculate inference time
elapsed = (time() - start_time)/60
# if args.no_eval:
# print('[%d] time %.2f lr %f 3d_train %f' % (
# epoch + 1,
# elapsed,
# lr,
# losses_3d_train[-1] * 1000))
# else:
print('[%d] time %.2f lr %f 3d_train %f 3d_eval %f 3d_valid %f' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000,
losses_3d_train_eval[-1] * 1000,
losses_3d_valid[-1] * 1000))
# Decay learning rate exponentially
lr *= lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] *= lr_decay
epoch += 1
# Decay BatchNorm momentum
momentum = initial_momentum * np.exp(-epoch/args.epochs * np.log(initial_momentum/final_momentum))
model_pos_train.set_bn_momentum(momentum)
# Save checkpoint if necessary
if epoch % args.checkpoint_frequency == 0:
chk_path = os.path.join(args.checkpoint, 'epoch_{}.bin'.format(epoch))
print('Saving checkpoint to', chk_path)
torch.save({
'epoch': epoch,
'lr': lr,
'random_state': train_generator.random_state(),
'optimizer': optimizer.state_dict(),
'model_pos': model_pos_train.state_dict(),
'model_traj': None,
'random_state_semi': None,
}, chk_path)
# Save training curves after every epoch, as .png images (if requested)
if args.export_training_curves and epoch > 3:
if 'matplotlib' not in sys.modules:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure()
epoch_x = np.arange(3, len(losses_3d_train)) + 1
plt.plot(epoch_x, losses_3d_train[3:], '--', color='C0')
plt.plot(epoch_x, losses_3d_train_eval[3:], color='C0')
plt.plot(epoch_x, losses_3d_valid[3:], color='C1')
plt.legend(['3d train', '3d train (eval)', '3d valid (eval)'])
plt.ylabel('MPJPE (m)')
plt.xlabel('Epoch')
plt.xlim((3, epoch))
plt.savefig(os.path.join(args.checkpoint, 'loss_3d.png'))
def evaluate(test_generator, action=None, return_predictions=False, use_trajectory_model=False):
epoch_loss_3d_pos = 0
epoch_loss_3d_pos_procrustes = 0
epoch_loss_3d_pos_scale = 0
epoch_loss_3d_vel = 0
with torch.no_grad():
if not use_trajectory_model:
model_pos.eval()
else:
model_traj.eval()
N = 0
for _, batch, batch_2d in test_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_2d = inputs_2d.cuda()
# Positional model
if not use_trajectory_model:
predicted_3d_pos = model_pos(inputs_2d)
else:
predicted_3d_pos = model_traj(inputs_2d)
# Test-time augmentation (if enabled)
if test_generator.augment_enabled():
# Undo flipping and take average with non-flipped version
predicted_3d_pos[1, :, :, 0] *= -1
if not use_trajectory_model:
predicted_3d_pos[1, :, joints_left + joints_right] = \
predicted_3d_pos[1, :, joints_right + joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos, dim=0, keepdim=True)
if return_predictions:
return predicted_3d_pos.squeeze(0).cpu().numpy()
inputs_3d = torch.from_numpy(batch.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_3d[:, :, 0] = 0
if test_generator.augment_enabled():
inputs_3d = inputs_3d[:1]
error = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_pos_scale += \
inputs_3d.shape[0]*inputs_3d.shape[1] * n_mpjpe(predicted_3d_pos, inputs_3d).item()
epoch_loss_3d_pos += inputs_3d.shape[0]*inputs_3d.shape[1] * error.item()
N += inputs_3d.shape[0] * inputs_3d.shape[1]
inputs = inputs_3d.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
predicted_3d_pos = predicted_3d_pos.cpu().numpy()\
.reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
epoch_loss_3d_pos_procrustes += \
inputs_3d.shape[0]*inputs_3d.shape[1] * p_mpjpe(predicted_3d_pos, inputs)
# Compute velocity error
epoch_loss_3d_vel += \
inputs_3d.shape[0]*inputs_3d.shape[1] * mean_velocity_error(predicted_3d_pos, inputs)
if action is None:
print('----------')
else:
print('----'+action+'----')
e1 = (epoch_loss_3d_pos / N)*1000
e2 = (epoch_loss_3d_pos_procrustes / N)*1000
e3 = (epoch_loss_3d_pos_scale / N)*1000
ev = (epoch_loss_3d_vel / N)*1000
print('Test time augmentation:', test_generator.augment_enabled())
print('Protocol #1 Error (MPJPE):', e1, 'mm')
print('Protocol #2 Error (P-MPJPE):', e2, 'mm')
print('Protocol #3 Error (N-MPJPE):', e3, 'mm')
print('Velocity Error (MPJVE):', ev, 'mm')
print('----------')
return e1, e2, e3, ev
def fetch_actions(actions):
out_poses_3d = []
out_poses_2d = []
for subject, action in actions:
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
poses_3d = dataset[subject][action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
for i in range(len(poses_3d)): # Iterate across cameras
out_poses_3d.append(poses_3d[i])
stride = args.downsample
if stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_poses_3d, out_poses_2d
def run_evaluation(actions, action_filter=None):
errors_p1 = []
errors_p2 = []
errors_p3 = []
errors_vel = []
for action_key in actions.keys():
if action_filter is not None:
found = False
for a in action_filter:
if action_key.startswith(a):
found = True
break
if not found:
continue
poses_act, poses_2d_act = fetch_actions(actions[action_key])
gen = UnchunkedGenerator(None,
poses_act,
poses_2d_act,
pad=pad,
causal_shift=causal_shift,
augment=args.test_time_augmentation,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
e1, e2, e3, ev = evaluate(gen, action_key)
errors_p1.append(e1)
errors_p2.append(e2)
errors_p3.append(e3)
errors_vel.append(ev)
print('Protocol #1 (MPJPE) action-wise average:', round(np.mean(errors_p1), 1), 'mm')
print('Protocol #2 (P-MPJPE) action-wise average:', round(np.mean(errors_p2), 1), 'mm')
print('Protocol #3 (N-MPJPE) action-wise average:', round(np.mean(errors_p3), 1), 'mm')
print('Velocity Error (MPJVE) action-wise average:', round(np.mean(errors_vel), 2), 'mm')
print('Evaluating...')
all_actions = {}
all_actions_by_subject = {}
for subject in subjects_test:
if subject not in all_actions_by_subject:
all_actions_by_subject[subject] = {}
for action in dataset[subject].keys():
action_name = action.split(' ')[0]
if action_name not in all_actions:
all_actions[action_name] = []
if action_name not in all_actions_by_subject[subject]:
all_actions_by_subject[subject][action_name] = []
all_actions[action_name].append((subject, action))
all_actions_by_subject[subject][action_name].append((subject, action))
if not args.by_subject:
run_evaluation(all_actions, action_filter)
else:
for subject in all_actions_by_subject.keys():
print('Evaluating on subject', subject)
run_evaluation(all_actions_by_subject[subject], action_filter)
print('')
Render results
(See next notebook)
- Data loading
- -> RDD
- Data splitting
- Take one subject for held-out testing
- For the remaining subjects:
- Use at least 50% of data as unlabeled data
- Ensemble training
- Do 1 epoch of supervised training
- Generate pseudo-labels for unlabeled data
- Repeat
- Compare to training ensemble on only labeled data
Parts of this code are taken from VideoPose3D repository.
Copyright (c) 2018-present, Facebook, Inc.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
ls VideoPose3D/humaneva
| path | name | size | modificationTime |
|---|---|---|---|
| dbfs:/VideoPose3D/humaneva/data_2d_humaneva15_detectron_pt_coco.npz | data_2d_humaneva15_detectron_pt_coco.npz | 1.6876431e7 | 1.669130266e12 |
| dbfs:/VideoPose3D/humaneva/data_2d_humaneva15_gt.npz | data_2d_humaneva15_gt.npz | 2956013.0 | 1.669129922e12 |
| dbfs:/VideoPose3D/humaneva/data_3d_humaneva15.npz | data_3d_humaneva15.npz | 1578843.0 | 1.669129924e12 |
| dbfs:/VideoPose3D/humaneva/humaneva_cameras_extrinsic_params.npz | humaneva_cameras_extrinsic_params.npz | 1251.0 | 1.669130248e12 |
| dbfs:/VideoPose3D/humaneva/humaneva_cameras_intrinsic_params.npz | humaneva_cameras_intrinsic_params.npz | 548.0 | 1.669130248e12 |
| dbfs:/VideoPose3D/humaneva/humaneva_skeleton.npz | humaneva_skeleton.npz | 539.0 | 1.669130248e12 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import os
import sys
import errno
from time import time
import copy
import hashlib
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, writers
import subprocess as sp
from tqdm import tqdm
import shutil
assert(torch.cuda.is_available())
ROOTDIR = '/dbfs/VideoPose3D'
def wrap(func, *args, unsqueeze=False):
"""
Wrap a torch function so it can be called with NumPy arrays.
Input and return types are seamlessly converted.
"""
# Convert input types where applicable
args = list(args)
for i, arg in enumerate(args):
if type(arg) == np.ndarray:
args[i] = torch.from_numpy(arg)
if unsqueeze:
args[i] = args[i].unsqueeze(0)
result = func(*args)
# Convert output types where applicable
if isinstance(result, tuple):
result = list(result)
for i, res in enumerate(result):
if type(res) == torch.Tensor:
if unsqueeze:
res = res.squeeze(0)
result[i] = res.numpy()
return tuple(result)
elif type(result) == torch.Tensor:
if unsqueeze:
result = result.squeeze(0)
return result.numpy()
else:
return result
def qrot(q, v):
"""
Rotate vector(s) v about the rotation described by quaternion(s) q.
Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
where * denotes any number of dimensions.
Returns a tensor of shape (*, 3).
"""
assert q.shape[-1] == 4
assert v.shape[-1] == 3
assert q.shape[:-1] == v.shape[:-1]
qvec = q[..., 1:]
uv = torch.cross(qvec, v, dim=len(q.shape)-1)
uuv = torch.cross(qvec, uv, dim=len(q.shape)-1)
return (v + 2 * (q[..., :1] * uv + uuv))
def qinverse(q, inplace=False):
# We assume the quaternion to be normalized
if inplace:
q[..., 1:] *= -1
return q
else:
w = q[..., :1]
xyz = q[..., 1:]
return torch.cat((w, -xyz), dim=len(q.shape)-1)
def normalize_screen_coordinates(X, w, h):
assert X.shape[-1] == 2
# Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio
return X/w*2 - [1, h/w]
def image_coordinates(X, w, h):
assert X.shape[-1] == 2
# Reverse camera frame normalization
return (X + [1, h/w])*w/2
def world_to_camera(X, R, t):
Rt = wrap(qinverse, R) # Invert rotation
return wrap(qrot, np.tile(Rt, (*X.shape[:-1], 1)), X - t) # Rotate and translate
def camera_to_world(X, R, t):
return wrap(qrot, np.tile(R, (*X.shape[:-1], 1)), X) + t
class Skeleton:
def __init__(self, parents, joints_left, joints_right):
assert len(joints_left) == len(joints_right)
self._parents = np.array(parents)
self._joints_left = joints_left
self._joints_right = joints_right
self._compute_metadata()
def num_joints(self):
return len(self._parents)
def parents(self):
return self._parents
def has_children(self):
return self._has_children
def children(self):
return self._children
def remove_joints(self, joints_to_remove):
"""
Remove the joints specified in 'joints_to_remove'.
"""
valid_joints = []
for joint in range(len(self._parents)):
if joint not in joints_to_remove:
valid_joints.append(joint)
for i in range(len(self._parents)):
while self._parents[i] in joints_to_remove:
self._parents[i] = self._parents[self._parents[i]]
index_offsets = np.zeros(len(self._parents), dtype=int)
new_parents = []
for i, parent in enumerate(self._parents):
if i not in joints_to_remove:
new_parents.append(parent - index_offsets[parent])
else:
index_offsets[i:] += 1
self._parents = np.array(new_parents)
if self._joints_left is not None:
new_joints_left = []
for joint in self._joints_left:
if joint in valid_joints:
new_joints_left.append(joint - index_offsets[joint])
self._joints_left = new_joints_left
if self._joints_right is not None:
new_joints_right = []
for joint in self._joints_right:
if joint in valid_joints:
new_joints_right.append(joint - index_offsets[joint])
self._joints_right = new_joints_right
self._compute_metadata()
return valid_joints
def joints_left(self):
return self._joints_left
def joints_right(self):
return self._joints_right
def _compute_metadata(self):
self._has_children = np.zeros(len(self._parents)).astype(bool)
for i, parent in enumerate(self._parents):
if parent != -1:
self._has_children[parent] = True
self._children = []
for i, parent in enumerate(self._parents):
self._children.append([])
for i, parent in enumerate(self._parents):
if parent != -1:
self._children[parent].append(i)
class MocapDataSubset(torch.utils.data.Dataset):
def __init__(self, fps, skeleton):
self._skeleton = skeleton
self._fps = fps
self._data = None # Must be filled by subclass
self._cameras = None # Must be filled by subclass
self._data_list = None # Must be filled by subclass
self.receptive_field = 1 # Must be filled by subclass
def remove_joints(self, joints_to_remove):
kept_joints = self._skeleton.remove_joints(joints_to_remove)
for subject in self._data.keys():
for action in self._data[subject].keys():
s = self._data[subject][action]
if 'positions' in s:
s['positions'] = s['positions'][:, kept_joints]
def __getitem__(self, idx):
# idx = torch.randint(len(self._data_list),[1])
data = self._data_list[idx]
pos_3d = data['positions_3d'][0]
pos_2d = data['kps'][0]
i = torch.randint(pos_3d.shape[0] - self.receptive_field + 1, [1])
sample = {
'pos_2d': pos_2d[i:i+self.receptive_field],
'pos_3d': pos_3d[i+(self.receptive_field - 1)//2][None]
}
return sample
def __len__(self):
return 1280
def subjects(self):
return self._data.keys()
def fps(self):
return self._fps
def skeleton(self):
return self._skeleton
def cameras(self):
return self._cameras
def supports_semi_supervised(self):
# This method can be overridden
return False
humaneva_skeleton_data = np.load(f'{ROOTDIR}/humaneva/humaneva_skeleton.npz', allow_pickle=True)['data'].item()
humaneva_skeleton = Skeleton(parents=humaneva_skeleton_data['parents'],
joints_left=humaneva_skeleton_data['joints_left'],
joints_right=humaneva_skeleton_data['joints_right'])
humaneva_cameras_intrinsic_params = list(np.load(f'{ROOTDIR}/humaneva/humaneva_cameras_intrinsic_params.npz', allow_pickle=True)['data'])
humaneva_cameras_extrinsic_params = np.load(f'{ROOTDIR}/humaneva/humaneva_cameras_extrinsic_params.npz', allow_pickle=True)['data'].item()
class HumanEvaDataSubset(MocapDataSubset):
def __init__(self, path, keypoints_path,
subjects=None,
prefixes=['Train/', 'Validate/', 'Unlabeled/Train/', 'Unlabeled/Validate/', 'Unlabeled/'],
local_copy=None,
stride=1,
pad=0):
if local_copy is not None:
if not os.path.exists(local_copy):
os.makedirs(local_copy)
path_new = os.path.join(local_copy, os.path.split(path)[1])
keypoints_path_new = os.path.join(local_copy, os.path.split(keypoints_path)[1])
shutil.copyfile(path, path_new)
shutil.copyfile(keypoints_path, keypoints_path_new)
path = path_new
keypoints_path = keypoints_path_new
# print(path)
# print(keypoints_path)
super().__init__(fps=60, skeleton=humaneva_skeleton)
self._cameras = copy.deepcopy(humaneva_cameras_extrinsic_params)
subjects = self._cameras.keys() if subjects is None else subjects
for subject in subjects:
for i, cam in enumerate(self._cameras[subject]):
cam.update(humaneva_cameras_intrinsic_params[i])
for k, v in cam.items():
if k not in ['id', 'res_w', 'res_h']:
cam[k] = np.array(v, dtype='float32')
if 'translation' in cam:
cam['translation'] = cam['translation']/1000 # mm to meters
keys = list(self._cameras.keys())
for subject in keys:
cameras = self._cameras[subject]
if subject in subjects:
for prefix in prefixes:
self._cameras[prefix + subject] = cameras
del self._cameras[subject]
subjects = list(self._cameras.keys())
# Load serialized dataset
data = np.load(path, allow_pickle=True)['positions_3d'].item()
self._data = {}
print(data.keys())
for subject in subjects:
actions = data[subject]
self._data[subject] = {}
for action_name in actions.keys():
positions = actions[action_name]
positions_3d = []
for cam in self._cameras[subject]:
pos_3d = world_to_camera(positions, R=cam['orientation'], t=cam['translation'])
pos_3d[:, 1:] -= pos_3d[:, :1]
# Remove global offset, but keep trajectory in first position
positions_3d.append(pos_3d)
self._data[subject][action_name] = {
'subject': subject,
'action_name': action_name,
'positions': positions,
'positions_3d': positions_3d,
'cameras': self._cameras[subject],
}
keypoints = np.load(keypoints_path, allow_pickle=True)
keypoints_metadata = keypoints['metadata'].item()
keypoints_symmetry = keypoints_metadata['keypoints_symmetry']
kps_left, kps_right = list(keypoints_symmetry[0]), list(keypoints_symmetry[1])
joints_left, joints_right = list(self.skeleton().joints_left()), list(self.skeleton().joints_right())
keypoints = keypoints['positions_2d'].item()
for subject in subjects:
assert subject in keypoints, 'Subject {} is missing from the 2D detections dataset'.format(subject)
for action in self._data[subject].keys():
assert action in keypoints[subject], 'Action {} of subject {} is missing from the 2D detections dataset'.format(action, subject)
if 'positions_3d' not in self._data[subject][action]:
continue
for cam_idx in range(len(keypoints[subject][action])):
# We check for >= instead of == because some videos in H3.6M contain extra frames
mocap_length = self._data[subject][action]['positions_3d'][cam_idx].shape[0]
assert keypoints[subject][action][cam_idx].shape[0] >= mocap_length
if keypoints[subject][action][cam_idx].shape[0] > mocap_length:
# Shorten sequence
keypoints[subject][action][cam_idx] = keypoints[subject][action][cam_idx][:mocap_length]
assert len(keypoints[subject][action]) == len(self._data[subject][action]['positions_3d'])
keys = list(keypoints.keys())
for subject in keys:
if subject in subjects:
for action in keypoints[subject]:
for cam_idx, kps in enumerate(keypoints[subject][action]):
# Normalize camera frame
cam = self._cameras[subject][cam_idx]
kps[..., :2] = normalize_screen_coordinates(kps[..., :2], w=cam['res_w'], h=cam['res_h'])
keypoints[subject][action][cam_idx] = kps
if action in self._data[subject].keys():
self._data[subject][action]['kps'] = keypoints[subject][action]
else:
del keypoints[subject]
self._keypoints = keypoints
self.receptive_field = 2 * pad + 1
self._data_list = []
for subject in subjects:
for action_name in self._data[subject].keys():
if len(self._data[subject][action_name]['positions_3d'][0]) >= self.receptive_field:
self._data_list.append(self._data[subject][action_name])
class TemporalModelBase(nn.Module):
"""
Do not instantiate this class.
"""
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, causal, dropout, channels):
super().__init__()
# Validate input
for fw in filter_widths:
assert fw % 2 != 0, 'Only odd filter widths are supported'
self.num_joints_in = num_joints_in
self.in_features = in_features
self.num_joints_out = num_joints_out
self.filter_widths = filter_widths
self.drop = nn.Dropout(dropout)
self.relu = nn.ReLU(inplace=True)
self.pad = [ filter_widths[0] // 2 ]
self.expand_bn = nn.BatchNorm1d(channels, momentum=0.1)
self.shrink = nn.Conv1d(channels, num_joints_out*3, 1)
def set_bn_momentum(self, momentum):
self.expand_bn.momentum = momentum
for bn in self.layers_bn:
bn.momentum = momentum
def receptive_field(self):
"""
Return the total receptive field of this model as # of frames.
"""
frames = 0
for f in self.pad:
frames += f
return 1 + 2*frames
def total_causal_shift(self):
"""
Return the asymmetric offset for sequence padding.
The returned value is typically 0 if causal convolutions are disabled,
otherwise it is half the receptive field.
"""
frames = self.causal_shift[0]
next_dilation = self.filter_widths[0]
for i in range(1, len(self.filter_widths)):
frames += self.causal_shift[i] * next_dilation
next_dilation *= self.filter_widths[i]
return frames
def forward(self, x):
assert len(x.shape) == 4
assert x.shape[-2] == self.num_joints_in
assert x.shape[-1] == self.in_features
sz = x.shape[:3]
x = x.view(x.shape[0], x.shape[1], -1)
x = x.permute(0, 2, 1)
x = self._forward_blocks(x)
x = x.permute(0, 2, 1)
x = x.view(sz[0], -1, self.num_joints_out, 3)
return x
class TemporalModel(TemporalModelBase):
"""
Reference 3D pose estimation model with temporal convolutions.
This implementation can be used for all use-cases.
"""
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, causal=False, dropout=0.25, channels=1024, dense=False):
"""
Initialize this model.
Arguments:
num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
in_features -- number of input features for each joint (typically 2 for 2D input)
num_joints_out -- number of output joints (can be different than input)
filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
causal -- use causal convolutions instead of symmetric convolutions (for real-time applications)
dropout -- dropout probability
channels -- number of convolution channels
dense -- use regular dense convolutions instead of dilated convolutions (ablation experiment)
"""
super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
self.expand_conv = nn.Conv1d(num_joints_in*in_features, channels, filter_widths[0], bias=False)
layers_conv = []
layers_bn = []
self.causal_shift = [ (filter_widths[0]) // 2 if causal else 0 ]
next_dilation = filter_widths[0]
for i in range(1, len(filter_widths)):
self.pad.append((filter_widths[i] - 1)*next_dilation // 2)
self.causal_shift.append((filter_widths[i]//2 * next_dilation) if causal else 0)
layers_conv.append(nn.Conv1d(channels, channels,
filter_widths[i] if not dense else (2*self.pad[-1] + 1),
dilation=next_dilation if not dense else 1,
bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
next_dilation *= filter_widths[i]
self.layers_conv = nn.ModuleList(layers_conv)
self.layers_bn = nn.ModuleList(layers_bn)
def _forward_blocks(self, x):
x = self.drop(self.relu(self.expand_bn(self.expand_conv(x))))
for i in range(len(self.pad) - 1):
pad = self.pad[i+1]
shift = self.causal_shift[i+1]
res = x[:, :, pad + shift : x.shape[2] - pad + shift]
x = self.drop(self.relu(self.layers_bn[2*i](self.layers_conv[2*i](x))))
x = res + self.drop(self.relu(self.layers_bn[2*i + 1](self.layers_conv[2*i + 1](x))))
x = self.shrink(x)
return x
#constructing an instance of this class based on a state dictionary (network parameters)
@staticmethod
def from_state_dict(params,hyperparams):
net=TemporalModel(*hyperparams)
net.load_state_dict(params)
return net
def mpjpe(predicted, target):
"""
Mean per-joint position error (i.e. mean Euclidean distance),
often referred to as "Protocol #1" in many papers.
"""
assert predicted.shape == target.shape
return torch.mean(torch.norm(predicted - target, dim=len(target.shape)-1))
def weighted_mpjpe(predicted, target, w):
"""
Weighted mean per-joint position error (i.e. mean Euclidean distance)
"""
assert predicted.shape == target.shape
assert w.shape[0] == predicted.shape[0]
return torch.mean(w * torch.norm(predicted - target, dim=len(target.shape)-1))
def p_mpjpe(predicted, target):
"""
Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
often referred to as "Protocol #2" in many papers.
"""
assert predicted.shape == target.shape
muX = np.mean(target, axis=1, keepdims=True)
muY = np.mean(predicted, axis=1, keepdims=True)
X0 = target - muX
Y0 = predicted - muY
normX = np.sqrt(np.sum(X0**2, axis=(1, 2), keepdims=True))
normY = np.sqrt(np.sum(Y0**2, axis=(1, 2), keepdims=True))
X0 /= normX
Y0 /= normY
H = np.matmul(X0.transpose(0, 2, 1), Y0)
U, s, Vt = np.linalg.svd(H)
V = Vt.transpose(0, 2, 1)
R = np.matmul(V, U.transpose(0, 2, 1))
# Avoid improper rotations (reflections), i.e. rotations with det(R) = -1
sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
V[:, :, -1] *= sign_detR
s[:, -1] *= sign_detR.flatten()
R = np.matmul(V, U.transpose(0, 2, 1)) # Rotation
tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)
a = tr * normX / normY # Scale
t = muX - a*np.matmul(muY, R) # Translation
# Perform rigid transformation on the input
predicted_aligned = a*np.matmul(predicted, R) + t
# Return MPJPE
return np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape)-1))
def n_mpjpe(predicted, target):
"""
Normalized MPJPE (scale only), adapted from:
https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py
"""
assert predicted.shape == target.shape
norm_predicted = torch.mean(torch.sum(predicted**2, dim=3, keepdim=True), dim=2, keepdim=True)
norm_target = torch.mean(torch.sum(target*predicted, dim=3, keepdim=True), dim=2, keepdim=True)
scale = norm_target / norm_predicted
return mpjpe(scale * predicted, target)
def mean_velocity_error(predicted, target):
"""
Mean per-joint velocity error (i.e. mean Euclidean distance of the 1st derivative)
"""
assert predicted.shape == target.shape
velocity_predicted = np.diff(predicted, axis=0)
velocity_target = np.diff(target, axis=0)
return np.mean(np.linalg.norm(velocity_predicted - velocity_target, axis=len(target.shape)-1))
class Args:
# General arguments
dataset = 'humaneva' # target dataset # h36m or humaneva
keypoints = 'cpn_ft_h36m_dbb' # 2D detections to use
subjects_train = 'Train/S1,Train/S2,Train/S3' # 'S1,S5,S6,S7,S8' # training subjects separated by comma
subjects_test = 'Validate/S4' # 'S9,S11' # test subjects separated by comma
subjects_unlabeled = '' # unlabeled subjects separated by comma for self-supervision
actions = '*' #'Walk,Jog,Box' #'*' # actions to train/test on, separated by comma, or * for all
checkpoint = f'/{ROOTDIR}/checkpoint' # checkpoint directory
checkpoint_frequency = 10 # create a checkpoint every N epochs
resume = '' # checkpoint to resume (file name)
evaluate = '' # checkpoint to evaluate (file name)
render = False # visualize a particular video
by_subject = True # False # break down error by subject (on evaluation)
export_training_curves = False # save training curves as .png images
# Model arguments
stride = 1 # chunk size to use during training
epochs = 1000 # 60 # number of training epochs
batch_size = 128 # 1024 # batch size in terms of predicted frames
dropout = 0.25 # dropout probability
learning_rate = 0.001 # initial learning rate
lr_decay = 0.996 # 0.95 # learning rate decay per epoch
data_augmentation = True # disable train-time flipping
test_time_augmentation = True # disable test-time flipping
architecture = '3,3,3' # filter widths separated by comma
causal = False # use causal convolutions for real-time processing
channels = 1024 # number of channels in convolution layers
# Experimental
subset = 1 # reduce dataset size by fraction
downsample = 1 # downsample frame rate by factor (semi-supervised)
warmup = 1 # warm-up epochs for semi-supervision
no_eval = False # disable epoch evaluation while training (small speed-up)
dense = False # use dense convolutions instead of dilated convolutions
disable_optimizations = False # disable optimized model for single-frame predictions
linear_projection = False # use only linear coefficients for semi-supervised projection
bone_length = False # disable bone length term in semi-supervised settings
no_proj = False # disable projection for semi-supervised setting
bone_length_term = True
# Visualization
viz_subject = '' # subject to render
viz_action = '' # action to render
viz_camera = 0 # camera to render
viz_video = '' # path to input video
viz_skip = 0 # skip first N frames of input video
viz_output = '' # output file name (.gif or .mp4)
viz_export = '' # output file name for coordinates
viz_bitrate = 3000 # bitrate for mp4 videos
viz_no_ground_truth = False # do not show ground-truth poses
viz_limit = -1 # only render first N frames
viz_downsample = 1 # downsample FPS by a factor N
viz_size = 5 # image size
args = Args()
if args.resume and args.evaluate:
print('Invalid flags: --resume and --evaluate cannot be set at the same time')
exit()
if args.export_training_curves and args.no_eval:
print('Invalid flags: --export-training-curves and --no-eval cannot be set at the same time')
exit()
filter_widths = [int(x) for x in args.architecture.split(',')]
receptive_field = np.prod(filter_widths) # model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
if args.dataset.startswith('humaneva'):
dataset_path = f'{ROOTDIR}/{args.dataset}/data_3d_{args.dataset}15.npz'
keypoints_path = f'{ROOTDIR}/{args.dataset}/data_2d_{args.dataset}15_gt.npz'
print(keypoints_path, dataset_path)
train_dataset = HumanEvaDataSubset(dataset_path, keypoints_path, [s.replace('Train/','') for s in args.subjects_train.split(',')], prefixes=['Train/'], pad=pad)
val_dataset = HumanEvaDataSubset(dataset_path, keypoints_path, [s.replace('Validate/','') for s in args.subjects_test.split(',')], prefixes=['Validate/'], pad=pad)
else:
raise KeyError('Invalid dataset')
train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=True)
# Non-optimized version for non-single-frame preditictions
# Also the only possible option for stride > 1 and/or dense filters
#print(train_dataset.__getitem__(0)["pos_2d"].shape)
model_pos_train = TemporalModel(train_dataset.__getitem__(0)["pos_2d"].shape[-2],
train_dataset.__getitem__(0)["pos_2d"].shape[-1],
train_dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
model_pos = TemporalModel(train_dataset.__getitem__(0)["pos_2d"].shape[-2],
train_dataset.__getitem__(0)["pos_2d"].shape[-1],
train_dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
if args.causal:
print('INFO: Using causal convolutions')
causal_shift = pad
else:
causal_shift = 0
model_params = 0
for parameter in model_pos.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
model_pos_train = model_pos_train.cuda()
if args.resume or args.evaluate:
chk_filename = os.path.join(args.checkpoint, args.resume if args.resume else args.evaluate)
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
model_pos_train.load_state_dict(checkpoint['model_pos'])
model_pos.load_state_dict(checkpoint['model_pos'])
if args.evaluate and 'model_traj' in checkpoint:
# Load trajectory model if it contained in the checkpoint (e.g. for inference in the wild)
model_traj = TemporalModel(poses_valid_2d[0].shape[-2],
poses_valid_2d[0].shape[-1],
1,
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
if torch.cuda.is_available():
model_traj = model_traj.cuda()
model_traj.load_state_dict(checkpoint['model_traj'])
else:
model_traj = None
print('INFO: Testing on {} frames'.format(len(val_dataset)*args.batch_size))
if not args.evaluate:
# This is just to confirm we really want to (re-) train the model
# Otherwise this cell will be skipped
#cameras_train, poses_train, poses_train_2d = fetch(subjects_train, action_filter, subset=args.subset)
lr = args.learning_rate
optimizer = optim.Adam(model_pos_train.parameters(), lr=lr, amsgrad=True)
lr_decay = args.lr_decay
losses_3d_train = []
losses_3d_train_eval = []
losses_3d_valid = []
epoch = 0
initial_momentum = 0.1
final_momentum = 0.001
print('INFO: Training on {} frames'.format(len(train_dataset)*args.batch_size))
if args.resume:
epoch = checkpoint['epoch']
if 'optimizer' in checkpoint and checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
#train_generator.set_random_state(checkpoint['random_state'])
else:
print('WARNING: this checkpoint does not contain an optimizer state. The optimizer will be reinitialized.')
lr = checkpoint['lr']
print('** Note: reported losses are averaged over all frames and test-time augmentation is not used here.')
print('** The final evaluation will be carried out after the last training epoch.')
# Pos model only
while epoch < args.epochs:
start_time = time()
epoch_loss_3d_train = 0
epoch_loss_traj_train = 0
epoch_loss_2d_train_unlabeled = 0
N = 0
N_semi = 0
model_pos_train.train()
# Regular supervised scenario
for batch in tqdm(train_dataloader):
inputs_3d = batch['pos_3d']
inputs_2d = batch['pos_2d']
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_3d[:, :, 0] = 0
optimizer.zero_grad()
# Predict 3D poses
predicted_3d_pos = model_pos_train(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_train += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
loss_total = loss_3d_pos
loss_total.backward()
optimizer.step()
losses_3d_train.append(epoch_loss_3d_train / N)
# End-of-epoch evaluation
with torch.no_grad():
model_pos.load_state_dict(model_pos_train.state_dict())
model_pos.eval()
# if not args.no_eval:
# Evaluate on test set
epoch_loss_3d_valid = 0
epoch_loss_traj_valid = 0
epoch_loss_2d_valid = 0
N = 0
for batch in val_dataloader:
inputs_3d = batch['pos_3d']
inputs_2d = batch['pos_2d']
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
# Predict 3D poses
predicted_3d_pos = model_pos(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_valid += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
losses_3d_valid.append(epoch_loss_3d_valid / N)
# Evaluate on training set, this time in evaluation mode
epoch_loss_3d_train_eval = 0
epoch_loss_traj_train_eval = 0
epoch_loss_2d_train_labeled_eval = 0
N = 0
for batch in train_dataloader:
inputs_3d = batch['pos_3d']
inputs_2d = batch['pos_2d']
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
# Compute 3D poses
predicted_3d_pos = model_pos(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_train_eval += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
losses_3d_train_eval.append(epoch_loss_3d_train_eval / N)
# Calculate inference time
elapsed = (time() - start_time)/60
print('[%d] time %.2f lr %f 3d_train %f 3d_eval %f 3d_valid %f' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000,
losses_3d_train_eval[-1] * 1000,
losses_3d_valid[-1] * 1000))
# Decay learning rate exponentially
lr *= lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] *= lr_decay
epoch += 1
# Decay BatchNorm momentum
momentum = initial_momentum * np.exp(-epoch/args.epochs * np.log(initial_momentum/final_momentum))
model_pos_train.set_bn_momentum(momentum)
# Save checkpoint if necessary
if epoch % args.checkpoint_frequency == 0:
chk_path = os.path.join(args.checkpoint, 'epoch_{}.bin'.format(epoch))
print('Saving checkpoint to', chk_path)
torch.save({
'epoch': epoch,
'lr': lr,
#'random_state': train_generator.random_state(),
'optimizer': optimizer.state_dict(),
'model_pos': model_pos_train.state_dict(),
'model_traj': None,
'random_state_semi': None,
}, chk_path)
# Save training curves after every epoch, as .png images (if requested)
if args.export_training_curves and epoch > 3:
if 'matplotlib' not in sys.modules:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure()
epoch_x = np.arange(3, len(losses_3d_train)) + 1
plt.plot(epoch_x, losses_3d_train[3:], '--', color='C0')
plt.plot(epoch_x, losses_3d_train_eval[3:], color='C0')
plt.plot(epoch_x, losses_3d_valid[3:], color='C1')
plt.legend(['3d train', '3d train (eval)', '3d valid (eval)'])
plt.ylabel('MPJPE (m)')
plt.xlabel('Epoch')
plt.xlim((3, epoch))
plt.savefig(os.path.join(args.checkpoint, 'loss_3d.png'))
### Distributed Package
import horovod.torch as hvd
from sparkdl import HorovodRunner
from torch.utils.data.distributed import DistributedSampler
from horovod.spark.common.store import DBFSLocalStore
filter_widths = [int(x) for x in args.architecture.split(',')]
receptive_field = np.prod(filter_widths) # model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
if args.dataset.startswith('humaneva'):
dataset_path = f'{ROOTDIR}/{args.dataset}/data_3d_{args.dataset}15.npz'
keypoints_path = f'{ROOTDIR}/{args.dataset}/data_2d_{args.dataset}15_gt.npz'
print(keypoints_path, dataset_path)
train_dataset = HumanEvaDataSubset(dataset_path, keypoints_path, local_copy=f'data-{hvd.rank()}', subjects=[s.replace('Train/','') for s in args.subjects_train.split(',')], prefixes=['Train/'], pad=pad)
val_dataset = HumanEvaDataSubset(dataset_path, keypoints_path, [s.replace('Validate/','') for s in args.subjects_test.split(',')], prefixes=['Validate/'], pad=pad)
else:
raise KeyError('Invalid dataset')
# Setup store for intermediate datadiate data
store = DBFSLocalStore(work_dir)
# Load MNIST data from databricks-datasets
# So that this notebook can run quickly, this example uses the .limit() option. Using only limited data decreases the model's accuracy; remove this option for better accuracy.
train_df = spark.read.format("libsvm") \
.option('numFeatures', '784') \
.load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt") \
.limit(60).repartition(num_proc)
# Non-optimized version for non-single-frame preditictions
# Also the only possible option for stride > 1 and/or dense filters
#print(train_dataset.__getitem__(0)["pos_2d"].shape)
model_pos_train = TemporalModel(train_dataset.__getitem__(0)["pos_2d"].shape[-2],
train_dataset.__getitem__(0)["pos_2d"].shape[-1],
train_dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
model_pos = TemporalModel(train_dataset.__getitem__(0)["pos_2d"].shape[-2],
train_dataset.__getitem__(0)["pos_2d"].shape[-1],
train_dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
if args.causal:
print('INFO: Using causal convolutions')
causal_shift = pad
else:
causal_shift = 0
model_params = 0
for parameter in model_pos.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
model_pos_train = model_pos_train.cuda()
def train_hvd():
# Initialize Horovod
hvd.init()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type == 'cuda':
torch.cuda.set_device(hvd.local_rank())
train_sampler = DistributedSampler(train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size//args.stride, sampler=train_sampler)
## we did not use train_eval_loader##
train_eval_sampler = DistributedSampler(train_eval_dataset, num_replicas=hvd.size(), rank=hvd.rank())
train_eval_loader = torch.utils.data.DataLoader(train_eval_dataset, batch_size=1, sampler=train_eval_sampler)
print('INFO: Training on {} frames'.format(len(train_dataset)*args.batch_size))
# Model
model = model_pos_train.to(device)
lr = args.learning_rate
optimizer = optim.Adam(model.parameters(), lr=lr*hvd.size(), amsgrad=True)
# Wrap the local optimizer with hvd.DistributedOptimizer so that Horovod handles the distributed optimization
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
# Broadcast initial parameters so all workers start with the same parameters
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
lr_decay = args.lr_decay
#losses_3d_train = []
#losses_3d_train_eval = []
#losses_3d_valid = []
#epoch = 0
initial_momentum = 0.1
final_momentum = 0.001
#Pos model only
for epoch in range(1,args.epochs+1):
train_one_epoch(model, device, train_loader, optimizer, epoch)
if hvd.rank() == 0:
save_checkpoint(model,optimizer,epoch)
# Decay learning rate exponentially
lr *= lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] *= lr_decay
epoch += 1
# Decay BatchNorm momentum
momentum = initial_momentum * np.exp(-epoch/args.epochs * np.log(initial_momentum/final_momentum))
model.set_bn_momentum(momentum)
#Distributed training using Horovod runner
if not args.evaluate:
hr = HorovodRunner(np=2)
hr.run(train_hvd)
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
# Specify training parameters
batch_size = 100
num_epochs = 5
momentum = 0.5
log_interval = 100
def train_one_epoch(model, device, data_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(data_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(data_loader) * len(data),
100. * batch_idx / len(data_loader), loss.item()))
from time import time
import os
PYTORCH_DIR = '/dbfs/ml/horovod_pytorch'
LOG_DIR = os.path.join(PYTORCH_DIR, str(time()), 'MNISTDemo')
os.makedirs(LOG_DIR)
def save_checkpoint(model, optimizer, epoch):
filepath = LOG_DIR + '/checkpoint-{epoch}.pth.tar'.format(epoch=epoch)
state = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(state, filepath)
import torch.optim as optim
from torchvision import datasets, transforms
def train(learning_rate):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_dataset = datasets.MNIST('data',
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))]))
data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
for epoch in range(1, num_epochs + 1):
train_one_epoch(model, device, data_loader, optimizer, epoch)
save_checkpoint(model, optimizer, epoch)
# Runs in 49.65 seconds on 3 node GPU cluster
# Runs in 118.2 seconds on 3 node non-GPU cluster
train(learning_rate = 0.001)train(0.001)
import horovod.torch as hvd
from sparkdl import HorovodRunner
from torch.utils.data.distributed import DistributedSampler
def train_hvd(learning_rate):
# Initialize Horovod
hvd.init()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type == 'cuda':
# Pin GPU to local rank
torch.cuda.set_device(hvd.local_rank())
train_dataset = datasets.MNIST(
# Use different root directory for each worker to avoid conflicts
root='data-%d'% hvd.rank(),
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
)
# Configure the sampler so that each worker gets a distinct sample of the input dataset
train_sampler = DistributedSampler(train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
# Use train_sampler to load a different sample of data on each worker
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler)
model = Net().to(device)
# The effective batch size in synchronous distributed training is scaled by the number of workers
# Increase learning_rate to compensate for the increased batch size
optimizer = optim.SGD(model.parameters(), lr=learning_rate * hvd.size(), momentum=momentum)
# Wrap the local optimizer with hvd.DistributedOptimizer so that Horovod handles the distributed optimization
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
# Broadcast initial parameters so all workers start with the same parameters
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
for epoch in range(1, num_epochs + 1):
train_one_epoch(model, device, train_loader, optimizer, epoch)
# Save checkpoints only on worker 0 to prevent conflicts between workers
if hvd.rank() == 0:
save_checkpoint(model, optimizer, epoch)
# Runs in 51.63 seconds on 3 node GPU cluster
# Runs in 96.6 seconds on 3 node non-GPU cluster
hr = HorovodRunner(np=2)
hr.run(train_hvd, learning_rate = 0.001)
import horovod.torch as hvd
from sparkdl import HorovodRunner
from torch.utils.data.distributed import DistributedSampler
def train_hvd(dummy_var=1):
hvd.init()
print('data-%d'% hvd.rank())
if torch.cuda.is_available():
# Pin GPU to local rank
torch.cuda.set_device(hvd.local_rank())
filter_widths = [int(x) for x in args.architecture.split(',')]
receptive_field = np.prod(filter_widths) # model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
if args.dataset.startswith('humaneva'):
dataset_path = f'{ROOTDIR}/{args.dataset}/data_3d_{args.dataset}15.npz'
keypoints_path = f'{ROOTDIR}/{args.dataset}/data_2d_{args.dataset}15_gt.npz'
print(keypoints_path, dataset_path)
# !!! The problem is in the line below !!!
train_dataset = HumanEvaDataSubset(dataset_path, keypoints_path, local_copy=f'data-{hvd.rank()}', subjects=[s.replace('Train/','') for s in args.subjects_train.split(',')], prefixes=['Train/'], pad=pad)
else:
raise KeyError('Invalid dataset')
# train_sampler = DistributedSampler(train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
# train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_sampler)
# model_pos_train = TemporalModel(train_dataset.__getitem__(0)["pos_2d"].shape[-2],
# train_dataset.__getitem__(0)["pos_2d"].shape[-1],
# train_dataset.skeleton().num_joints(),
# filter_widths=filter_widths,
# causal=args.causal,
# dropout=args.dropout,
# channels=args.channels,
# dense=args.dense)
# if args.causal:
# print('INFO: Using causal convolutions')
# causal_shift = pad
# else:
# causal_shift = 0
# model_params = 0
# for parameter in model_pos_train.parameters():
# model_params += parameter.numel()
# print('INFO: Trainable parameter count:', model_params)
# if torch.cuda.is_available():
# model_pos_train = model_pos_train.cuda()
# # The effective batch size in synchronous distributed training is scaled by the number of workers
# # Increase learning_rate to compensate for the increased batch size
# lr = args.learning_rate
# optimizer = optim.Adam(model_pos_train.parameters(), lr=lr*hvd.size(), amsgrad=True)
# optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model_pos_train.named_parameters())
# hvd.broadcast_parameters(model_pos_train.state_dict(), root_rank=0)
# for epoch in range(1, num_epochs + 1):
# print(epoch)
# train_one_epoch(model, device, train_dataloader, optimizer, epoch)
# Save checkpoints only on worker 0 to prevent conflicts between workers
# if hvd.rank() == 0:
# save_checkpoint(model, optimizer, epoch)
hr = HorovodRunner(np=2)
hr.run(train_hvd, dummy_var=1)
print('data-%d'% hvd.rank())
import numpy as np
import torch
import pyspark.sql.functions as F
from pyspark.sql import Window
from pyspark.sql.functions import collect_list, size, udf
from pyspark.ml.feature import VectorAssembler
from pyspark.sql.types import BooleanType
from pyspark.sql.functions import udf
from itertools import groupby
from pyspark.rdd import PipelinedRDD
from pathlib import Path
import os
import matplotlib.pyplot as plt
from pyspark.sql.types import StructType, StringType, DoubleType, IntegerType
humaneva_train_path = "/VideoPose3D/humaneva/humaneva15_train.csv"
humaneva_test_path = "/VideoPose3D/humaneva/humaneva15_test.csv"
def load_data_from_csv(file_location):
"""Load and preprocess HumanEva data
Args:
file_location: file location from which to load the data
Returns:
df: spark DataFrame
"""
file_type = "csv"
infer_schema = "true"
first_row_is_header = False
delimiter = ","
schema = StructType() \
.add("Idx",IntegerType(),True) \
.add("Subject",StringType(),True) \
.add("Action",StringType(),True) \
.add("Camera",StringType(),True)
for i in range(15):
schema = schema.add(f"u{i}",DoubleType(),True).add(f"v{i}",DoubleType(),True)
for i in range(15):
schema = schema.add(f"X{i}",DoubleType(),True).add(f"Y{i}",DoubleType(),True).add(f"Z{i}",DoubleType(),True)
# Load the data from file
df = spark.read.csv(file_location, header=True, schema=schema, sep=',')
return df
df_train = load_data_from_csv(humaneva_train_path).withColumn("Group", F.concat_ws(', ', "Subject", "Action", "Camera")).drop("Subject", "Action", "Camera")
df_test = load_data_from_csv(humaneva_test_path).withColumn("Group", F.concat_ws(', ', "Subject", "Action", "Camera")).drop("Subject", "Action", "Camera")
feature_names = []
target_names = []
n_keypoints = 15
for i in range(n_keypoints):
feature_names.append("u{}".format(i))
feature_names.append("v{}".format(i))
target_names.append("X{}".format(i))
target_names.append("Y{}".format(i))
target_names.append("Z{}".format(i))
feature_assembler = VectorAssembler(inputCols=feature_names, outputCol="features") # merge u and v into a vector column.
target_assembler = VectorAssembler(inputCols=target_names, outputCol="targets")
def assemble_vectors(df):
df = feature_assembler.transform(df)
df = target_assembler.transform(df)
df = df.drop(*feature_names).drop(*target_names)
return df
df_train = assemble_vectors(df_train)
df_test = assemble_vectors(df_test)
receptive_field = 27
w = Window.orderBy("Idx").partitionBy(["Group"]).rowsBetween(Window.currentRow-receptive_field//2, Window.currentRow+receptive_field//2)
def create_receptive_fields(df):
df = df.withColumn("feature_sequence", collect_list("features").over(w))
df = df.withColumn("group_sequence", collect_list("Group").over(w))
df = df.filter(size(df.group_sequence) == receptive_field)
return df
df_train_receptive = create_receptive_fields(df_train).drop("features")
df_test_receptive = create_receptive_fields(df_test).drop("features")
from random import sample, seed
## find right random seed to compensate for the different size of each chunk
seed(0) # seed 0 gives ok split
chunks = df_train_receptive.select("Group").distinct().collect()
chunks = [x["Group"] for x in chunks]
num_chunks = len(chunks)
num_unlabeled = int(num_chunks*0.6)
unlabeled_chunks = sample(chunks, num_unlabeled)
labeled_chunks = [x for x in chunks if x not in unlabeled_chunks]
df_train_receptive_unlabeled = df_train_receptive.filter(df_train_receptive.Group.isin(unlabeled_chunks))
df_train_receptive_unlabeled = df_train_receptive_unlabeled.drop("targets")
df_train_receptive_labeled = df_train_receptive.filter(~df_train_receptive.Group.isin(unlabeled_chunks))
### We do not have targets for unlabelled dataset
def toTensorLabeled(x):
fs = x["feature_sequence"]
target = x["targets"]
feature_tensor = []
for f in fs:
feature_tensor.append(f)
xx = torch.tensor(feature_tensor,dtype=torch.float)
yy = torch.tensor(target,dtype=torch.float)
return xx.view(27, 15, 2), yy.view(1, 15, 3)
def toTensorUnlabeled(x):
fs = x["feature_sequence"]
feature_tensor = []
for f in fs:
feature_tensor.append(f)
xx = torch.tensor(feature_tensor, dtype=torch.float)
return xx.view(27, 15, 2)
labeled_tensor_rdd = df_train_receptive_labeled.rdd.map(toTensorLabeled)
unlabeled_tensor_rdd = df_train_receptive_unlabeled.rdd.map(toTensorUnlabeled)
test_tensor_rdd = df_test_receptive.rdd.map(toTensorLabeled)
labeled_full_size = labeled_tensor_rdd.count()
unlabeled_full_size = unlabeled_tensor_rdd.count()
test_full_size = test_tensor_rdd.count()
from torch import nn
class Args:
# Data arguments
num_joints = 15
# Model arguments
stride = 1 # chunk size to use during training
epochs = 10 # 100 # number of training epochs
batch_size = 128 # batch size in terms of predicted frames
dropout = 0.25 # dropout probability
learning_rate = 0.001 # initial learning rate
lr_decay = 0.996 # learning rate decay per epoch
data_augmentation = True # disable train-time flipping
test_time_augmentation = True # disable test-time flipping
architecture = '3,3,3' # filter widths separated by comma
channels = 1024 # number of channels in convolution layers
args = Args()
filter_widths = [int(x) for x in args.architecture.split(',')]
receptive_field = np.prod(filter_widths) # model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
hyperparams = [args.num_joints, 2, args.num_joints, filter_widths, args.dropout, args.channels]
class TemporalModelBase(nn.Module):
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, dropout, channels):
super().__init__()
# Validate input
for fw in filter_widths:
assert fw % 2 != 0, 'Only odd filter widths are supported'
self.num_joints_in = num_joints_in
self.in_features = in_features
self.num_joints_out = num_joints_out
self.filter_widths = filter_widths
self.drop = nn.Dropout(dropout)
self.relu = nn.ReLU(inplace=True)
self.pad = [ filter_widths[0] // 2 ]
self.expand_bn = nn.BatchNorm1d(channels, momentum=0.1)
self.shrink = nn.Conv1d(channels, num_joints_out*3, 1)
def set_bn_momentum(self, momentum):
self.expand_bn.momentum = momentum
for bn in self.layers_bn:
bn.momentum = momentum
def forward(self, pos2D):
assert len(pos2D.shape) == 4 # pos2D: B x 27 x 15 x 2
assert pos2D.shape[-2] == self.num_joints_in # 15
assert pos2D.shape[-1] == self.in_features # 2
sz = pos2D.shape[:3] # B x 27 x 15
pos2D = pos2D.view(pos2D.shape[0], pos2D.shape[1], -1) # B x 27 x 15 * 2
pos2D = pos2D.permute(0, 2, 1) # B x 15 * 2 x 27
pos3D = self._forward_blocks(pos2D)
pos3D = pos3D.permute(0, 2, 1)
pos3D = pos3D.view(sz[0], -1, self.num_joints_out, 3)
return pos3D
class TemporalModel(TemporalModelBase):
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, dropout=0.25, channels=1024):
"""
Reference 3D pose estimation model with temporal convolutions.Initialize this model.
Arg:
num_joints_in -- number of input joints (i.e. 15 for HumanEva-I)
in_features -- number of input features for each joint (typically 2 for 2D input)
num_joints_out -- number of output joints (can be different than input)
filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
dropout -- dropout probability
channels -- number of convolution channels
"""
super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, dropout, channels)
self.expand_conv = nn.Conv1d(num_joints_in*in_features, channels, filter_widths[0], bias=False)
layers_conv = []
layers_bn = []
next_dilation = filter_widths[0] # 3
for i in range(1, len(filter_widths)):
self.pad.append((filter_widths[i] - 1)*next_dilation // 2) # [1, 3, 9]
layers_conv.append(nn.Conv1d(channels, channels,
filter_widths[i],
dilation=next_dilation,
bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
next_dilation *= filter_widths[i] # 3, 9, 27
self.layers_conv = nn.ModuleList(layers_conv)
self.layers_bn = nn.ModuleList(layers_bn)
def _forward_blocks(self, pos2D):
# pos2D: B x 15 * 2 x 27
x = self.drop(self.relu(self.expand_bn(self.expand_conv(pos2D)))) # B x 1024 x 25
for i in range(len(self.pad) - 1):
pad = self.pad[i+1] # 3, 9
res = x[:, :, pad : x.shape[2] - pad] # B x 1024 x 19, B x 1024 x 1
x = self.drop(self.relu(self.layers_bn[2*i](self.layers_conv[2*i](x)))) # B x 1024 x 19, B x 1024 x 1
x = res + self.drop(self.relu(self.layers_bn[2*i + 1](self.layers_conv[2*i + 1](x))))
pos3D = self.shrink(x) # B x 15*3 x 1
return pos3D
@staticmethod
def from_state_dict(params, hyperparams):
net = TemporalModel(*hyperparams)
net.load_state_dict(params)
return net
def mpjpe(predicted, target):
"""
Mean per-joint position error (i.e. mean Euclidean distance),
often referred to as "Protocol #1" in many papers.
"""
assert predicted.shape == target.shape
return torch.mean(torch.norm(predicted - target, dim=len(target.shape)-1))
class DataSet(torch.utils.data.Dataset):
def __init__(self, pos2D, pos3D):
self.pos2D = pos2D # self.pos2D: B x 27 x 15 * 2
self.pos3D = pos3D # self.pos3D: B x 1 x 15 * 3
def __len__(self):
return self.pos2D.shape[0]
def __getitem__(self, ind):
pos2D = self.pos2D[ind] # pos2D: B x 27 x 15 * 2 -> 27 x 15 * 2
pos3D = self.pos3D[ind] # pos2D: B x 1 x 15 * 3 -> 1 x 15 * 2
return pos2D, pos3D
data_labeled = labeled_tensor_rdd.takeSample(False, labeled_full_size)
pos2D_labeled, pos3D_labeled = zip(*data_labeled)
pos2D_labeled, pos3D_labeled = torch.stack(pos2D_labeled), torch.stack(pos3D_labeled)
data_test = test_tensor_rdd.takeSample(False, test_full_size)
pos2D_test, pos3D_test = zip(*data_test)
pos2D_test, pos3D_test = torch.stack(pos2D_test), torch.stack(pos3D_test)
from tqdm import tqdm
def evaluate(models, pos2D, pos3D, args):
for model in models:
model.eval()
# dataset = DataSet(pos2D, pos3D)
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=False)
# loss_3d = 0
# N = 0
# for i, batch in enumerate(dataloader):
# print(f'{i}/{len(dataloader)}:')
# inputs_2d, inputs_3d = batch
# inputs_3d[:, :, 0] = 0
# # Predict 3D poses
# predicted_3d_pos = [model(inputs_2d) for model in models]
# predicted_3d_pos = sum(predicted_3d_pos) / len(predicted_3d_pos)
# # Calcuclate MPJPE loss
# loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
# loss_3d += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
# N += inputs_3d.shape[0]*inputs_3d.shape[1]
# print(f'\t{loss_3d_pos.item()}')
# break
with torch.no_grad():
inputs_2d, inputs_3d = pos2D, pos3D
predicted_3d_pos = [model(inputs_2d) for model in models]
predicted_3d_pos = sum(predicted_3d_pos) / len(predicted_3d_pos)
loss_3d = mpjpe(predicted_3d_pos, inputs_3d).item()
N = 1
print(f'\t{loss_3d}')
return loss_3d/N
def get_test_predictions(models, pos2D, args):
for model in models:
model.eval()
with torch.no_grad():
inputs_2d = pos2D
predicted_3d_pos = [model(inputs_2d) for model in models]
return predicted_3d_pos
pos2D_labeled.shape
#for i in [1, 2]:
# print(f'{i} model(s)')
# models = []
# for j in range(i):
# params_path = f'/dbfs/VideoPose3D/saved_models/humaneva/checkpoints/supervised/{i}_members_ensemble{j}_iter97.ckpt'
# params = torch.load(params_path, map_location=torch.device('cpu'))
# models.append(TemporalModel.from_state_dict(params, hyperparams))
# #evaluate(models, pos2D_labeled, pos3D_labeled, args)
# evaluate(models, pos2D_test, pos3D_test, args)
def eval_ensemble_size(n_members, iters):
test_scores = []
for i in iters:
models = []
for j in range(n_members):
params_path = '/dbfs/VideoPose3D/saved_models/humaneva/checkpoints/supervised/{}_members_ensemble{}_iter{}.ckpt'.format(n_members, j, i)
params = torch.load(params_path, map_location=torch.device("cpu"))
models.append(TemporalModel.from_state_dict(params, hyperparams))
test_score = evaluate(models, pos2D_test, pos3D_test, args)
test_scores.append(test_score)
return test_scores
x = list(range(0,100,5))
x.append(99)
#ensemble_sizes = [1, 2, 3]
ensemble_sizes = [5]
#test_scores_dict = {}
for size in ensemble_sizes:
test_scores_dict[size] = eval_ensemble_size(size, x)
for size, test_scores in test_scores_dict.items():
plt.plot(x, test_scores, label="Ensemble size: {}".format(size))
plt.ylabel("Test error")
plt.xlabel("Training iterations")
plt.legend()
plt.show()
def get_all_preds(iter_):
ensemble_sizes = [1, 2, 3, 5]
pairs = []
for size in ensemble_sizes:
for i in range(size):
pairs.append((size, i))
models = []
for i,j in pairs:
params_path = "/dbfs/VideoPose3D/saved_models/humaneva/checkpoints/supervised/{}_members_ensemble{}_iter{}.ckpt".format(i,j,iter_)
params = torch.load(params_path, map_location=torch.device("cpu"))
models.append(TemporalModel.from_state_dict(params, hyperparams))
test_preds = get_test_predictions(models, pos2D_test, args)
return test_preds
test_preds = get_all_preds(99)
from random import choices
def eval_last_iteration(test_preds):
n_samples = 30
sizes = np.arange(11)+1
avg_test_errors = []
test_stdvs = []
for size in sizes:
ensemble_errors = []
for s in n_samples:
preds_subset = choices(test_preds, k=size)
test_error = []
for t in preds_subset:
error = mpjpe(pos3D_test, t).item()
test_error.append(error)
ensemble_errors =
avg_test_errors.append(sum(test_error)/len(test_error))
test_stdvs.append(np.std(test_error))
return avg_test_errors, test_stdvs
mean_error, stdvs = eval_last_iteration(test_preds)
print(mean_error)
print(stdvs)
ls dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised
| path | name | size |
|---|---|---|
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter0.ckpt | 1_members_ensemble0_iter0.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter1.ckpt | 1_members_ensemble0_iter1.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter10.ckpt | 1_members_ensemble0_iter10.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter100.ckpt | 1_members_ensemble0_iter100.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter101.ckpt | 1_members_ensemble0_iter101.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter102.ckpt | 1_members_ensemble0_iter102.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter103.ckpt | 1_members_ensemble0_iter103.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter104.ckpt | 1_members_ensemble0_iter104.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter105.ckpt | 1_members_ensemble0_iter105.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter106.ckpt | 1_members_ensemble0_iter106.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter107.ckpt | 1_members_ensemble0_iter107.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter108.ckpt | 1_members_ensemble0_iter108.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter109.ckpt | 1_members_ensemble0_iter109.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter11.ckpt | 1_members_ensemble0_iter11.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter110.ckpt | 1_members_ensemble0_iter110.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter111.ckpt | 1_members_ensemble0_iter111.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter112.ckpt | 1_members_ensemble0_iter112.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter113.ckpt | 1_members_ensemble0_iter113.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter114.ckpt | 1_members_ensemble0_iter114.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter115.ckpt | 1_members_ensemble0_iter115.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter116.ckpt | 1_members_ensemble0_iter116.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter117.ckpt | 1_members_ensemble0_iter117.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter118.ckpt | 1_members_ensemble0_iter118.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter119.ckpt | 1_members_ensemble0_iter119.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter12.ckpt | 1_members_ensemble0_iter12.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter120.ckpt | 1_members_ensemble0_iter120.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter121.ckpt | 1_members_ensemble0_iter121.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter122.ckpt | 1_members_ensemble0_iter122.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter123.ckpt | 1_members_ensemble0_iter123.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter124.ckpt | 1_members_ensemble0_iter124.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter125.ckpt | 1_members_ensemble0_iter125.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter126.ckpt | 1_members_ensemble0_iter126.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter127.ckpt | 1_members_ensemble0_iter127.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter128.ckpt | 1_members_ensemble0_iter128.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter129.ckpt | 1_members_ensemble0_iter129.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter13.ckpt | 1_members_ensemble0_iter13.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter130.ckpt | 1_members_ensemble0_iter130.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter131.ckpt | 1_members_ensemble0_iter131.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter132.ckpt | 1_members_ensemble0_iter132.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter133.ckpt | 1_members_ensemble0_iter133.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter134.ckpt | 1_members_ensemble0_iter134.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter135.ckpt | 1_members_ensemble0_iter135.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter136.ckpt | 1_members_ensemble0_iter136.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter137.ckpt | 1_members_ensemble0_iter137.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter138.ckpt | 1_members_ensemble0_iter138.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter139.ckpt | 1_members_ensemble0_iter139.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter14.ckpt | 1_members_ensemble0_iter14.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter140.ckpt | 1_members_ensemble0_iter140.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter141.ckpt | 1_members_ensemble0_iter141.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter142.ckpt | 1_members_ensemble0_iter142.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter143.ckpt | 1_members_ensemble0_iter143.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter144.ckpt | 1_members_ensemble0_iter144.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter145.ckpt | 1_members_ensemble0_iter145.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter146.ckpt | 1_members_ensemble0_iter146.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter147.ckpt | 1_members_ensemble0_iter147.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter148.ckpt | 1_members_ensemble0_iter148.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter149.ckpt | 1_members_ensemble0_iter149.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter15.ckpt | 1_members_ensemble0_iter15.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter150.ckpt | 1_members_ensemble0_iter150.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter151.ckpt | 1_members_ensemble0_iter151.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter152.ckpt | 1_members_ensemble0_iter152.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter54.ckpt | 1_members_ensemble0_iter54.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter55.ckpt | 1_members_ensemble0_iter55.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter56.ckpt | 1_members_ensemble0_iter56.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter57.ckpt | 1_members_ensemble0_iter57.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter58.ckpt | 1_members_ensemble0_iter58.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter59.ckpt | 1_members_ensemble0_iter59.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter6.ckpt | 1_members_ensemble0_iter6.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter60.ckpt | 1_members_ensemble0_iter60.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter61.ckpt | 1_members_ensemble0_iter61.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter62.ckpt | 1_members_ensemble0_iter62.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter63.ckpt | 1_members_ensemble0_iter63.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter64.ckpt | 1_members_ensemble0_iter64.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter65.ckpt | 1_members_ensemble0_iter65.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter66.ckpt | 1_members_ensemble0_iter66.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter67.ckpt | 1_members_ensemble0_iter67.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter68.ckpt | 1_members_ensemble0_iter68.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter7.ckpt | 1_members_ensemble0_iter7.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter70.ckpt | 1_members_ensemble0_iter70.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter90.ckpt | 1_members_ensemble0_iter90.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter0.ckpt | 2_members_ensemble0_iter0.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter78.ckpt | 2_members_ensemble0_iter78.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter79.ckpt | 2_members_ensemble0_iter79.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter8.ckpt | 2_members_ensemble0_iter8.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter80.ckpt | 2_members_ensemble0_iter80.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter81.ckpt | 2_members_ensemble0_iter81.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter82.ckpt | 2_members_ensemble0_iter82.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter83.ckpt | 2_members_ensemble0_iter83.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter84.ckpt | 2_members_ensemble0_iter84.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter85.ckpt | 2_members_ensemble0_iter85.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter86.ckpt | 2_members_ensemble0_iter86.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter87.ckpt | 2_members_ensemble0_iter87.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter88.ckpt | 2_members_ensemble0_iter88.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter89.ckpt | 2_members_ensemble0_iter89.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter9.ckpt | 2_members_ensemble0_iter9.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter90.ckpt | 2_members_ensemble0_iter90.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter91.ckpt | 2_members_ensemble0_iter91.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter0.ckpt | 2_members_ensemble1_iter0.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter1.ckpt | 2_members_ensemble1_iter1.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter0.ckpt | 3_members_ensemble0_iter0.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter12.ckpt | 3_members_ensemble0_iter12.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter13.ckpt | 3_members_ensemble0_iter13.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter14.ckpt | 3_members_ensemble0_iter14.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter15.ckpt | 3_members_ensemble0_iter15.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter16.ckpt | 3_members_ensemble0_iter16.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter17.ckpt | 3_members_ensemble0_iter17.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter18.ckpt | 3_members_ensemble0_iter18.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter19.ckpt | 3_members_ensemble0_iter19.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter2.ckpt | 3_members_ensemble0_iter2.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter20.ckpt | 3_members_ensemble0_iter20.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter21.ckpt | 3_members_ensemble0_iter21.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter22.ckpt | 3_members_ensemble0_iter22.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter23.ckpt | 3_members_ensemble0_iter23.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter24.ckpt | 3_members_ensemble0_iter24.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter25.ckpt | 3_members_ensemble0_iter25.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter26.ckpt | 3_members_ensemble0_iter26.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter27.ckpt | 3_members_ensemble0_iter27.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter28.ckpt | 3_members_ensemble0_iter28.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter29.ckpt | 3_members_ensemble0_iter29.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter3.ckpt | 3_members_ensemble0_iter3.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter30.ckpt | 3_members_ensemble0_iter30.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter31.ckpt | 3_members_ensemble0_iter31.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter32.ckpt | 3_members_ensemble0_iter32.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter33.ckpt | 3_members_ensemble0_iter33.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter34.ckpt | 3_members_ensemble0_iter34.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter35.ckpt | 3_members_ensemble0_iter35.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter36.ckpt | 3_members_ensemble0_iter36.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter37.ckpt | 3_members_ensemble0_iter37.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter38.ckpt | 3_members_ensemble0_iter38.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter39.ckpt | 3_members_ensemble0_iter39.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter4.ckpt | 3_members_ensemble0_iter4.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter40.ckpt | 3_members_ensemble0_iter40.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter41.ckpt | 3_members_ensemble0_iter41.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter42.ckpt | 3_members_ensemble0_iter42.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter43.ckpt | 3_members_ensemble0_iter43.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter44.ckpt | 3_members_ensemble0_iter44.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter45.ckpt | 3_members_ensemble0_iter45.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter46.ckpt | 3_members_ensemble0_iter46.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter47.ckpt | 3_members_ensemble0_iter47.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter48.ckpt | 3_members_ensemble0_iter48.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter49.ckpt | 3_members_ensemble0_iter49.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter5.ckpt | 3_members_ensemble0_iter5.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter50.ckpt | 3_members_ensemble0_iter50.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter51.ckpt | 3_members_ensemble0_iter51.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter52.ckpt | 3_members_ensemble0_iter52.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter53.ckpt | 3_members_ensemble0_iter53.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter54.ckpt | 3_members_ensemble0_iter54.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter55.ckpt | 3_members_ensemble0_iter55.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter56.ckpt | 3_members_ensemble0_iter56.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter57.ckpt | 3_members_ensemble0_iter57.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter58.ckpt | 3_members_ensemble0_iter58.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter59.ckpt | 3_members_ensemble0_iter59.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter6.ckpt | 3_members_ensemble0_iter6.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter60.ckpt | 3_members_ensemble0_iter60.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter61.ckpt | 3_members_ensemble0_iter61.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter62.ckpt | 3_members_ensemble0_iter62.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter63.ckpt | 3_members_ensemble0_iter63.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter64.ckpt | 3_members_ensemble0_iter64.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter65.ckpt | 3_members_ensemble0_iter65.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter66.ckpt | 3_members_ensemble0_iter66.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter67.ckpt | 3_members_ensemble0_iter67.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter68.ckpt | 3_members_ensemble0_iter68.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter69.ckpt | 3_members_ensemble0_iter69.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter7.ckpt | 3_members_ensemble0_iter7.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter70.ckpt | 3_members_ensemble0_iter70.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter71.ckpt | 3_members_ensemble0_iter71.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter72.ckpt | 3_members_ensemble0_iter72.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter73.ckpt | 3_members_ensemble0_iter73.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter74.ckpt | 3_members_ensemble0_iter74.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter75.ckpt | 3_members_ensemble0_iter75.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter76.ckpt | 3_members_ensemble0_iter76.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter77.ckpt | 3_members_ensemble0_iter77.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter78.ckpt | 3_members_ensemble0_iter78.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter79.ckpt | 3_members_ensemble0_iter79.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter8.ckpt | 3_members_ensemble0_iter8.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter80.ckpt | 3_members_ensemble0_iter80.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter81.ckpt | 3_members_ensemble0_iter81.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter82.ckpt | 3_members_ensemble0_iter82.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter83.ckpt | 3_members_ensemble0_iter83.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter84.ckpt | 3_members_ensemble0_iter84.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter86.ckpt | 3_members_ensemble0_iter86.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter87.ckpt | 3_members_ensemble0_iter87.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter88.ckpt | 3_members_ensemble0_iter88.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter89.ckpt | 3_members_ensemble0_iter89.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter9.ckpt | 3_members_ensemble0_iter9.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter90.ckpt | 3_members_ensemble0_iter90.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter91.ckpt | 3_members_ensemble0_iter91.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter93.ckpt | 3_members_ensemble0_iter93.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter95.ckpt | 3_members_ensemble0_iter95.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter96.ckpt | 3_members_ensemble0_iter96.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter97.ckpt | 3_members_ensemble0_iter97.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter98.ckpt | 3_members_ensemble0_iter98.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter99.ckpt | 3_members_ensemble0_iter99.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter0.ckpt | 3_members_ensemble1_iter0.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter1.ckpt | 3_members_ensemble1_iter1.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter10.ckpt | 3_members_ensemble1_iter10.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter12.ckpt | 3_members_ensemble1_iter12.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter14.ckpt | 3_members_ensemble1_iter14.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter15.ckpt | 3_members_ensemble1_iter15.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter16.ckpt | 3_members_ensemble1_iter16.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter17.ckpt | 3_members_ensemble1_iter17.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter18.ckpt | 3_members_ensemble1_iter18.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter19.ckpt | 3_members_ensemble1_iter19.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter2.ckpt | 3_members_ensemble1_iter2.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter30.ckpt | 3_members_ensemble1_iter30.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter32.ckpt | 3_members_ensemble1_iter32.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter38.ckpt | 3_members_ensemble1_iter38.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter39.ckpt | 3_members_ensemble1_iter39.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter4.ckpt | 3_members_ensemble1_iter4.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter40.ckpt | 3_members_ensemble1_iter40.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter41.ckpt | 3_members_ensemble1_iter41.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter42.ckpt | 3_members_ensemble1_iter42.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter43.ckpt | 3_members_ensemble1_iter43.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter44.ckpt | 3_members_ensemble1_iter44.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter45.ckpt | 3_members_ensemble1_iter45.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter46.ckpt | 3_members_ensemble1_iter46.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter47.ckpt | 3_members_ensemble1_iter47.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter48.ckpt | 3_members_ensemble1_iter48.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter49.ckpt | 3_members_ensemble1_iter49.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter5.ckpt | 3_members_ensemble1_iter5.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter50.ckpt | 3_members_ensemble1_iter50.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter51.ckpt | 3_members_ensemble1_iter51.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter53.ckpt | 3_members_ensemble1_iter53.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter63.ckpt | 3_members_ensemble2_iter63.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter64.ckpt | 3_members_ensemble2_iter64.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter65.ckpt | 3_members_ensemble2_iter65.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter66.ckpt | 3_members_ensemble2_iter66.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter67.ckpt | 3_members_ensemble2_iter67.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter68.ckpt | 3_members_ensemble2_iter68.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter69.ckpt | 3_members_ensemble2_iter69.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter7.ckpt | 3_members_ensemble2_iter7.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter70.ckpt | 3_members_ensemble2_iter70.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter71.ckpt | 3_members_ensemble2_iter71.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter72.ckpt | 3_members_ensemble2_iter72.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter73.ckpt | 3_members_ensemble2_iter73.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter74.ckpt | 3_members_ensemble2_iter74.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter75.ckpt | 3_members_ensemble2_iter75.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter8.ckpt | 3_members_ensemble2_iter8.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter80.ckpt | 3_members_ensemble2_iter80.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter9.ckpt | 3_members_ensemble2_iter9.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter90.ckpt | 3_members_ensemble2_iter90.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter0.ckpt | 5_members_ensemble0_iter0.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter1.ckpt | 5_members_ensemble0_iter1.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter10.ckpt | 5_members_ensemble0_iter10.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter11.ckpt | 5_members_ensemble0_iter11.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter12.ckpt | 5_members_ensemble0_iter12.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter15.ckpt | 5_members_ensemble0_iter15.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter16.ckpt | 5_members_ensemble0_iter16.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter19.ckpt | 5_members_ensemble0_iter19.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter2.ckpt | 5_members_ensemble0_iter2.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter5.ckpt | 5_members_ensemble0_iter5.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter60.ckpt | 5_members_ensemble0_iter60.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter65.ckpt | 5_members_ensemble0_iter65.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter66.ckpt | 5_members_ensemble0_iter66.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter68.ckpt | 5_members_ensemble0_iter68.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter69.ckpt | 5_members_ensemble0_iter69.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter7.ckpt | 5_members_ensemble0_iter7.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter70.ckpt | 5_members_ensemble0_iter70.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter71.ckpt | 5_members_ensemble0_iter71.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter8.ckpt | 5_members_ensemble0_iter8.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter80.ckpt | 5_members_ensemble0_iter80.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter83.ckpt | 5_members_ensemble0_iter83.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter84.ckpt | 5_members_ensemble0_iter84.ckpt | 3.4199173e7 |
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| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter0.ckpt | 5_members_ensemble1_iter0.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter1.ckpt | 5_members_ensemble1_iter1.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter10.ckpt | 5_members_ensemble1_iter10.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter11.ckpt | 5_members_ensemble1_iter11.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter12.ckpt | 5_members_ensemble1_iter12.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter13.ckpt | 5_members_ensemble1_iter13.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter14.ckpt | 5_members_ensemble1_iter14.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter15.ckpt | 5_members_ensemble1_iter15.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter16.ckpt | 5_members_ensemble1_iter16.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter17.ckpt | 5_members_ensemble1_iter17.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter18.ckpt | 5_members_ensemble1_iter18.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter19.ckpt | 5_members_ensemble1_iter19.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter2.ckpt | 5_members_ensemble1_iter2.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter20.ckpt | 5_members_ensemble1_iter20.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter21.ckpt | 5_members_ensemble1_iter21.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter22.ckpt | 5_members_ensemble1_iter22.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter23.ckpt | 5_members_ensemble1_iter23.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter24.ckpt | 5_members_ensemble1_iter24.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter25.ckpt | 5_members_ensemble1_iter25.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter26.ckpt | 5_members_ensemble1_iter26.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter27.ckpt | 5_members_ensemble1_iter27.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter28.ckpt | 5_members_ensemble1_iter28.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter29.ckpt | 5_members_ensemble1_iter29.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter3.ckpt | 5_members_ensemble1_iter3.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter30.ckpt | 5_members_ensemble1_iter30.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter31.ckpt | 5_members_ensemble1_iter31.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter32.ckpt | 5_members_ensemble1_iter32.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter33.ckpt | 5_members_ensemble1_iter33.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter34.ckpt | 5_members_ensemble1_iter34.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter35.ckpt | 5_members_ensemble1_iter35.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter36.ckpt | 5_members_ensemble1_iter36.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter37.ckpt | 5_members_ensemble1_iter37.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter38.ckpt | 5_members_ensemble1_iter38.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter39.ckpt | 5_members_ensemble1_iter39.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter4.ckpt | 5_members_ensemble1_iter4.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter40.ckpt | 5_members_ensemble1_iter40.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter41.ckpt | 5_members_ensemble1_iter41.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter42.ckpt | 5_members_ensemble1_iter42.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter43.ckpt | 5_members_ensemble1_iter43.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter44.ckpt | 5_members_ensemble1_iter44.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter45.ckpt | 5_members_ensemble1_iter45.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter46.ckpt | 5_members_ensemble1_iter46.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter47.ckpt | 5_members_ensemble1_iter47.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter48.ckpt | 5_members_ensemble1_iter48.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter49.ckpt | 5_members_ensemble1_iter49.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter5.ckpt | 5_members_ensemble1_iter5.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter50.ckpt | 5_members_ensemble1_iter50.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter51.ckpt | 5_members_ensemble1_iter51.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter52.ckpt | 5_members_ensemble1_iter52.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter53.ckpt | 5_members_ensemble1_iter53.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter54.ckpt | 5_members_ensemble1_iter54.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter55.ckpt | 5_members_ensemble1_iter55.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter56.ckpt | 5_members_ensemble1_iter56.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter57.ckpt | 5_members_ensemble1_iter57.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter58.ckpt | 5_members_ensemble1_iter58.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter59.ckpt | 5_members_ensemble1_iter59.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter6.ckpt | 5_members_ensemble1_iter6.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter60.ckpt | 5_members_ensemble1_iter60.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter61.ckpt | 5_members_ensemble1_iter61.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter62.ckpt | 5_members_ensemble1_iter62.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter63.ckpt | 5_members_ensemble1_iter63.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter64.ckpt | 5_members_ensemble1_iter64.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter65.ckpt | 5_members_ensemble1_iter65.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter66.ckpt | 5_members_ensemble1_iter66.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter67.ckpt | 5_members_ensemble1_iter67.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter68.ckpt | 5_members_ensemble1_iter68.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter69.ckpt | 5_members_ensemble1_iter69.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter7.ckpt | 5_members_ensemble1_iter7.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter70.ckpt | 5_members_ensemble1_iter70.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter71.ckpt | 5_members_ensemble1_iter71.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter72.ckpt | 5_members_ensemble1_iter72.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter73.ckpt | 5_members_ensemble1_iter73.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter74.ckpt | 5_members_ensemble1_iter74.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter75.ckpt | 5_members_ensemble1_iter75.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter76.ckpt | 5_members_ensemble1_iter76.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter77.ckpt | 5_members_ensemble1_iter77.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter78.ckpt | 5_members_ensemble1_iter78.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter79.ckpt | 5_members_ensemble1_iter79.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter8.ckpt | 5_members_ensemble1_iter8.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter80.ckpt | 5_members_ensemble1_iter80.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter81.ckpt | 5_members_ensemble1_iter81.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter82.ckpt | 5_members_ensemble1_iter82.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter83.ckpt | 5_members_ensemble1_iter83.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter84.ckpt | 5_members_ensemble1_iter84.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter85.ckpt | 5_members_ensemble1_iter85.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter86.ckpt | 5_members_ensemble1_iter86.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter87.ckpt | 5_members_ensemble1_iter87.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter9.ckpt | 5_members_ensemble1_iter9.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter0.ckpt | 5_members_ensemble2_iter0.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter1.ckpt | 5_members_ensemble2_iter1.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter10.ckpt | 5_members_ensemble2_iter10.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter11.ckpt | 5_members_ensemble2_iter11.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter12.ckpt | 5_members_ensemble2_iter12.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter13.ckpt | 5_members_ensemble2_iter13.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter14.ckpt | 5_members_ensemble2_iter14.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter15.ckpt | 5_members_ensemble2_iter15.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter16.ckpt | 5_members_ensemble2_iter16.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter17.ckpt | 5_members_ensemble2_iter17.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter18.ckpt | 5_members_ensemble2_iter18.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter19.ckpt | 5_members_ensemble2_iter19.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter2.ckpt | 5_members_ensemble2_iter2.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter20.ckpt | 5_members_ensemble2_iter20.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter21.ckpt | 5_members_ensemble2_iter21.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter22.ckpt | 5_members_ensemble2_iter22.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter23.ckpt | 5_members_ensemble2_iter23.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter24.ckpt | 5_members_ensemble2_iter24.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter25.ckpt | 5_members_ensemble2_iter25.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter26.ckpt | 5_members_ensemble2_iter26.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter27.ckpt | 5_members_ensemble2_iter27.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter28.ckpt | 5_members_ensemble2_iter28.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter29.ckpt | 5_members_ensemble2_iter29.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter3.ckpt | 5_members_ensemble2_iter3.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter30.ckpt | 5_members_ensemble2_iter30.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter31.ckpt | 5_members_ensemble2_iter31.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter32.ckpt | 5_members_ensemble2_iter32.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter33.ckpt | 5_members_ensemble2_iter33.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter34.ckpt | 5_members_ensemble2_iter34.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter35.ckpt | 5_members_ensemble2_iter35.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter36.ckpt | 5_members_ensemble2_iter36.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter37.ckpt | 5_members_ensemble2_iter37.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter38.ckpt | 5_members_ensemble2_iter38.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter39.ckpt | 5_members_ensemble2_iter39.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter4.ckpt | 5_members_ensemble2_iter4.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter40.ckpt | 5_members_ensemble2_iter40.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter41.ckpt | 5_members_ensemble2_iter41.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter42.ckpt | 5_members_ensemble2_iter42.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter43.ckpt | 5_members_ensemble2_iter43.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter44.ckpt | 5_members_ensemble2_iter44.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter45.ckpt | 5_members_ensemble2_iter45.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter46.ckpt | 5_members_ensemble2_iter46.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter47.ckpt | 5_members_ensemble2_iter47.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter48.ckpt | 5_members_ensemble2_iter48.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter49.ckpt | 5_members_ensemble2_iter49.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter5.ckpt | 5_members_ensemble2_iter5.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter50.ckpt | 5_members_ensemble2_iter50.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter51.ckpt | 5_members_ensemble2_iter51.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter52.ckpt | 5_members_ensemble2_iter52.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter53.ckpt | 5_members_ensemble2_iter53.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter54.ckpt | 5_members_ensemble2_iter54.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter55.ckpt | 5_members_ensemble2_iter55.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter56.ckpt | 5_members_ensemble2_iter56.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter57.ckpt | 5_members_ensemble2_iter57.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter58.ckpt | 5_members_ensemble2_iter58.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter59.ckpt | 5_members_ensemble2_iter59.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter6.ckpt | 5_members_ensemble2_iter6.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter60.ckpt | 5_members_ensemble2_iter60.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter61.ckpt | 5_members_ensemble2_iter61.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter62.ckpt | 5_members_ensemble2_iter62.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter63.ckpt | 5_members_ensemble2_iter63.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter64.ckpt | 5_members_ensemble2_iter64.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter65.ckpt | 5_members_ensemble2_iter65.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter66.ckpt | 5_members_ensemble2_iter66.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter67.ckpt | 5_members_ensemble2_iter67.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter68.ckpt | 5_members_ensemble2_iter68.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter69.ckpt | 5_members_ensemble2_iter69.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter7.ckpt | 5_members_ensemble2_iter7.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter70.ckpt | 5_members_ensemble2_iter70.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter71.ckpt | 5_members_ensemble2_iter71.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter72.ckpt | 5_members_ensemble2_iter72.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter73.ckpt | 5_members_ensemble2_iter73.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter74.ckpt | 5_members_ensemble2_iter74.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter75.ckpt | 5_members_ensemble2_iter75.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter76.ckpt | 5_members_ensemble2_iter76.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter77.ckpt | 5_members_ensemble2_iter77.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter78.ckpt | 5_members_ensemble2_iter78.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter79.ckpt | 5_members_ensemble2_iter79.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter8.ckpt | 5_members_ensemble2_iter8.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter80.ckpt | 5_members_ensemble2_iter80.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter81.ckpt | 5_members_ensemble2_iter81.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter82.ckpt | 5_members_ensemble2_iter82.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter83.ckpt | 5_members_ensemble2_iter83.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter84.ckpt | 5_members_ensemble2_iter84.ckpt | 3.4199173e7 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter85.ckpt | 5_members_ensemble2_iter85.ckpt | 3.4199173e7 |
def render_animation(keypoints, keypoints_metadata, poses, skeleton, fps, bitrate, azim, output, viewport,
limit=-1, downsample=1, size=6, input_video_path=None, input_video_skip=0):
"""
TODO
Render an animation. The supported output modes are:
-- 'interactive': display an interactive figure
(also works on notebooks if associated with %matplotlib inline)
-- 'html': render the animation as HTML5 video. Can be displayed in a notebook using HTML(...).
-- 'filename.mp4': render and export the animation as an h264 video (requires ffmpeg).
-- 'filename.gif': render and export the animation a gif file (requires imagemagick).
"""
plt.ioff()
fig = plt.figure(figsize=(size*(1 + len(poses)), size))
ax_in = fig.add_subplot(1, 1 + len(poses), 1)
ax_in.get_xaxis().set_visible(False)
ax_in.get_yaxis().set_visible(False)
ax_in.set_axis_off()
ax_in.set_title('Input')
ax_3d = []
lines_3d = []
trajectories = []
radius = 1.7
for index, (title, data) in enumerate(poses.items()):
ax = fig.add_subplot(1, 1 + len(poses), index+2, projection='3d')
ax.view_init(elev=15., azim=azim)
ax.set_xlim3d([-radius/2, radius/2])
ax.set_zlim3d([0, radius])
ax.set_ylim3d([-radius/2, radius/2])
try:
ax.set_aspect('equal')
except NotImplementedError:
ax.set_aspect('auto')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.dist = 7.5
ax.set_title(title) #, pad=35
ax_3d.append(ax)
lines_3d.append([])
trajectories.append(data[:, 0, [0, 1]])
poses = list(poses.values())
# Decode video
if input_video_path is None:
# Black background
all_frames = np.zeros((keypoints.shape[0], viewport[1], viewport[0]), dtype='uint8')
else:
# Load video using ffmpeg
all_frames = []
for f in read_video(input_video_path, skip=input_video_skip, limit=limit):
all_frames.append(f)
effective_length = min(keypoints.shape[0], len(all_frames))
all_frames = all_frames[:effective_length]
keypoints = keypoints[input_video_skip:] # todo remove
for idx in range(len(poses)):
poses[idx] = poses[idx][input_video_skip:]
if fps is None:
fps = get_fps(input_video_path)
if downsample > 1:
keypoints = downsample_tensor(keypoints, downsample)
all_frames = downsample_tensor(np.array(all_frames), downsample).astype('uint8')
for idx in range(len(poses)):
poses[idx] = downsample_tensor(poses[idx], downsample)
trajectories[idx] = downsample_tensor(trajectories[idx], downsample)
fps /= downsample
initialized = False
image = None
lines = []
points = None
if limit < 1:
limit = len(all_frames)
else:
limit = min(limit, len(all_frames))
parents = skeleton.parents()
def update_video(i):
nonlocal initialized, image, lines, points
for n, ax in enumerate(ax_3d):
ax.set_xlim3d([-radius/2 + trajectories[n][i, 0], radius/2 + trajectories[n][i, 0]])
ax.set_ylim3d([-radius/2 + trajectories[n][i, 1], radius/2 + trajectories[n][i, 1]])
# Update 2D poses
joints_right_2d = keypoints_metadata['keypoints_symmetry'][1]
colors_2d = np.full(keypoints.shape[1], 'black')
colors_2d[joints_right_2d] = 'red'
if not initialized:
image = ax_in.imshow(all_frames[i], aspect='equal')
for j, j_parent in enumerate(parents):
if j_parent == -1:
continue
if len(parents) == keypoints.shape[1] and keypoints_metadata['layout_name'] != 'coco':
# Draw skeleton only if keypoints match (otherwise we don't have the parents definition)
lines.append(ax_in.plot([keypoints[i, j, 0], keypoints[i, j_parent, 0]],
[keypoints[i, j, 1], keypoints[i, j_parent, 1]], color='pink'))
col = 'red' if j in skeleton.joints_right() else 'black'
for n, ax in enumerate(ax_3d):
pos = poses[n][i]
lines_3d[n].append(ax.plot([pos[j, 0], pos[j_parent, 0]],
[pos[j, 1], pos[j_parent, 1]],
[pos[j, 2], pos[j_parent, 2]], zdir='z', c=col))
points = ax_in.scatter(*keypoints[i].T, 10, color=colors_2d, edgecolors='white', zorder=10)
initialized = True
else:
image.set_data(all_frames[i])
for j, j_parent in enumerate(parents):
if j_parent == -1:
continue
if len(parents) == keypoints.shape[1] and keypoints_metadata['layout_name'] != 'coco':
lines[j-1][0].set_data([keypoints[i, j, 0], keypoints[i, j_parent, 0]],
[keypoints[i, j, 1], keypoints[i, j_parent, 1]])
for n, ax in enumerate(ax_3d):
pos = poses[n][i]
lines_3d[n][j-1][0].set_xdata(np.array([pos[j, 0], pos[j_parent, 0]]))
lines_3d[n][j-1][0].set_ydata(np.array([pos[j, 1], pos[j_parent, 1]]))
lines_3d[n][j-1][0].set_3d_properties(np.array([pos[j, 2], pos[j_parent, 2]]), zdir='z')
points.set_offsets(keypoints[i])
print('{}/{} '.format(i, limit), end='\r')
fig.tight_layout()
anim = FuncAnimation(fig, update_video, frames=np.arange(0, limit), interval=1000/fps, repeat=False)
if output.endswith('.mp4'):
Writer = writers['ffmpeg']
writer = Writer(fps=fps, metadata={}, bitrate=bitrate)
anim.save(output, writer=writer)
elif output.endswith('.gif'):
anim.save(output, dpi=80, writer='imagemagick')
else:
raise ValueError('Unsupported output format (only .mp4 and .gif are supported)')
plt.close()
print('Rendering...')
input_keypoints = keypoints[args.viz_subject][args.viz_action][args.viz_camera].copy()
ground_truth = None
if args.viz_subject in dataset.subjects() and args.viz_action in dataset[args.viz_subject]:
if 'positions_3d' in dataset[args.viz_subject][args.viz_action]:
ground_truth = dataset[args.viz_subject][args.viz_action]['positions_3d'][args.viz_camera].copy()
if ground_truth is None:
print('INFO: this action is unlabeled. Ground truth will not be rendered.')
gen = UnchunkedGenerator(None,
None,
[input_keypoints],
pad=pad,
causal_shift=causal_shift,
augment=args.test_time_augmentation,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
prediction = evaluate(gen, return_predictions=True)
if model_traj is not None and ground_truth is None:
prediction_traj = evaluate(gen, return_predictions=True, use_trajectory_model=True)
prediction += prediction_traj
if args.viz_export is not None:
print('Exporting joint positions to', args.viz_export)
# Predictions are in camera space
np.save(args.viz_export, prediction)
if args.viz_output is not None:
if ground_truth is not None:
# Reapply trajectory
trajectory = ground_truth[:, :1]
ground_truth[:, 1:] += trajectory
prediction += trajectory
# Invert camera transformation
cam = dataset.cameras()[args.viz_subject][args.viz_camera]
if ground_truth is not None:
prediction = camera_to_world(prediction, R=cam['orientation'], t=cam['translation'])
ground_truth = camera_to_world(ground_truth, R=cam['orientation'], t=cam['translation'])
else:
# If the ground truth is not available, take the camera extrinsic params from a random subject.
# They are almost the same, and anyway, we only need this for visualization purposes.
for subject in dataset.cameras():
if 'orientation' in dataset.cameras()[subject][args.viz_camera]:
rot = dataset.cameras()[subject][args.viz_camera]['orientation']
break
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
anim_output = {'Reconstruction': prediction}
if ground_truth is not None and not args.viz_no_ground_truth:
anim_output['Ground truth'] = ground_truth
input_keypoints = image_coordinates(input_keypoints[..., :2], w=cam['res_w'], h=cam['res_h'])
render_animation(input_keypoints, keypoints_metadata, anim_output,
dataset.skeleton(), dataset.fps(), args.viz_bitrate, cam['azimuth'], args.viz_output,
limit=args.viz_limit, downsample=args.viz_downsample, size=args.viz_size,
input_video_path=args.viz_video, viewport=(cam['res_w'], cam['res_h']),
ls VideoPose3D/saved_models/humaneva/checkpoints/supervised/
| path | name | size | modificationTime |
|---|---|---|---|
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter0.ckpt | 1_members_ensemble0_iter0.ckpt | 3.4199173e7 | 1.670238244e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter1.ckpt | 1_members_ensemble0_iter1.ckpt | 3.4199173e7 | 1.670238273e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter10.ckpt | 1_members_ensemble0_iter10.ckpt | 3.4199173e7 | 1.670238499e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter100.ckpt | 1_members_ensemble0_iter100.ckpt | 3.4199173e7 | 1.670241478e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter101.ckpt | 1_members_ensemble0_iter101.ckpt | 3.4199173e7 | 1.670241504e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter102.ckpt | 1_members_ensemble0_iter102.ckpt | 3.4199173e7 | 1.670241531e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter103.ckpt | 1_members_ensemble0_iter103.ckpt | 3.4199173e7 | 1.670241558e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter104.ckpt | 1_members_ensemble0_iter104.ckpt | 3.4199173e7 | 1.670241585e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter105.ckpt | 1_members_ensemble0_iter105.ckpt | 3.4199173e7 | 1.670241612e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter106.ckpt | 1_members_ensemble0_iter106.ckpt | 3.4199173e7 | 1.67024164e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter107.ckpt | 1_members_ensemble0_iter107.ckpt | 3.4199173e7 | 1.670241667e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter108.ckpt | 1_members_ensemble0_iter108.ckpt | 3.4199173e7 | 1.670241694e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter109.ckpt | 1_members_ensemble0_iter109.ckpt | 3.4199173e7 | 1.670241721e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter11.ckpt | 1_members_ensemble0_iter11.ckpt | 3.4199173e7 | 1.670238879e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter110.ckpt | 1_members_ensemble0_iter110.ckpt | 3.4199173e7 | 1.670241748e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter111.ckpt | 1_members_ensemble0_iter111.ckpt | 3.4199173e7 | 1.670241775e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter112.ckpt | 1_members_ensemble0_iter112.ckpt | 3.4199173e7 | 1.670241802e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter113.ckpt | 1_members_ensemble0_iter113.ckpt | 3.4199173e7 | 1.670241829e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter114.ckpt | 1_members_ensemble0_iter114.ckpt | 3.4199173e7 | 1.670241856e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter115.ckpt | 1_members_ensemble0_iter115.ckpt | 3.4199173e7 | 1.670241883e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter116.ckpt | 1_members_ensemble0_iter116.ckpt | 3.4199173e7 | 1.670241909e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter117.ckpt | 1_members_ensemble0_iter117.ckpt | 3.4199173e7 | 1.670241936e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter118.ckpt | 1_members_ensemble0_iter118.ckpt | 3.4199173e7 | 1.670241963e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter119.ckpt | 1_members_ensemble0_iter119.ckpt | 3.4199173e7 | 1.670241991e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter12.ckpt | 1_members_ensemble0_iter12.ckpt | 3.4199173e7 | 1.670238915e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter120.ckpt | 1_members_ensemble0_iter120.ckpt | 3.4199173e7 | 1.670242018e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter121.ckpt | 1_members_ensemble0_iter121.ckpt | 3.4199173e7 | 1.670242045e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter122.ckpt | 1_members_ensemble0_iter122.ckpt | 3.4199173e7 | 1.670242072e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter123.ckpt | 1_members_ensemble0_iter123.ckpt | 3.4199173e7 | 1.670242099e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter124.ckpt | 1_members_ensemble0_iter124.ckpt | 3.4199173e7 | 1.670242126e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter125.ckpt | 1_members_ensemble0_iter125.ckpt | 3.4199173e7 | 1.670242153e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter126.ckpt | 1_members_ensemble0_iter126.ckpt | 3.4199173e7 | 1.67024218e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter127.ckpt | 1_members_ensemble0_iter127.ckpt | 3.4199173e7 | 1.670242207e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter128.ckpt | 1_members_ensemble0_iter128.ckpt | 3.4199173e7 | 1.670242234e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter129.ckpt | 1_members_ensemble0_iter129.ckpt | 3.4199173e7 | 1.670242261e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter13.ckpt | 1_members_ensemble0_iter13.ckpt | 3.4199173e7 | 1.670238956e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter130.ckpt | 1_members_ensemble0_iter130.ckpt | 3.4199173e7 | 1.670242288e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter131.ckpt | 1_members_ensemble0_iter131.ckpt | 3.4199173e7 | 1.670242315e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter132.ckpt | 1_members_ensemble0_iter132.ckpt | 3.4199173e7 | 1.670242342e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter133.ckpt | 1_members_ensemble0_iter133.ckpt | 3.4199173e7 | 1.670242369e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter134.ckpt | 1_members_ensemble0_iter134.ckpt | 3.4199173e7 | 1.670242395e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter135.ckpt | 1_members_ensemble0_iter135.ckpt | 3.4199173e7 | 1.670242423e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter136.ckpt | 1_members_ensemble0_iter136.ckpt | 3.4199173e7 | 1.670242449e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter137.ckpt | 1_members_ensemble0_iter137.ckpt | 3.4199173e7 | 1.670242476e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter138.ckpt | 1_members_ensemble0_iter138.ckpt | 3.4199173e7 | 1.670242503e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter139.ckpt | 1_members_ensemble0_iter139.ckpt | 3.4199173e7 | 1.67024253e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter14.ckpt | 1_members_ensemble0_iter14.ckpt | 3.4199173e7 | 1.670238997e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter140.ckpt | 1_members_ensemble0_iter140.ckpt | 3.4199173e7 | 1.670242557e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter141.ckpt | 1_members_ensemble0_iter141.ckpt | 3.4199173e7 | 1.670242584e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter142.ckpt | 1_members_ensemble0_iter142.ckpt | 3.4199173e7 | 1.670242608e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter143.ckpt | 1_members_ensemble0_iter143.ckpt | 3.4199173e7 | 1.670242632e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter144.ckpt | 1_members_ensemble0_iter144.ckpt | 3.4199173e7 | 1.670242656e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter145.ckpt | 1_members_ensemble0_iter145.ckpt | 3.4199173e7 | 1.67024268e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter146.ckpt | 1_members_ensemble0_iter146.ckpt | 3.4199173e7 | 1.670242705e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter147.ckpt | 1_members_ensemble0_iter147.ckpt | 3.4199173e7 | 1.67024273e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter148.ckpt | 1_members_ensemble0_iter148.ckpt | 3.4199173e7 | 1.670242756e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter149.ckpt | 1_members_ensemble0_iter149.ckpt | 3.4199173e7 | 1.670242783e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter15.ckpt | 1_members_ensemble0_iter15.ckpt | 3.4199173e7 | 1.670239038e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter150.ckpt | 1_members_ensemble0_iter150.ckpt | 3.4199173e7 | 1.670242811e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter151.ckpt | 1_members_ensemble0_iter151.ckpt | 3.4199173e7 | 1.670242838e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter152.ckpt | 1_members_ensemble0_iter152.ckpt | 3.4199173e7 | 1.670242865e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter153.ckpt | 1_members_ensemble0_iter153.ckpt | 3.4199173e7 | 1.67024289e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter154.ckpt | 1_members_ensemble0_iter154.ckpt | 3.4199173e7 | 1.670242915e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter155.ckpt | 1_members_ensemble0_iter155.ckpt | 3.4199173e7 | 1.670242939e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter156.ckpt | 1_members_ensemble0_iter156.ckpt | 3.4199173e7 | 1.670242963e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter157.ckpt | 1_members_ensemble0_iter157.ckpt | 3.4199173e7 | 1.670242988e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter158.ckpt | 1_members_ensemble0_iter158.ckpt | 3.4199173e7 | 1.670243012e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter159.ckpt | 1_members_ensemble0_iter159.ckpt | 3.4199173e7 | 1.670243045e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter16.ckpt | 1_members_ensemble0_iter16.ckpt | 3.4199173e7 | 1.670239079e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter160.ckpt | 1_members_ensemble0_iter160.ckpt | 3.4199173e7 | 1.670243085e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter161.ckpt | 1_members_ensemble0_iter161.ckpt | 3.4199173e7 | 1.670243125e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter162.ckpt | 1_members_ensemble0_iter162.ckpt | 3.4199173e7 | 1.670243166e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter163.ckpt | 1_members_ensemble0_iter163.ckpt | 3.4199173e7 | 1.670243206e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter164.ckpt | 1_members_ensemble0_iter164.ckpt | 3.4199173e7 | 1.670243246e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter165.ckpt | 1_members_ensemble0_iter165.ckpt | 3.4199173e7 | 1.670243287e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter166.ckpt | 1_members_ensemble0_iter166.ckpt | 3.4199173e7 | 1.670243327e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter167.ckpt | 1_members_ensemble0_iter167.ckpt | 3.4199173e7 | 1.670243368e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter168.ckpt | 1_members_ensemble0_iter168.ckpt | 3.4199173e7 | 1.670243408e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter169.ckpt | 1_members_ensemble0_iter169.ckpt | 3.4199173e7 | 1.670243448e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter17.ckpt | 1_members_ensemble0_iter17.ckpt | 3.4199173e7 | 1.670239119e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter170.ckpt | 1_members_ensemble0_iter170.ckpt | 3.4199173e7 | 1.670243488e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter171.ckpt | 1_members_ensemble0_iter171.ckpt | 3.4199173e7 | 1.670243529e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter172.ckpt | 1_members_ensemble0_iter172.ckpt | 3.4199173e7 | 1.670243569e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter173.ckpt | 1_members_ensemble0_iter173.ckpt | 3.4199173e7 | 1.670243609e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter174.ckpt | 1_members_ensemble0_iter174.ckpt | 3.4199173e7 | 1.67024365e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter175.ckpt | 1_members_ensemble0_iter175.ckpt | 3.4199173e7 | 1.670243691e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter176.ckpt | 1_members_ensemble0_iter176.ckpt | 3.4199173e7 | 1.670243731e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter177.ckpt | 1_members_ensemble0_iter177.ckpt | 3.4199173e7 | 1.670243772e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter178.ckpt | 1_members_ensemble0_iter178.ckpt | 3.4199173e7 | 1.670243812e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter179.ckpt | 1_members_ensemble0_iter179.ckpt | 3.4199173e7 | 1.670243853e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter18.ckpt | 1_members_ensemble0_iter18.ckpt | 3.4199173e7 | 1.670239159e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter180.ckpt | 1_members_ensemble0_iter180.ckpt | 3.4199173e7 | 1.670243893e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter181.ckpt | 1_members_ensemble0_iter181.ckpt | 3.4199173e7 | 1.670243934e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter182.ckpt | 1_members_ensemble0_iter182.ckpt | 3.4199173e7 | 1.670243974e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter183.ckpt | 1_members_ensemble0_iter183.ckpt | 3.4199173e7 | 1.670244016e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter184.ckpt | 1_members_ensemble0_iter184.ckpt | 3.4199173e7 | 1.670244057e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter185.ckpt | 1_members_ensemble0_iter185.ckpt | 3.4199173e7 | 1.670244097e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter186.ckpt | 1_members_ensemble0_iter186.ckpt | 3.4199173e7 | 1.670244137e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter187.ckpt | 1_members_ensemble0_iter187.ckpt | 3.4199173e7 | 1.670244178e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter188.ckpt | 1_members_ensemble0_iter188.ckpt | 3.4199173e7 | 1.670244218e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter189.ckpt | 1_members_ensemble0_iter189.ckpt | 3.4199173e7 | 1.670244258e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter19.ckpt | 1_members_ensemble0_iter19.ckpt | 3.4199173e7 | 1.6702392e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter190.ckpt | 1_members_ensemble0_iter190.ckpt | 3.4199173e7 | 1.670244298e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter191.ckpt | 1_members_ensemble0_iter191.ckpt | 3.4199173e7 | 1.670244339e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter192.ckpt | 1_members_ensemble0_iter192.ckpt | 3.4199173e7 | 1.67024438e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter193.ckpt | 1_members_ensemble0_iter193.ckpt | 3.4199173e7 | 1.670244421e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter194.ckpt | 1_members_ensemble0_iter194.ckpt | 3.4199173e7 | 1.670244461e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter195.ckpt | 1_members_ensemble0_iter195.ckpt | 3.4199173e7 | 1.670244501e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter196.ckpt | 1_members_ensemble0_iter196.ckpt | 3.4199173e7 | 1.670244541e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter197.ckpt | 1_members_ensemble0_iter197.ckpt | 3.4199173e7 | 1.670244581e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter198.ckpt | 1_members_ensemble0_iter198.ckpt | 3.4199173e7 | 1.670244622e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter199.ckpt | 1_members_ensemble0_iter199.ckpt | 3.4199173e7 | 1.670244662e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter2.ckpt | 1_members_ensemble0_iter2.ckpt | 3.4199173e7 | 1.670238298e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter20.ckpt | 1_members_ensemble0_iter20.ckpt | 3.4199173e7 | 1.670239226e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter200.ckpt | 1_members_ensemble0_iter200.ckpt | 3.4199173e7 | 1.670244702e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter201.ckpt | 1_members_ensemble0_iter201.ckpt | 3.4199173e7 | 1.670244742e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter202.ckpt | 1_members_ensemble0_iter202.ckpt | 3.4199173e7 | 1.670244782e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter203.ckpt | 1_members_ensemble0_iter203.ckpt | 3.4199173e7 | 1.670244823e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter204.ckpt | 1_members_ensemble0_iter204.ckpt | 3.4199173e7 | 1.670244863e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter205.ckpt | 1_members_ensemble0_iter205.ckpt | 3.4199173e7 | 1.670244903e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter206.ckpt | 1_members_ensemble0_iter206.ckpt | 3.4199173e7 | 1.670244944e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter207.ckpt | 1_members_ensemble0_iter207.ckpt | 3.4199173e7 | 1.670244984e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter208.ckpt | 1_members_ensemble0_iter208.ckpt | 3.4199173e7 | 1.670245024e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter209.ckpt | 1_members_ensemble0_iter209.ckpt | 3.4199173e7 | 1.670245065e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter21.ckpt | 1_members_ensemble0_iter21.ckpt | 3.4199173e7 | 1.670239266e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter210.ckpt | 1_members_ensemble0_iter210.ckpt | 3.4199173e7 | 1.670245105e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter211.ckpt | 1_members_ensemble0_iter211.ckpt | 3.4199173e7 | 1.670245145e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter212.ckpt | 1_members_ensemble0_iter212.ckpt | 3.4199173e7 | 1.670245185e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter213.ckpt | 1_members_ensemble0_iter213.ckpt | 3.4199173e7 | 1.670245225e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter214.ckpt | 1_members_ensemble0_iter214.ckpt | 3.4199173e7 | 1.670245266e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter215.ckpt | 1_members_ensemble0_iter215.ckpt | 3.4199173e7 | 1.670245306e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter216.ckpt | 1_members_ensemble0_iter216.ckpt | 3.4199173e7 | 1.670245346e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter217.ckpt | 1_members_ensemble0_iter217.ckpt | 3.4199173e7 | 1.670245387e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter218.ckpt | 1_members_ensemble0_iter218.ckpt | 3.4199173e7 | 1.670245427e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter219.ckpt | 1_members_ensemble0_iter219.ckpt | 3.4199173e7 | 1.670245467e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter22.ckpt | 1_members_ensemble0_iter22.ckpt | 3.4199173e7 | 1.670239307e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter220.ckpt | 1_members_ensemble0_iter220.ckpt | 3.4199173e7 | 1.670245507e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter221.ckpt | 1_members_ensemble0_iter221.ckpt | 3.4199173e7 | 1.670245548e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter222.ckpt | 1_members_ensemble0_iter222.ckpt | 3.4199173e7 | 1.670245588e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter223.ckpt | 1_members_ensemble0_iter223.ckpt | 3.4199173e7 | 1.670245628e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter224.ckpt | 1_members_ensemble0_iter224.ckpt | 3.4199173e7 | 1.670245668e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter225.ckpt | 1_members_ensemble0_iter225.ckpt | 3.4199173e7 | 1.670245709e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter226.ckpt | 1_members_ensemble0_iter226.ckpt | 3.4199173e7 | 1.670245743e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter227.ckpt | 1_members_ensemble0_iter227.ckpt | 3.4199173e7 | 1.67024577e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter228.ckpt | 1_members_ensemble0_iter228.ckpt | 3.4199173e7 | 1.670245794e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter229.ckpt | 1_members_ensemble0_iter229.ckpt | 3.4199173e7 | 1.670245818e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter23.ckpt | 1_members_ensemble0_iter23.ckpt | 3.4199173e7 | 1.670239347e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter230.ckpt | 1_members_ensemble0_iter230.ckpt | 3.4199173e7 | 1.670245843e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter231.ckpt | 1_members_ensemble0_iter231.ckpt | 3.4199173e7 | 1.670245868e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter232.ckpt | 1_members_ensemble0_iter232.ckpt | 3.4199173e7 | 1.670245892e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter233.ckpt | 1_members_ensemble0_iter233.ckpt | 3.4199173e7 | 1.670245916e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter234.ckpt | 1_members_ensemble0_iter234.ckpt | 3.4199173e7 | 1.670245941e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter235.ckpt | 1_members_ensemble0_iter235.ckpt | 3.4199173e7 | 1.670245965e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter236.ckpt | 1_members_ensemble0_iter236.ckpt | 3.4199173e7 | 1.67024599e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter237.ckpt | 1_members_ensemble0_iter237.ckpt | 3.4199173e7 | 1.670246014e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter238.ckpt | 1_members_ensemble0_iter238.ckpt | 3.4199173e7 | 1.670246038e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter24.ckpt | 1_members_ensemble0_iter24.ckpt | 3.4199173e7 | 1.670239387e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter25.ckpt | 1_members_ensemble0_iter25.ckpt | 3.4199173e7 | 1.670239428e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter26.ckpt | 1_members_ensemble0_iter26.ckpt | 3.4199173e7 | 1.670239468e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter27.ckpt | 1_members_ensemble0_iter27.ckpt | 3.4199173e7 | 1.670239508e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter28.ckpt | 1_members_ensemble0_iter28.ckpt | 3.4199173e7 | 1.670239556e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter29.ckpt | 1_members_ensemble0_iter29.ckpt | 3.4199173e7 | 1.670239595e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter3.ckpt | 1_members_ensemble0_iter3.ckpt | 3.4199173e7 | 1.670238323e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter30.ckpt | 1_members_ensemble0_iter30.ckpt | 3.4199173e7 | 1.670239635e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter31.ckpt | 1_members_ensemble0_iter31.ckpt | 3.4199173e7 | 1.670239675e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter32.ckpt | 1_members_ensemble0_iter32.ckpt | 3.4199173e7 | 1.670239715e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter33.ckpt | 1_members_ensemble0_iter33.ckpt | 3.4199173e7 | 1.670239755e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter34.ckpt | 1_members_ensemble0_iter34.ckpt | 3.4199173e7 | 1.670239796e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter35.ckpt | 1_members_ensemble0_iter35.ckpt | 3.4199173e7 | 1.670239824e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter36.ckpt | 1_members_ensemble0_iter36.ckpt | 3.4199173e7 | 1.670239849e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter37.ckpt | 1_members_ensemble0_iter37.ckpt | 3.4199173e7 | 1.670239873e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter38.ckpt | 1_members_ensemble0_iter38.ckpt | 3.4199173e7 | 1.670239898e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter39.ckpt | 1_members_ensemble0_iter39.ckpt | 3.4199173e7 | 1.670239925e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter4.ckpt | 1_members_ensemble0_iter4.ckpt | 3.4199173e7 | 1.670238348e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter40.ckpt | 1_members_ensemble0_iter40.ckpt | 3.4199173e7 | 1.670239952e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter41.ckpt | 1_members_ensemble0_iter41.ckpt | 3.4199173e7 | 1.670239979e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter42.ckpt | 1_members_ensemble0_iter42.ckpt | 3.4199173e7 | 1.670240006e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter43.ckpt | 1_members_ensemble0_iter43.ckpt | 3.4199173e7 | 1.670240033e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter44.ckpt | 1_members_ensemble0_iter44.ckpt | 3.4199173e7 | 1.67024006e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter45.ckpt | 1_members_ensemble0_iter45.ckpt | 3.4199173e7 | 1.670240087e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter46.ckpt | 1_members_ensemble0_iter46.ckpt | 3.4199173e7 | 1.670240114e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter47.ckpt | 1_members_ensemble0_iter47.ckpt | 3.4199173e7 | 1.670240141e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter48.ckpt | 1_members_ensemble0_iter48.ckpt | 3.4199173e7 | 1.670240168e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter49.ckpt | 1_members_ensemble0_iter49.ckpt | 3.4199173e7 | 1.670240195e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter5.ckpt | 1_members_ensemble0_iter5.ckpt | 3.4199173e7 | 1.670238374e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter50.ckpt | 1_members_ensemble0_iter50.ckpt | 3.4199173e7 | 1.670240222e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter51.ckpt | 1_members_ensemble0_iter51.ckpt | 3.4199173e7 | 1.670240249e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter52.ckpt | 1_members_ensemble0_iter52.ckpt | 3.4199173e7 | 1.670240273e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter53.ckpt | 1_members_ensemble0_iter53.ckpt | 3.4199173e7 | 1.670240297e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter54.ckpt | 1_members_ensemble0_iter54.ckpt | 3.4199173e7 | 1.670240322e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter55.ckpt | 1_members_ensemble0_iter55.ckpt | 3.4199173e7 | 1.670240346e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter56.ckpt | 1_members_ensemble0_iter56.ckpt | 3.4199173e7 | 1.670240371e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter57.ckpt | 1_members_ensemble0_iter57.ckpt | 3.4199173e7 | 1.670240395e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter58.ckpt | 1_members_ensemble0_iter58.ckpt | 3.4199173e7 | 1.670240419e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter59.ckpt | 1_members_ensemble0_iter59.ckpt | 3.4199173e7 | 1.670240444e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter6.ckpt | 1_members_ensemble0_iter6.ckpt | 3.4199173e7 | 1.670238399e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter60.ckpt | 1_members_ensemble0_iter60.ckpt | 3.4199173e7 | 1.670240468e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter61.ckpt | 1_members_ensemble0_iter61.ckpt | 3.4199173e7 | 1.670240492e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter62.ckpt | 1_members_ensemble0_iter62.ckpt | 3.4199173e7 | 1.670240517e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter63.ckpt | 1_members_ensemble0_iter63.ckpt | 3.4199173e7 | 1.670240542e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter64.ckpt | 1_members_ensemble0_iter64.ckpt | 3.4199173e7 | 1.670240566e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter65.ckpt | 1_members_ensemble0_iter65.ckpt | 3.4199173e7 | 1.670240591e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter66.ckpt | 1_members_ensemble0_iter66.ckpt | 3.4199173e7 | 1.670240615e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter67.ckpt | 1_members_ensemble0_iter67.ckpt | 3.4199173e7 | 1.670240639e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter68.ckpt | 1_members_ensemble0_iter68.ckpt | 3.4199173e7 | 1.670240664e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter69.ckpt | 1_members_ensemble0_iter69.ckpt | 3.4199173e7 | 1.670240688e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter7.ckpt | 1_members_ensemble0_iter7.ckpt | 3.4199173e7 | 1.670238424e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter70.ckpt | 1_members_ensemble0_iter70.ckpt | 3.4199173e7 | 1.670240713e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter71.ckpt | 1_members_ensemble0_iter71.ckpt | 3.4199173e7 | 1.670240737e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter72.ckpt | 1_members_ensemble0_iter72.ckpt | 3.4199173e7 | 1.670240763e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter73.ckpt | 1_members_ensemble0_iter73.ckpt | 3.4199173e7 | 1.670240787e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter74.ckpt | 1_members_ensemble0_iter74.ckpt | 3.4199173e7 | 1.670240811e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter75.ckpt | 1_members_ensemble0_iter75.ckpt | 3.4199173e7 | 1.670240835e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter76.ckpt | 1_members_ensemble0_iter76.ckpt | 3.4199173e7 | 1.670240859e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter77.ckpt | 1_members_ensemble0_iter77.ckpt | 3.4199173e7 | 1.670240884e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter78.ckpt | 1_members_ensemble0_iter78.ckpt | 3.4199173e7 | 1.670240908e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter79.ckpt | 1_members_ensemble0_iter79.ckpt | 3.4199173e7 | 1.670240932e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter8.ckpt | 1_members_ensemble0_iter8.ckpt | 3.4199173e7 | 1.670238449e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter80.ckpt | 1_members_ensemble0_iter80.ckpt | 3.4199173e7 | 1.670240957e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter81.ckpt | 1_members_ensemble0_iter81.ckpt | 3.4199173e7 | 1.670240982e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter82.ckpt | 1_members_ensemble0_iter82.ckpt | 3.4199173e7 | 1.670241008e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter83.ckpt | 1_members_ensemble0_iter83.ckpt | 3.4199173e7 | 1.670241032e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter84.ckpt | 1_members_ensemble0_iter84.ckpt | 3.4199173e7 | 1.670241057e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter85.ckpt | 1_members_ensemble0_iter85.ckpt | 3.4199173e7 | 1.670241082e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter86.ckpt | 1_members_ensemble0_iter86.ckpt | 3.4199173e7 | 1.670241109e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter87.ckpt | 1_members_ensemble0_iter87.ckpt | 3.4199173e7 | 1.670241136e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter88.ckpt | 1_members_ensemble0_iter88.ckpt | 3.4199173e7 | 1.670241163e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter89.ckpt | 1_members_ensemble0_iter89.ckpt | 3.4199173e7 | 1.67024119e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter9.ckpt | 1_members_ensemble0_iter9.ckpt | 3.4199173e7 | 1.670238474e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter90.ckpt | 1_members_ensemble0_iter90.ckpt | 3.4199173e7 | 1.670241216e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter91.ckpt | 1_members_ensemble0_iter91.ckpt | 3.4199173e7 | 1.670241243e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter92.ckpt | 1_members_ensemble0_iter92.ckpt | 3.4199173e7 | 1.67024127e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter93.ckpt | 1_members_ensemble0_iter93.ckpt | 3.4199173e7 | 1.670241297e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter94.ckpt | 1_members_ensemble0_iter94.ckpt | 3.4199173e7 | 1.670241323e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter95.ckpt | 1_members_ensemble0_iter95.ckpt | 3.4199173e7 | 1.670241349e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter96.ckpt | 1_members_ensemble0_iter96.ckpt | 3.4199173e7 | 1.670241376e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter97.ckpt | 1_members_ensemble0_iter97.ckpt | 3.4199173e7 | 1.670241403e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter98.ckpt | 1_members_ensemble0_iter98.ckpt | 3.4199173e7 | 1.670241429e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter99.ckpt | 1_members_ensemble0_iter99.ckpt | 3.4199173e7 | 1.670241453e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter0.ckpt | 2_members_ensemble0_iter0.ckpt | 3.4199173e7 | 1.670246372e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter1.ckpt | 2_members_ensemble0_iter1.ckpt | 3.4199173e7 | 1.670246398e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter10.ckpt | 2_members_ensemble0_iter10.ckpt | 3.4199173e7 | 1.670246637e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter11.ckpt | 2_members_ensemble0_iter11.ckpt | 3.4199173e7 | 1.670246663e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter12.ckpt | 2_members_ensemble0_iter12.ckpt | 3.4199173e7 | 1.67024669e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter13.ckpt | 2_members_ensemble0_iter13.ckpt | 3.4199173e7 | 1.670246716e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter14.ckpt | 2_members_ensemble0_iter14.ckpt | 3.4199173e7 | 1.670246743e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter15.ckpt | 2_members_ensemble0_iter15.ckpt | 3.4199173e7 | 1.67024677e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter16.ckpt | 2_members_ensemble0_iter16.ckpt | 3.4199173e7 | 1.670246796e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter17.ckpt | 2_members_ensemble0_iter17.ckpt | 3.4199173e7 | 1.670246822e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter18.ckpt | 2_members_ensemble0_iter18.ckpt | 3.4199173e7 | 1.670246849e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter19.ckpt | 2_members_ensemble0_iter19.ckpt | 3.4199173e7 | 1.670246875e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter2.ckpt | 2_members_ensemble0_iter2.ckpt | 3.4199173e7 | 1.670246425e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter20.ckpt | 2_members_ensemble0_iter20.ckpt | 3.4199173e7 | 1.670246902e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter21.ckpt | 2_members_ensemble0_iter21.ckpt | 3.4199173e7 | 1.670246928e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter22.ckpt | 2_members_ensemble0_iter22.ckpt | 3.4199173e7 | 1.670246955e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter23.ckpt | 2_members_ensemble0_iter23.ckpt | 3.4199173e7 | 1.670246981e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter24.ckpt | 2_members_ensemble0_iter24.ckpt | 3.4199173e7 | 1.670247008e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter25.ckpt | 2_members_ensemble0_iter25.ckpt | 3.4199173e7 | 1.670247034e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter26.ckpt | 2_members_ensemble0_iter26.ckpt | 3.4199173e7 | 1.67024706e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter27.ckpt | 2_members_ensemble0_iter27.ckpt | 3.4199173e7 | 1.670247087e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter28.ckpt | 2_members_ensemble0_iter28.ckpt | 3.4199173e7 | 1.670247114e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter29.ckpt | 2_members_ensemble0_iter29.ckpt | 3.4199173e7 | 1.67024714e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter3.ckpt | 2_members_ensemble0_iter3.ckpt | 3.4199173e7 | 1.670246451e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter30.ckpt | 2_members_ensemble0_iter30.ckpt | 3.4199173e7 | 1.670247167e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter31.ckpt | 2_members_ensemble0_iter31.ckpt | 3.4199173e7 | 1.670247193e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter32.ckpt | 2_members_ensemble0_iter32.ckpt | 3.4199173e7 | 1.670247219e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter33.ckpt | 2_members_ensemble0_iter33.ckpt | 3.4199173e7 | 1.670247246e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter34.ckpt | 2_members_ensemble0_iter34.ckpt | 3.4199173e7 | 1.670247272e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter35.ckpt | 2_members_ensemble0_iter35.ckpt | 3.4199173e7 | 1.670247299e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter36.ckpt | 2_members_ensemble0_iter36.ckpt | 3.4199173e7 | 1.670247325e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter37.ckpt | 2_members_ensemble0_iter37.ckpt | 3.4199173e7 | 1.670247352e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter38.ckpt | 2_members_ensemble0_iter38.ckpt | 3.4199173e7 | 1.670247378e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter39.ckpt | 2_members_ensemble0_iter39.ckpt | 3.4199173e7 | 1.670247404e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter4.ckpt | 2_members_ensemble0_iter4.ckpt | 3.4199173e7 | 1.670246478e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter40.ckpt | 2_members_ensemble0_iter40.ckpt | 3.4199173e7 | 1.670247431e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter41.ckpt | 2_members_ensemble0_iter41.ckpt | 3.4199173e7 | 1.670247457e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter42.ckpt | 2_members_ensemble0_iter42.ckpt | 3.4199173e7 | 1.670247483e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter43.ckpt | 2_members_ensemble0_iter43.ckpt | 3.4199173e7 | 1.67024751e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter44.ckpt | 2_members_ensemble0_iter44.ckpt | 3.4199173e7 | 1.670247537e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter45.ckpt | 2_members_ensemble0_iter45.ckpt | 3.4199173e7 | 1.670247563e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter46.ckpt | 2_members_ensemble0_iter46.ckpt | 3.4199173e7 | 1.670247589e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter47.ckpt | 2_members_ensemble0_iter47.ckpt | 3.4199173e7 | 1.670247616e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter48.ckpt | 2_members_ensemble0_iter48.ckpt | 3.4199173e7 | 1.670247642e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter49.ckpt | 2_members_ensemble0_iter49.ckpt | 3.4199173e7 | 1.670247668e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter5.ckpt | 2_members_ensemble0_iter5.ckpt | 3.4199173e7 | 1.670246504e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter50.ckpt | 2_members_ensemble0_iter50.ckpt | 3.4199173e7 | 1.670247695e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter51.ckpt | 2_members_ensemble0_iter51.ckpt | 3.4199173e7 | 1.670247721e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter52.ckpt | 2_members_ensemble0_iter52.ckpt | 3.4199173e7 | 1.670247747e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter53.ckpt | 2_members_ensemble0_iter53.ckpt | 3.4199173e7 | 1.670247774e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter54.ckpt | 2_members_ensemble0_iter54.ckpt | 3.4199173e7 | 1.670247801e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter55.ckpt | 2_members_ensemble0_iter55.ckpt | 3.4199173e7 | 1.670247827e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter56.ckpt | 2_members_ensemble0_iter56.ckpt | 3.4199173e7 | 1.670247854e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter57.ckpt | 2_members_ensemble0_iter57.ckpt | 3.4199173e7 | 1.670247881e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter58.ckpt | 2_members_ensemble0_iter58.ckpt | 3.4199173e7 | 1.670247907e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter59.ckpt | 2_members_ensemble0_iter59.ckpt | 3.4199173e7 | 1.670247933e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter6.ckpt | 2_members_ensemble0_iter6.ckpt | 3.4199173e7 | 1.670246531e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter60.ckpt | 2_members_ensemble0_iter60.ckpt | 3.4199173e7 | 1.67024796e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter61.ckpt | 2_members_ensemble0_iter61.ckpt | 3.4199173e7 | 1.670247986e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter62.ckpt | 2_members_ensemble0_iter62.ckpt | 3.4199173e7 | 1.670248013e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter63.ckpt | 2_members_ensemble0_iter63.ckpt | 3.4199173e7 | 1.670248039e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter64.ckpt | 2_members_ensemble0_iter64.ckpt | 3.4199173e7 | 1.670248066e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter65.ckpt | 2_members_ensemble0_iter65.ckpt | 3.4199173e7 | 1.670248092e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter66.ckpt | 2_members_ensemble0_iter66.ckpt | 3.4199173e7 | 1.670248119e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter67.ckpt | 2_members_ensemble0_iter67.ckpt | 3.4199173e7 | 1.670248145e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter68.ckpt | 2_members_ensemble0_iter68.ckpt | 3.4199173e7 | 1.670248171e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter69.ckpt | 2_members_ensemble0_iter69.ckpt | 3.4199173e7 | 1.670248198e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter7.ckpt | 2_members_ensemble0_iter7.ckpt | 3.4199173e7 | 1.670246557e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter70.ckpt | 2_members_ensemble0_iter70.ckpt | 3.4199173e7 | 1.670248224e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter71.ckpt | 2_members_ensemble0_iter71.ckpt | 3.4199173e7 | 1.670248251e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter72.ckpt | 2_members_ensemble0_iter72.ckpt | 3.4199173e7 | 1.670248279e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter73.ckpt | 2_members_ensemble0_iter73.ckpt | 3.4199173e7 | 1.670248306e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter74.ckpt | 2_members_ensemble0_iter74.ckpt | 3.4199173e7 | 1.670248332e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter75.ckpt | 2_members_ensemble0_iter75.ckpt | 3.4199173e7 | 1.670248358e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter76.ckpt | 2_members_ensemble0_iter76.ckpt | 3.4199173e7 | 1.670248384e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter77.ckpt | 2_members_ensemble0_iter77.ckpt | 3.4199173e7 | 1.670248411e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter78.ckpt | 2_members_ensemble0_iter78.ckpt | 3.4199173e7 | 1.670248437e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter79.ckpt | 2_members_ensemble0_iter79.ckpt | 3.4199173e7 | 1.670248463e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter8.ckpt | 2_members_ensemble0_iter8.ckpt | 3.4199173e7 | 1.670246584e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter80.ckpt | 2_members_ensemble0_iter80.ckpt | 3.4199173e7 | 1.67024849e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter81.ckpt | 2_members_ensemble0_iter81.ckpt | 3.4199173e7 | 1.670248516e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter82.ckpt | 2_members_ensemble0_iter82.ckpt | 3.4199173e7 | 1.670248543e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter83.ckpt | 2_members_ensemble0_iter83.ckpt | 3.4199173e7 | 1.670248587e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter84.ckpt | 2_members_ensemble0_iter84.ckpt | 3.4199173e7 | 1.670248638e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter85.ckpt | 2_members_ensemble0_iter85.ckpt | 3.4199173e7 | 1.67024869e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter86.ckpt | 2_members_ensemble0_iter86.ckpt | 3.4199173e7 | 1.670248743e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter87.ckpt | 2_members_ensemble0_iter87.ckpt | 3.4199173e7 | 1.670248795e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter88.ckpt | 2_members_ensemble0_iter88.ckpt | 3.4199173e7 | 1.670248847e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter89.ckpt | 2_members_ensemble0_iter89.ckpt | 3.4199173e7 | 1.670248899e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter9.ckpt | 2_members_ensemble0_iter9.ckpt | 3.4199173e7 | 1.670246611e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter90.ckpt | 2_members_ensemble0_iter90.ckpt | 3.4199173e7 | 1.670248951e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter91.ckpt | 2_members_ensemble0_iter91.ckpt | 3.4199173e7 | 1.670249003e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter92.ckpt | 2_members_ensemble0_iter92.ckpt | 3.4199173e7 | 1.670249055e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter93.ckpt | 2_members_ensemble0_iter93.ckpt | 3.4199173e7 | 1.670249107e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter94.ckpt | 2_members_ensemble0_iter94.ckpt | 3.4199173e7 | 1.670249159e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter95.ckpt | 2_members_ensemble0_iter95.ckpt | 3.4199173e7 | 1.670249211e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter96.ckpt | 2_members_ensemble0_iter96.ckpt | 3.4199173e7 | 1.670249263e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter97.ckpt | 2_members_ensemble0_iter97.ckpt | 3.4199173e7 | 1.670249315e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter98.ckpt | 2_members_ensemble0_iter98.ckpt | 3.4199173e7 | 1.670249367e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble0_iter99.ckpt | 2_members_ensemble0_iter99.ckpt | 3.4199173e7 | 1.670249419e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter0.ckpt | 2_members_ensemble1_iter0.ckpt | 3.4199173e7 | 1.670246372e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter1.ckpt | 2_members_ensemble1_iter1.ckpt | 3.4199173e7 | 1.670246399e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter10.ckpt | 2_members_ensemble1_iter10.ckpt | 3.4199173e7 | 1.670246638e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter11.ckpt | 2_members_ensemble1_iter11.ckpt | 3.4199173e7 | 1.670246664e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter12.ckpt | 2_members_ensemble1_iter12.ckpt | 3.4199173e7 | 1.67024669e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter13.ckpt | 2_members_ensemble1_iter13.ckpt | 3.4199173e7 | 1.670246717e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter14.ckpt | 2_members_ensemble1_iter14.ckpt | 3.4199173e7 | 1.670246743e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter15.ckpt | 2_members_ensemble1_iter15.ckpt | 3.4199173e7 | 1.67024677e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter16.ckpt | 2_members_ensemble1_iter16.ckpt | 3.4199173e7 | 1.670246797e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter17.ckpt | 2_members_ensemble1_iter17.ckpt | 3.4199173e7 | 1.670246823e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter18.ckpt | 2_members_ensemble1_iter18.ckpt | 3.4199173e7 | 1.670246849e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter19.ckpt | 2_members_ensemble1_iter19.ckpt | 3.4199173e7 | 1.670246876e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter2.ckpt | 2_members_ensemble1_iter2.ckpt | 3.4199173e7 | 1.670246425e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter20.ckpt | 2_members_ensemble1_iter20.ckpt | 3.4199173e7 | 1.670246902e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter21.ckpt | 2_members_ensemble1_iter21.ckpt | 3.4199173e7 | 1.670246929e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter22.ckpt | 2_members_ensemble1_iter22.ckpt | 3.4199173e7 | 1.670246955e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter23.ckpt | 2_members_ensemble1_iter23.ckpt | 3.4199173e7 | 1.670246981e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter24.ckpt | 2_members_ensemble1_iter24.ckpt | 3.4199173e7 | 1.670247008e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter25.ckpt | 2_members_ensemble1_iter25.ckpt | 3.4199173e7 | 1.670247034e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter26.ckpt | 2_members_ensemble1_iter26.ckpt | 3.4199173e7 | 1.670247061e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter27.ckpt | 2_members_ensemble1_iter27.ckpt | 3.4199173e7 | 1.670247087e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter28.ckpt | 2_members_ensemble1_iter28.ckpt | 3.4199173e7 | 1.670247114e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter29.ckpt | 2_members_ensemble1_iter29.ckpt | 3.4199173e7 | 1.670247141e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter3.ckpt | 2_members_ensemble1_iter3.ckpt | 3.4199173e7 | 1.670246452e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter30.ckpt | 2_members_ensemble1_iter30.ckpt | 3.4199173e7 | 1.670247167e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter31.ckpt | 2_members_ensemble1_iter31.ckpt | 3.4199173e7 | 1.670247194e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter32.ckpt | 2_members_ensemble1_iter32.ckpt | 3.4199173e7 | 1.67024722e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter33.ckpt | 2_members_ensemble1_iter33.ckpt | 3.4199173e7 | 1.670247247e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter34.ckpt | 2_members_ensemble1_iter34.ckpt | 3.4199173e7 | 1.670247273e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter35.ckpt | 2_members_ensemble1_iter35.ckpt | 3.4199173e7 | 1.670247299e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter36.ckpt | 2_members_ensemble1_iter36.ckpt | 3.4199173e7 | 1.670247326e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter37.ckpt | 2_members_ensemble1_iter37.ckpt | 3.4199173e7 | 1.670247352e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter38.ckpt | 2_members_ensemble1_iter38.ckpt | 3.4199173e7 | 1.670247378e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter39.ckpt | 2_members_ensemble1_iter39.ckpt | 3.4199173e7 | 1.670247405e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter4.ckpt | 2_members_ensemble1_iter4.ckpt | 3.4199173e7 | 1.670246478e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter40.ckpt | 2_members_ensemble1_iter40.ckpt | 3.4199173e7 | 1.670247431e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter41.ckpt | 2_members_ensemble1_iter41.ckpt | 3.4199173e7 | 1.670247457e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter42.ckpt | 2_members_ensemble1_iter42.ckpt | 3.4199173e7 | 1.670247484e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter43.ckpt | 2_members_ensemble1_iter43.ckpt | 3.4199173e7 | 1.670247511e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter44.ckpt | 2_members_ensemble1_iter44.ckpt | 3.4199173e7 | 1.670247537e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter45.ckpt | 2_members_ensemble1_iter45.ckpt | 3.4199173e7 | 1.670247563e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter46.ckpt | 2_members_ensemble1_iter46.ckpt | 3.4199173e7 | 1.67024759e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter47.ckpt | 2_members_ensemble1_iter47.ckpt | 3.4199173e7 | 1.670247616e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter48.ckpt | 2_members_ensemble1_iter48.ckpt | 3.4199173e7 | 1.670247642e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter49.ckpt | 2_members_ensemble1_iter49.ckpt | 3.4199173e7 | 1.670247669e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter5.ckpt | 2_members_ensemble1_iter5.ckpt | 3.4199173e7 | 1.670246505e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter50.ckpt | 2_members_ensemble1_iter50.ckpt | 3.4199173e7 | 1.670247695e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter51.ckpt | 2_members_ensemble1_iter51.ckpt | 3.4199173e7 | 1.670247721e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter52.ckpt | 2_members_ensemble1_iter52.ckpt | 3.4199173e7 | 1.670247748e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter53.ckpt | 2_members_ensemble1_iter53.ckpt | 3.4199173e7 | 1.670247774e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter54.ckpt | 2_members_ensemble1_iter54.ckpt | 3.4199173e7 | 1.670247801e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter55.ckpt | 2_members_ensemble1_iter55.ckpt | 3.4199173e7 | 1.670247828e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter56.ckpt | 2_members_ensemble1_iter56.ckpt | 3.4199173e7 | 1.670247855e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter57.ckpt | 2_members_ensemble1_iter57.ckpt | 3.4199173e7 | 1.670247881e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter58.ckpt | 2_members_ensemble1_iter58.ckpt | 3.4199173e7 | 1.670247908e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter59.ckpt | 2_members_ensemble1_iter59.ckpt | 3.4199173e7 | 1.670247934e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter6.ckpt | 2_members_ensemble1_iter6.ckpt | 3.4199173e7 | 1.670246531e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter60.ckpt | 2_members_ensemble1_iter60.ckpt | 3.4199173e7 | 1.67024796e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter61.ckpt | 2_members_ensemble1_iter61.ckpt | 3.4199173e7 | 1.670247987e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter62.ckpt | 2_members_ensemble1_iter62.ckpt | 3.4199173e7 | 1.670248013e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter63.ckpt | 2_members_ensemble1_iter63.ckpt | 3.4199173e7 | 1.67024804e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter64.ckpt | 2_members_ensemble1_iter64.ckpt | 3.4199173e7 | 1.670248066e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter65.ckpt | 2_members_ensemble1_iter65.ckpt | 3.4199173e7 | 1.670248093e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter66.ckpt | 2_members_ensemble1_iter66.ckpt | 3.4199173e7 | 1.670248119e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter67.ckpt | 2_members_ensemble1_iter67.ckpt | 3.4199173e7 | 1.670248145e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter68.ckpt | 2_members_ensemble1_iter68.ckpt | 3.4199173e7 | 1.670248172e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter69.ckpt | 2_members_ensemble1_iter69.ckpt | 3.4199173e7 | 1.670248198e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter7.ckpt | 2_members_ensemble1_iter7.ckpt | 3.4199173e7 | 1.670246558e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter70.ckpt | 2_members_ensemble1_iter70.ckpt | 3.4199173e7 | 1.670248225e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter71.ckpt | 2_members_ensemble1_iter71.ckpt | 3.4199173e7 | 1.670248253e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter72.ckpt | 2_members_ensemble1_iter72.ckpt | 3.4199173e7 | 1.670248279e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter73.ckpt | 2_members_ensemble1_iter73.ckpt | 3.4199173e7 | 1.670248306e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter74.ckpt | 2_members_ensemble1_iter74.ckpt | 3.4199173e7 | 1.670248332e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter75.ckpt | 2_members_ensemble1_iter75.ckpt | 3.4199173e7 | 1.670248359e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter76.ckpt | 2_members_ensemble1_iter76.ckpt | 3.4199173e7 | 1.670248385e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter77.ckpt | 2_members_ensemble1_iter77.ckpt | 3.4199173e7 | 1.670248411e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter78.ckpt | 2_members_ensemble1_iter78.ckpt | 3.4199173e7 | 1.670248438e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter79.ckpt | 2_members_ensemble1_iter79.ckpt | 3.4199173e7 | 1.670248464e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter8.ckpt | 2_members_ensemble1_iter8.ckpt | 3.4199173e7 | 1.670246584e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter80.ckpt | 2_members_ensemble1_iter80.ckpt | 3.4199173e7 | 1.67024849e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter81.ckpt | 2_members_ensemble1_iter81.ckpt | 3.4199173e7 | 1.670248517e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter82.ckpt | 2_members_ensemble1_iter82.ckpt | 3.4199173e7 | 1.670248543e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter83.ckpt | 2_members_ensemble1_iter83.ckpt | 3.4199173e7 | 1.670248587e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter84.ckpt | 2_members_ensemble1_iter84.ckpt | 3.4199173e7 | 1.670248639e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter85.ckpt | 2_members_ensemble1_iter85.ckpt | 3.4199173e7 | 1.670248691e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter86.ckpt | 2_members_ensemble1_iter86.ckpt | 3.4199173e7 | 1.670248743e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter87.ckpt | 2_members_ensemble1_iter87.ckpt | 3.4199173e7 | 1.670248795e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter88.ckpt | 2_members_ensemble1_iter88.ckpt | 3.4199173e7 | 1.670248848e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter89.ckpt | 2_members_ensemble1_iter89.ckpt | 3.4199173e7 | 1.6702489e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter9.ckpt | 2_members_ensemble1_iter9.ckpt | 3.4199173e7 | 1.670246611e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter90.ckpt | 2_members_ensemble1_iter90.ckpt | 3.4199173e7 | 1.670248952e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter91.ckpt | 2_members_ensemble1_iter91.ckpt | 3.4199173e7 | 1.670249004e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter92.ckpt | 2_members_ensemble1_iter92.ckpt | 3.4199173e7 | 1.670249056e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter93.ckpt | 2_members_ensemble1_iter93.ckpt | 3.4199173e7 | 1.670249108e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter94.ckpt | 2_members_ensemble1_iter94.ckpt | 3.4199173e7 | 1.670249159e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter95.ckpt | 2_members_ensemble1_iter95.ckpt | 3.4199173e7 | 1.670249211e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter96.ckpt | 2_members_ensemble1_iter96.ckpt | 3.4199173e7 | 1.670249263e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter97.ckpt | 2_members_ensemble1_iter97.ckpt | 3.4199173e7 | 1.670249316e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter98.ckpt | 2_members_ensemble1_iter98.ckpt | 3.4199173e7 | 1.670249367e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/2_members_ensemble1_iter99.ckpt | 2_members_ensemble1_iter99.ckpt | 3.4199173e7 | 1.670249419e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter0.ckpt | 3_members_ensemble0_iter0.ckpt | 3.4199173e7 | 1.670249475e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter1.ckpt | 3_members_ensemble0_iter1.ckpt | 3.4199173e7 | 1.670249537e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter10.ckpt | 3_members_ensemble0_iter10.ckpt | 3.4199173e7 | 1.670250158e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter11.ckpt | 3_members_ensemble0_iter11.ckpt | 3.4199173e7 | 1.67025022e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter12.ckpt | 3_members_ensemble0_iter12.ckpt | 3.4199173e7 | 1.670250282e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter13.ckpt | 3_members_ensemble0_iter13.ckpt | 3.4199173e7 | 1.670250345e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter14.ckpt | 3_members_ensemble0_iter14.ckpt | 3.4199173e7 | 1.670250407e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter15.ckpt | 3_members_ensemble0_iter15.ckpt | 3.4199173e7 | 1.670250469e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter16.ckpt | 3_members_ensemble0_iter16.ckpt | 3.4199173e7 | 1.670250531e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter17.ckpt | 3_members_ensemble0_iter17.ckpt | 3.4199173e7 | 1.670250594e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter18.ckpt | 3_members_ensemble0_iter18.ckpt | 3.4199173e7 | 1.670250656e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter19.ckpt | 3_members_ensemble0_iter19.ckpt | 3.4199173e7 | 1.670250717e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter2.ckpt | 3_members_ensemble0_iter2.ckpt | 3.4199173e7 | 1.670249657e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter20.ckpt | 3_members_ensemble0_iter20.ckpt | 3.4199173e7 | 1.670250779e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter21.ckpt | 3_members_ensemble0_iter21.ckpt | 3.4199173e7 | 1.670250841e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter22.ckpt | 3_members_ensemble0_iter22.ckpt | 3.4199173e7 | 1.670250903e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter23.ckpt | 3_members_ensemble0_iter23.ckpt | 3.4199173e7 | 1.670250966e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter24.ckpt | 3_members_ensemble0_iter24.ckpt | 3.4199173e7 | 1.670251028e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter25.ckpt | 3_members_ensemble0_iter25.ckpt | 3.4199173e7 | 1.67025109e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter26.ckpt | 3_members_ensemble0_iter26.ckpt | 3.4199173e7 | 1.670251152e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter27.ckpt | 3_members_ensemble0_iter27.ckpt | 3.4199173e7 | 1.670251214e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter28.ckpt | 3_members_ensemble0_iter28.ckpt | 3.4199173e7 | 1.670251276e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter29.ckpt | 3_members_ensemble0_iter29.ckpt | 3.4199173e7 | 1.670251339e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter3.ckpt | 3_members_ensemble0_iter3.ckpt | 3.4199173e7 | 1.67024972e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter30.ckpt | 3_members_ensemble0_iter30.ckpt | 3.4199173e7 | 1.670251401e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter31.ckpt | 3_members_ensemble0_iter31.ckpt | 3.4199173e7 | 1.670251464e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter32.ckpt | 3_members_ensemble0_iter32.ckpt | 3.4199173e7 | 1.670251496e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter33.ckpt | 3_members_ensemble0_iter33.ckpt | 3.4199173e7 | 1.670251526e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter34.ckpt | 3_members_ensemble0_iter34.ckpt | 3.4199173e7 | 1.670251558e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter35.ckpt | 3_members_ensemble0_iter35.ckpt | 3.4199173e7 | 1.670251587e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter36.ckpt | 3_members_ensemble0_iter36.ckpt | 3.4199173e7 | 1.670251617e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter37.ckpt | 3_members_ensemble0_iter37.ckpt | 3.4199173e7 | 1.670251647e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter38.ckpt | 3_members_ensemble0_iter38.ckpt | 3.4199173e7 | 1.670251676e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter39.ckpt | 3_members_ensemble0_iter39.ckpt | 3.4199173e7 | 1.670251706e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter4.ckpt | 3_members_ensemble0_iter4.ckpt | 3.4199173e7 | 1.670249784e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter40.ckpt | 3_members_ensemble0_iter40.ckpt | 3.4199173e7 | 1.670251735e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter41.ckpt | 3_members_ensemble0_iter41.ckpt | 3.4199173e7 | 1.670251766e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter42.ckpt | 3_members_ensemble0_iter42.ckpt | 3.4199173e7 | 1.670251796e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter43.ckpt | 3_members_ensemble0_iter43.ckpt | 3.4199173e7 | 1.670251825e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter44.ckpt | 3_members_ensemble0_iter44.ckpt | 3.4199173e7 | 1.670251855e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter45.ckpt | 3_members_ensemble0_iter45.ckpt | 3.4199173e7 | 1.670251931e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter46.ckpt | 3_members_ensemble0_iter46.ckpt | 3.4199173e7 | 1.670251964e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter47.ckpt | 3_members_ensemble0_iter47.ckpt | 3.4199173e7 | 1.670251993e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter48.ckpt | 3_members_ensemble0_iter48.ckpt | 3.4199173e7 | 1.670252023e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter49.ckpt | 3_members_ensemble0_iter49.ckpt | 3.4199173e7 | 1.670252052e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter5.ckpt | 3_members_ensemble0_iter5.ckpt | 3.4199173e7 | 1.670249846e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter50.ckpt | 3_members_ensemble0_iter50.ckpt | 3.4199173e7 | 1.670252081e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter51.ckpt | 3_members_ensemble0_iter51.ckpt | 3.4199173e7 | 1.67025211e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter52.ckpt | 3_members_ensemble0_iter52.ckpt | 3.4199173e7 | 1.670252149e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter53.ckpt | 3_members_ensemble0_iter53.ckpt | 3.4199173e7 | 1.670252188e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter54.ckpt | 3_members_ensemble0_iter54.ckpt | 3.4199173e7 | 1.670252226e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter55.ckpt | 3_members_ensemble0_iter55.ckpt | 3.4199173e7 | 1.670252265e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter56.ckpt | 3_members_ensemble0_iter56.ckpt | 3.4199173e7 | 1.670252304e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter57.ckpt | 3_members_ensemble0_iter57.ckpt | 3.4199173e7 | 1.670252341e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter58.ckpt | 3_members_ensemble0_iter58.ckpt | 3.4199173e7 | 1.67025237e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter59.ckpt | 3_members_ensemble0_iter59.ckpt | 3.4199173e7 | 1.6702524e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter6.ckpt | 3_members_ensemble0_iter6.ckpt | 3.4199173e7 | 1.670249909e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter60.ckpt | 3_members_ensemble0_iter60.ckpt | 3.4199173e7 | 1.67025243e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter61.ckpt | 3_members_ensemble0_iter61.ckpt | 3.4199173e7 | 1.670252468e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter62.ckpt | 3_members_ensemble0_iter62.ckpt | 3.4199173e7 | 1.670252507e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter63.ckpt | 3_members_ensemble0_iter63.ckpt | 3.4199173e7 | 1.670252546e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter64.ckpt | 3_members_ensemble0_iter64.ckpt | 3.4199173e7 | 1.670252582e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter65.ckpt | 3_members_ensemble0_iter65.ckpt | 3.4199173e7 | 1.670252613e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter66.ckpt | 3_members_ensemble0_iter66.ckpt | 3.4199173e7 | 1.670252642e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter67.ckpt | 3_members_ensemble0_iter67.ckpt | 3.4199173e7 | 1.670252671e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter68.ckpt | 3_members_ensemble0_iter68.ckpt | 3.4199173e7 | 1.670252706e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter69.ckpt | 3_members_ensemble0_iter69.ckpt | 3.4199173e7 | 1.670252749e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter7.ckpt | 3_members_ensemble0_iter7.ckpt | 3.4199173e7 | 1.670249972e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter70.ckpt | 3_members_ensemble0_iter70.ckpt | 3.4199173e7 | 1.670252788e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter71.ckpt | 3_members_ensemble0_iter71.ckpt | 3.4199173e7 | 1.670252827e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter72.ckpt | 3_members_ensemble0_iter72.ckpt | 3.4199173e7 | 1.670252865e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter73.ckpt | 3_members_ensemble0_iter73.ckpt | 3.4199173e7 | 1.670252894e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter74.ckpt | 3_members_ensemble0_iter74.ckpt | 3.4199173e7 | 1.670252923e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter75.ckpt | 3_members_ensemble0_iter75.ckpt | 3.4199173e7 | 1.670252952e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter76.ckpt | 3_members_ensemble0_iter76.ckpt | 3.4199173e7 | 1.670252982e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter77.ckpt | 3_members_ensemble0_iter77.ckpt | 3.4199173e7 | 1.670253011e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter78.ckpt | 3_members_ensemble0_iter78.ckpt | 3.4199173e7 | 1.670253041e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter79.ckpt | 3_members_ensemble0_iter79.ckpt | 3.4199173e7 | 1.670253071e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter8.ckpt | 3_members_ensemble0_iter8.ckpt | 3.4199173e7 | 1.670250035e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter80.ckpt | 3_members_ensemble0_iter80.ckpt | 3.4199173e7 | 1.6702531e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter81.ckpt | 3_members_ensemble0_iter81.ckpt | 3.4199173e7 | 1.67025313e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter82.ckpt | 3_members_ensemble0_iter82.ckpt | 3.4199173e7 | 1.670253159e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter83.ckpt | 3_members_ensemble0_iter83.ckpt | 3.4199173e7 | 1.670253191e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter84.ckpt | 3_members_ensemble0_iter84.ckpt | 3.4199173e7 | 1.67025322e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter85.ckpt | 3_members_ensemble0_iter85.ckpt | 3.4199173e7 | 1.67025325e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter86.ckpt | 3_members_ensemble0_iter86.ckpt | 3.4199173e7 | 1.670253279e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter87.ckpt | 3_members_ensemble0_iter87.ckpt | 3.4199173e7 | 1.670253308e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter88.ckpt | 3_members_ensemble0_iter88.ckpt | 3.4199173e7 | 1.670253338e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter89.ckpt | 3_members_ensemble0_iter89.ckpt | 3.4199173e7 | 1.670253368e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter9.ckpt | 3_members_ensemble0_iter9.ckpt | 3.4199173e7 | 1.670250096e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter90.ckpt | 3_members_ensemble0_iter90.ckpt | 3.4199173e7 | 1.670253397e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter91.ckpt | 3_members_ensemble0_iter91.ckpt | 3.4199173e7 | 1.670253427e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter92.ckpt | 3_members_ensemble0_iter92.ckpt | 3.4199173e7 | 1.670253456e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter93.ckpt | 3_members_ensemble0_iter93.ckpt | 3.4199173e7 | 1.670253486e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter94.ckpt | 3_members_ensemble0_iter94.ckpt | 3.4199173e7 | 1.670253515e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter95.ckpt | 3_members_ensemble0_iter95.ckpt | 3.4199173e7 | 1.670253545e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter96.ckpt | 3_members_ensemble0_iter96.ckpt | 3.4199173e7 | 1.670253573e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter97.ckpt | 3_members_ensemble0_iter97.ckpt | 3.4199173e7 | 1.670253603e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter98.ckpt | 3_members_ensemble0_iter98.ckpt | 3.4199173e7 | 1.670253632e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble0_iter99.ckpt | 3_members_ensemble0_iter99.ckpt | 3.4199173e7 | 1.670253661e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter0.ckpt | 3_members_ensemble1_iter0.ckpt | 3.4199173e7 | 1.670249476e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter1.ckpt | 3_members_ensemble1_iter1.ckpt | 3.4199173e7 | 1.670249538e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter10.ckpt | 3_members_ensemble1_iter10.ckpt | 3.4199173e7 | 1.670250158e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter11.ckpt | 3_members_ensemble1_iter11.ckpt | 3.4199173e7 | 1.67025022e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter12.ckpt | 3_members_ensemble1_iter12.ckpt | 3.4199173e7 | 1.670250283e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter13.ckpt | 3_members_ensemble1_iter13.ckpt | 3.4199173e7 | 1.670250346e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter14.ckpt | 3_members_ensemble1_iter14.ckpt | 3.4199173e7 | 1.670250408e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter15.ckpt | 3_members_ensemble1_iter15.ckpt | 3.4199173e7 | 1.67025047e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter16.ckpt | 3_members_ensemble1_iter16.ckpt | 3.4199173e7 | 1.670250532e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter17.ckpt | 3_members_ensemble1_iter17.ckpt | 3.4199173e7 | 1.670250594e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter18.ckpt | 3_members_ensemble1_iter18.ckpt | 3.4199173e7 | 1.670250656e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter19.ckpt | 3_members_ensemble1_iter19.ckpt | 3.4199173e7 | 1.670250718e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter2.ckpt | 3_members_ensemble1_iter2.ckpt | 3.4199173e7 | 1.670249658e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter20.ckpt | 3_members_ensemble1_iter20.ckpt | 3.4199173e7 | 1.67025078e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter21.ckpt | 3_members_ensemble1_iter21.ckpt | 3.4199173e7 | 1.670250842e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter22.ckpt | 3_members_ensemble1_iter22.ckpt | 3.4199173e7 | 1.670250904e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter23.ckpt | 3_members_ensemble1_iter23.ckpt | 3.4199173e7 | 1.670250967e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter24.ckpt | 3_members_ensemble1_iter24.ckpt | 3.4199173e7 | 1.670251029e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter25.ckpt | 3_members_ensemble1_iter25.ckpt | 3.4199173e7 | 1.670251091e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter26.ckpt | 3_members_ensemble1_iter26.ckpt | 3.4199173e7 | 1.670251153e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter27.ckpt | 3_members_ensemble1_iter27.ckpt | 3.4199173e7 | 1.670251215e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter28.ckpt | 3_members_ensemble1_iter28.ckpt | 3.4199173e7 | 1.670251277e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter29.ckpt | 3_members_ensemble1_iter29.ckpt | 3.4199173e7 | 1.67025134e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter3.ckpt | 3_members_ensemble1_iter3.ckpt | 3.4199173e7 | 1.670249721e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter30.ckpt | 3_members_ensemble1_iter30.ckpt | 3.4199173e7 | 1.670251402e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter31.ckpt | 3_members_ensemble1_iter31.ckpt | 3.4199173e7 | 1.670251464e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter32.ckpt | 3_members_ensemble1_iter32.ckpt | 3.4199173e7 | 1.670251497e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter33.ckpt | 3_members_ensemble1_iter33.ckpt | 3.4199173e7 | 1.670251527e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter34.ckpt | 3_members_ensemble1_iter34.ckpt | 3.4199173e7 | 1.670251558e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter35.ckpt | 3_members_ensemble1_iter35.ckpt | 3.4199173e7 | 1.670251588e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter36.ckpt | 3_members_ensemble1_iter36.ckpt | 3.4199173e7 | 1.670251617e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter37.ckpt | 3_members_ensemble1_iter37.ckpt | 3.4199173e7 | 1.670251647e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter38.ckpt | 3_members_ensemble1_iter38.ckpt | 3.4199173e7 | 1.670251677e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter39.ckpt | 3_members_ensemble1_iter39.ckpt | 3.4199173e7 | 1.670251706e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter4.ckpt | 3_members_ensemble1_iter4.ckpt | 3.4199173e7 | 1.670249784e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter40.ckpt | 3_members_ensemble1_iter40.ckpt | 3.4199173e7 | 1.670251736e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter41.ckpt | 3_members_ensemble1_iter41.ckpt | 3.4199173e7 | 1.670251766e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter42.ckpt | 3_members_ensemble1_iter42.ckpt | 3.4199173e7 | 1.670251796e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter43.ckpt | 3_members_ensemble1_iter43.ckpt | 3.4199173e7 | 1.670251826e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter44.ckpt | 3_members_ensemble1_iter44.ckpt | 3.4199173e7 | 1.670251856e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter45.ckpt | 3_members_ensemble1_iter45.ckpt | 3.4199173e7 | 1.670251932e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter46.ckpt | 3_members_ensemble1_iter46.ckpt | 3.4199173e7 | 1.670251964e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter47.ckpt | 3_members_ensemble1_iter47.ckpt | 3.4199173e7 | 1.670251994e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter48.ckpt | 3_members_ensemble1_iter48.ckpt | 3.4199173e7 | 1.670252023e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter49.ckpt | 3_members_ensemble1_iter49.ckpt | 3.4199173e7 | 1.670252052e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter5.ckpt | 3_members_ensemble1_iter5.ckpt | 3.4199173e7 | 1.670249847e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter50.ckpt | 3_members_ensemble1_iter50.ckpt | 3.4199173e7 | 1.670252082e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter51.ckpt | 3_members_ensemble1_iter51.ckpt | 3.4199173e7 | 1.670252111e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter52.ckpt | 3_members_ensemble1_iter52.ckpt | 3.4199173e7 | 1.670252149e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter53.ckpt | 3_members_ensemble1_iter53.ckpt | 3.4199173e7 | 1.670252189e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter54.ckpt | 3_members_ensemble1_iter54.ckpt | 3.4199173e7 | 1.670252227e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter55.ckpt | 3_members_ensemble1_iter55.ckpt | 3.4199173e7 | 1.670252266e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter56.ckpt | 3_members_ensemble1_iter56.ckpt | 3.4199173e7 | 1.670252305e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter57.ckpt | 3_members_ensemble1_iter57.ckpt | 3.4199173e7 | 1.670252342e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter58.ckpt | 3_members_ensemble1_iter58.ckpt | 3.4199173e7 | 1.670252371e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter59.ckpt | 3_members_ensemble1_iter59.ckpt | 3.4199173e7 | 1.670252401e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter6.ckpt | 3_members_ensemble1_iter6.ckpt | 3.4199173e7 | 1.670249909e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter60.ckpt | 3_members_ensemble1_iter60.ckpt | 3.4199173e7 | 1.670252431e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter61.ckpt | 3_members_ensemble1_iter61.ckpt | 3.4199173e7 | 1.670252469e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter62.ckpt | 3_members_ensemble1_iter62.ckpt | 3.4199173e7 | 1.670252508e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter63.ckpt | 3_members_ensemble1_iter63.ckpt | 3.4199173e7 | 1.670252547e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter64.ckpt | 3_members_ensemble1_iter64.ckpt | 3.4199173e7 | 1.670252583e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter65.ckpt | 3_members_ensemble1_iter65.ckpt | 3.4199173e7 | 1.670252613e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter66.ckpt | 3_members_ensemble1_iter66.ckpt | 3.4199173e7 | 1.670252643e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter67.ckpt | 3_members_ensemble1_iter67.ckpt | 3.4199173e7 | 1.670252672e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter68.ckpt | 3_members_ensemble1_iter68.ckpt | 3.4199173e7 | 1.670252706e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter69.ckpt | 3_members_ensemble1_iter69.ckpt | 3.4199173e7 | 1.67025275e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter7.ckpt | 3_members_ensemble1_iter7.ckpt | 3.4199173e7 | 1.670249973e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter70.ckpt | 3_members_ensemble1_iter70.ckpt | 3.4199173e7 | 1.670252789e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter71.ckpt | 3_members_ensemble1_iter71.ckpt | 3.4199173e7 | 1.670252827e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter72.ckpt | 3_members_ensemble1_iter72.ckpt | 3.4199173e7 | 1.670252865e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter73.ckpt | 3_members_ensemble1_iter73.ckpt | 3.4199173e7 | 1.670252894e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter74.ckpt | 3_members_ensemble1_iter74.ckpt | 3.4199173e7 | 1.670252924e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter75.ckpt | 3_members_ensemble1_iter75.ckpt | 3.4199173e7 | 1.670252953e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter76.ckpt | 3_members_ensemble1_iter76.ckpt | 3.4199173e7 | 1.670252982e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter77.ckpt | 3_members_ensemble1_iter77.ckpt | 3.4199173e7 | 1.670253012e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter78.ckpt | 3_members_ensemble1_iter78.ckpt | 3.4199173e7 | 1.670253042e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter79.ckpt | 3_members_ensemble1_iter79.ckpt | 3.4199173e7 | 1.670253071e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter8.ckpt | 3_members_ensemble1_iter8.ckpt | 3.4199173e7 | 1.670250035e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter80.ckpt | 3_members_ensemble1_iter80.ckpt | 3.4199173e7 | 1.670253101e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter81.ckpt | 3_members_ensemble1_iter81.ckpt | 3.4199173e7 | 1.670253131e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter82.ckpt | 3_members_ensemble1_iter82.ckpt | 3.4199173e7 | 1.670253162e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter83.ckpt | 3_members_ensemble1_iter83.ckpt | 3.4199173e7 | 1.670253191e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter84.ckpt | 3_members_ensemble1_iter84.ckpt | 3.4199173e7 | 1.670253221e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter85.ckpt | 3_members_ensemble1_iter85.ckpt | 3.4199173e7 | 1.67025325e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter86.ckpt | 3_members_ensemble1_iter86.ckpt | 3.4199173e7 | 1.67025328e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter87.ckpt | 3_members_ensemble1_iter87.ckpt | 3.4199173e7 | 1.670253309e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter88.ckpt | 3_members_ensemble1_iter88.ckpt | 3.4199173e7 | 1.670253338e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter89.ckpt | 3_members_ensemble1_iter89.ckpt | 3.4199173e7 | 1.670253369e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter9.ckpt | 3_members_ensemble1_iter9.ckpt | 3.4199173e7 | 1.670250097e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter90.ckpt | 3_members_ensemble1_iter90.ckpt | 3.4199173e7 | 1.670253398e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter91.ckpt | 3_members_ensemble1_iter91.ckpt | 3.4199173e7 | 1.670253427e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter92.ckpt | 3_members_ensemble1_iter92.ckpt | 3.4199173e7 | 1.670253456e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter93.ckpt | 3_members_ensemble1_iter93.ckpt | 3.4199173e7 | 1.670253486e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter94.ckpt | 3_members_ensemble1_iter94.ckpt | 3.4199173e7 | 1.670253516e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter95.ckpt | 3_members_ensemble1_iter95.ckpt | 3.4199173e7 | 1.670253545e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter96.ckpt | 3_members_ensemble1_iter96.ckpt | 3.4199173e7 | 1.670253574e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter97.ckpt | 3_members_ensemble1_iter97.ckpt | 3.4199173e7 | 1.670253604e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter98.ckpt | 3_members_ensemble1_iter98.ckpt | 3.4199173e7 | 1.670253633e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble1_iter99.ckpt | 3_members_ensemble1_iter99.ckpt | 3.4199173e7 | 1.670253662e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter0.ckpt | 3_members_ensemble2_iter0.ckpt | 3.4199173e7 | 1.670249476e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter1.ckpt | 3_members_ensemble2_iter1.ckpt | 3.4199173e7 | 1.670249538e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter10.ckpt | 3_members_ensemble2_iter10.ckpt | 3.4199173e7 | 1.670250159e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter11.ckpt | 3_members_ensemble2_iter11.ckpt | 3.4199173e7 | 1.670250221e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter12.ckpt | 3_members_ensemble2_iter12.ckpt | 3.4199173e7 | 1.670250284e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter13.ckpt | 3_members_ensemble2_iter13.ckpt | 3.4199173e7 | 1.670250346e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter14.ckpt | 3_members_ensemble2_iter14.ckpt | 3.4199173e7 | 1.670250409e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter15.ckpt | 3_members_ensemble2_iter15.ckpt | 3.4199173e7 | 1.670250471e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter16.ckpt | 3_members_ensemble2_iter16.ckpt | 3.4199173e7 | 1.670250533e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter17.ckpt | 3_members_ensemble2_iter17.ckpt | 3.4199173e7 | 1.670250595e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter18.ckpt | 3_members_ensemble2_iter18.ckpt | 3.4199173e7 | 1.670250657e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter19.ckpt | 3_members_ensemble2_iter19.ckpt | 3.4199173e7 | 1.670250718e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter2.ckpt | 3_members_ensemble2_iter2.ckpt | 3.4199173e7 | 1.670249658e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter20.ckpt | 3_members_ensemble2_iter20.ckpt | 3.4199173e7 | 1.67025078e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter21.ckpt | 3_members_ensemble2_iter21.ckpt | 3.4199173e7 | 1.670250843e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter22.ckpt | 3_members_ensemble2_iter22.ckpt | 3.4199173e7 | 1.670250905e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter23.ckpt | 3_members_ensemble2_iter23.ckpt | 3.4199173e7 | 1.670250968e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter24.ckpt | 3_members_ensemble2_iter24.ckpt | 3.4199173e7 | 1.670251029e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter25.ckpt | 3_members_ensemble2_iter25.ckpt | 3.4199173e7 | 1.670251092e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter26.ckpt | 3_members_ensemble2_iter26.ckpt | 3.4199173e7 | 1.670251154e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter27.ckpt | 3_members_ensemble2_iter27.ckpt | 3.4199173e7 | 1.670251216e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter28.ckpt | 3_members_ensemble2_iter28.ckpt | 3.4199173e7 | 1.670251277e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter29.ckpt | 3_members_ensemble2_iter29.ckpt | 3.4199173e7 | 1.67025134e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter3.ckpt | 3_members_ensemble2_iter3.ckpt | 3.4199173e7 | 1.670249721e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter30.ckpt | 3_members_ensemble2_iter30.ckpt | 3.4199173e7 | 1.670251403e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter31.ckpt | 3_members_ensemble2_iter31.ckpt | 3.4199173e7 | 1.670251465e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter32.ckpt | 3_members_ensemble2_iter32.ckpt | 3.4199173e7 | 1.670251497e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter33.ckpt | 3_members_ensemble2_iter33.ckpt | 3.4199173e7 | 1.670251528e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter34.ckpt | 3_members_ensemble2_iter34.ckpt | 3.4199173e7 | 1.670251559e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter35.ckpt | 3_members_ensemble2_iter35.ckpt | 3.4199173e7 | 1.670251589e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter36.ckpt | 3_members_ensemble2_iter36.ckpt | 3.4199173e7 | 1.670251618e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter37.ckpt | 3_members_ensemble2_iter37.ckpt | 3.4199173e7 | 1.670251648e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter38.ckpt | 3_members_ensemble2_iter38.ckpt | 3.4199173e7 | 1.670251677e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter39.ckpt | 3_members_ensemble2_iter39.ckpt | 3.4199173e7 | 1.670251707e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter4.ckpt | 3_members_ensemble2_iter4.ckpt | 3.4199173e7 | 1.670249785e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter40.ckpt | 3_members_ensemble2_iter40.ckpt | 3.4199173e7 | 1.670251737e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter41.ckpt | 3_members_ensemble2_iter41.ckpt | 3.4199173e7 | 1.670251767e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter42.ckpt | 3_members_ensemble2_iter42.ckpt | 3.4199173e7 | 1.670251797e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter43.ckpt | 3_members_ensemble2_iter43.ckpt | 3.4199173e7 | 1.670251826e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter44.ckpt | 3_members_ensemble2_iter44.ckpt | 3.4199173e7 | 1.670251857e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter45.ckpt | 3_members_ensemble2_iter45.ckpt | 3.4199173e7 | 1.670251932e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter46.ckpt | 3_members_ensemble2_iter46.ckpt | 3.4199173e7 | 1.670251965e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter47.ckpt | 3_members_ensemble2_iter47.ckpt | 3.4199173e7 | 1.670251994e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter48.ckpt | 3_members_ensemble2_iter48.ckpt | 3.4199173e7 | 1.670252024e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter49.ckpt | 3_members_ensemble2_iter49.ckpt | 3.4199173e7 | 1.670252053e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter5.ckpt | 3_members_ensemble2_iter5.ckpt | 3.4199173e7 | 1.670249847e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter50.ckpt | 3_members_ensemble2_iter50.ckpt | 3.4199173e7 | 1.670252082e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter51.ckpt | 3_members_ensemble2_iter51.ckpt | 3.4199173e7 | 1.670252111e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter52.ckpt | 3_members_ensemble2_iter52.ckpt | 3.4199173e7 | 1.67025215e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter53.ckpt | 3_members_ensemble2_iter53.ckpt | 3.4199173e7 | 1.670252189e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter54.ckpt | 3_members_ensemble2_iter54.ckpt | 3.4199173e7 | 1.670252227e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter55.ckpt | 3_members_ensemble2_iter55.ckpt | 3.4199173e7 | 1.670252266e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter56.ckpt | 3_members_ensemble2_iter56.ckpt | 3.4199173e7 | 1.670252305e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter57.ckpt | 3_members_ensemble2_iter57.ckpt | 3.4199173e7 | 1.670252342e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter58.ckpt | 3_members_ensemble2_iter58.ckpt | 3.4199173e7 | 1.670252371e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter59.ckpt | 3_members_ensemble2_iter59.ckpt | 3.4199173e7 | 1.670252401e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter6.ckpt | 3_members_ensemble2_iter6.ckpt | 3.4199173e7 | 1.67024991e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter60.ckpt | 3_members_ensemble2_iter60.ckpt | 3.4199173e7 | 1.670252431e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter61.ckpt | 3_members_ensemble2_iter61.ckpt | 3.4199173e7 | 1.67025247e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter62.ckpt | 3_members_ensemble2_iter62.ckpt | 3.4199173e7 | 1.670252509e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter63.ckpt | 3_members_ensemble2_iter63.ckpt | 3.4199173e7 | 1.670252547e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter64.ckpt | 3_members_ensemble2_iter64.ckpt | 3.4199173e7 | 1.670252584e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter65.ckpt | 3_members_ensemble2_iter65.ckpt | 3.4199173e7 | 1.670252614e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter66.ckpt | 3_members_ensemble2_iter66.ckpt | 3.4199173e7 | 1.670252643e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter67.ckpt | 3_members_ensemble2_iter67.ckpt | 3.4199173e7 | 1.670252672e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter68.ckpt | 3_members_ensemble2_iter68.ckpt | 3.4199173e7 | 1.670252707e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter69.ckpt | 3_members_ensemble2_iter69.ckpt | 3.4199173e7 | 1.670252751e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter7.ckpt | 3_members_ensemble2_iter7.ckpt | 3.4199173e7 | 1.670249973e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter70.ckpt | 3_members_ensemble2_iter70.ckpt | 3.4199173e7 | 1.670252789e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter71.ckpt | 3_members_ensemble2_iter71.ckpt | 3.4199173e7 | 1.670252828e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter72.ckpt | 3_members_ensemble2_iter72.ckpt | 3.4199173e7 | 1.670252866e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter73.ckpt | 3_members_ensemble2_iter73.ckpt | 3.4199173e7 | 1.670252895e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter74.ckpt | 3_members_ensemble2_iter74.ckpt | 3.4199173e7 | 1.670252924e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter75.ckpt | 3_members_ensemble2_iter75.ckpt | 3.4199173e7 | 1.670252953e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter76.ckpt | 3_members_ensemble2_iter76.ckpt | 3.4199173e7 | 1.670252983e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter77.ckpt | 3_members_ensemble2_iter77.ckpt | 3.4199173e7 | 1.670253012e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter78.ckpt | 3_members_ensemble2_iter78.ckpt | 3.4199173e7 | 1.670253042e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter79.ckpt | 3_members_ensemble2_iter79.ckpt | 3.4199173e7 | 1.670253072e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter8.ckpt | 3_members_ensemble2_iter8.ckpt | 3.4199173e7 | 1.670250036e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter80.ckpt | 3_members_ensemble2_iter80.ckpt | 3.4199173e7 | 1.670253101e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter81.ckpt | 3_members_ensemble2_iter81.ckpt | 3.4199173e7 | 1.670253131e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter82.ckpt | 3_members_ensemble2_iter82.ckpt | 3.4199173e7 | 1.670253163e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter83.ckpt | 3_members_ensemble2_iter83.ckpt | 3.4199173e7 | 1.670253192e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter84.ckpt | 3_members_ensemble2_iter84.ckpt | 3.4199173e7 | 1.670253222e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter85.ckpt | 3_members_ensemble2_iter85.ckpt | 3.4199173e7 | 1.670253251e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter86.ckpt | 3_members_ensemble2_iter86.ckpt | 3.4199173e7 | 1.67025328e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter87.ckpt | 3_members_ensemble2_iter87.ckpt | 3.4199173e7 | 1.670253309e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter88.ckpt | 3_members_ensemble2_iter88.ckpt | 3.4199173e7 | 1.670253339e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter89.ckpt | 3_members_ensemble2_iter89.ckpt | 3.4199173e7 | 1.670253369e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter9.ckpt | 3_members_ensemble2_iter9.ckpt | 3.4199173e7 | 1.670250097e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter90.ckpt | 3_members_ensemble2_iter90.ckpt | 3.4199173e7 | 1.670253399e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter91.ckpt | 3_members_ensemble2_iter91.ckpt | 3.4199173e7 | 1.670253428e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter92.ckpt | 3_members_ensemble2_iter92.ckpt | 3.4199173e7 | 1.670253457e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter93.ckpt | 3_members_ensemble2_iter93.ckpt | 3.4199173e7 | 1.670253487e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter94.ckpt | 3_members_ensemble2_iter94.ckpt | 3.4199173e7 | 1.670253516e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter95.ckpt | 3_members_ensemble2_iter95.ckpt | 3.4199173e7 | 1.670253546e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter96.ckpt | 3_members_ensemble2_iter96.ckpt | 3.4199173e7 | 1.670253575e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter97.ckpt | 3_members_ensemble2_iter97.ckpt | 3.4199173e7 | 1.670253604e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter98.ckpt | 3_members_ensemble2_iter98.ckpt | 3.4199173e7 | 1.670253633e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/3_members_ensemble2_iter99.ckpt | 3_members_ensemble2_iter99.ckpt | 3.4199173e7 | 1.670253663e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter0.ckpt | 5_members_ensemble0_iter0.ckpt | 3.4199173e7 | 1.670253695e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter1.ckpt | 5_members_ensemble0_iter1.ckpt | 3.4199173e7 | 1.670253731e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter10.ckpt | 5_members_ensemble0_iter10.ckpt | 3.4199173e7 | 1.670254063e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter11.ckpt | 5_members_ensemble0_iter11.ckpt | 3.4199173e7 | 1.6702541e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter12.ckpt | 5_members_ensemble0_iter12.ckpt | 3.4199173e7 | 1.670254137e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter13.ckpt | 5_members_ensemble0_iter13.ckpt | 3.4199173e7 | 1.670254174e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter14.ckpt | 5_members_ensemble0_iter14.ckpt | 3.4199173e7 | 1.67025421e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter15.ckpt | 5_members_ensemble0_iter15.ckpt | 3.4199173e7 | 1.670254246e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter16.ckpt | 5_members_ensemble0_iter16.ckpt | 3.4199173e7 | 1.670254283e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter17.ckpt | 5_members_ensemble0_iter17.ckpt | 3.4199173e7 | 1.67025432e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter18.ckpt | 5_members_ensemble0_iter18.ckpt | 3.4199173e7 | 1.670254356e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter19.ckpt | 5_members_ensemble0_iter19.ckpt | 3.4199173e7 | 1.670254393e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter2.ckpt | 5_members_ensemble0_iter2.ckpt | 3.4199173e7 | 1.670253768e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter20.ckpt | 5_members_ensemble0_iter20.ckpt | 3.4199173e7 | 1.67025443e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter21.ckpt | 5_members_ensemble0_iter21.ckpt | 3.4199173e7 | 1.670254467e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter22.ckpt | 5_members_ensemble0_iter22.ckpt | 3.4199173e7 | 1.670254505e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter23.ckpt | 5_members_ensemble0_iter23.ckpt | 3.4199173e7 | 1.670254542e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter24.ckpt | 5_members_ensemble0_iter24.ckpt | 3.4199173e7 | 1.67025458e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter25.ckpt | 5_members_ensemble0_iter25.ckpt | 3.4199173e7 | 1.670254616e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter26.ckpt | 5_members_ensemble0_iter26.ckpt | 3.4199173e7 | 1.670254653e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter27.ckpt | 5_members_ensemble0_iter27.ckpt | 3.4199173e7 | 1.670254693e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter28.ckpt | 5_members_ensemble0_iter28.ckpt | 3.4199173e7 | 1.67025473e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter29.ckpt | 5_members_ensemble0_iter29.ckpt | 3.4199173e7 | 1.670254767e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter3.ckpt | 5_members_ensemble0_iter3.ckpt | 3.4199173e7 | 1.670253805e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter30.ckpt | 5_members_ensemble0_iter30.ckpt | 3.4199173e7 | 1.670254804e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter31.ckpt | 5_members_ensemble0_iter31.ckpt | 3.4199173e7 | 1.670254842e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter32.ckpt | 5_members_ensemble0_iter32.ckpt | 3.4199173e7 | 1.670254879e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter33.ckpt | 5_members_ensemble0_iter33.ckpt | 3.4199173e7 | 1.670254918e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter34.ckpt | 5_members_ensemble0_iter34.ckpt | 3.4199173e7 | 1.670254955e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter35.ckpt | 5_members_ensemble0_iter35.ckpt | 3.4199173e7 | 1.670254992e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter36.ckpt | 5_members_ensemble0_iter36.ckpt | 3.4199173e7 | 1.670255029e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter37.ckpt | 5_members_ensemble0_iter37.ckpt | 3.4199173e7 | 1.670255067e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter38.ckpt | 5_members_ensemble0_iter38.ckpt | 3.4199173e7 | 1.670255105e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter39.ckpt | 5_members_ensemble0_iter39.ckpt | 3.4199173e7 | 1.670255144e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter4.ckpt | 5_members_ensemble0_iter4.ckpt | 3.4199173e7 | 1.670253842e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter40.ckpt | 5_members_ensemble0_iter40.ckpt | 3.4199173e7 | 1.670255184e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter41.ckpt | 5_members_ensemble0_iter41.ckpt | 3.4199173e7 | 1.670255222e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter42.ckpt | 5_members_ensemble0_iter42.ckpt | 3.4199173e7 | 1.670255261e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter43.ckpt | 5_members_ensemble0_iter43.ckpt | 3.4199173e7 | 1.670255298e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter44.ckpt | 5_members_ensemble0_iter44.ckpt | 3.4199173e7 | 1.670255336e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter45.ckpt | 5_members_ensemble0_iter45.ckpt | 3.4199173e7 | 1.670255374e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter46.ckpt | 5_members_ensemble0_iter46.ckpt | 3.4199173e7 | 1.67025541e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter47.ckpt | 5_members_ensemble0_iter47.ckpt | 3.4199173e7 | 1.670255448e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter48.ckpt | 5_members_ensemble0_iter48.ckpt | 3.4199173e7 | 1.670255485e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter49.ckpt | 5_members_ensemble0_iter49.ckpt | 3.4199173e7 | 1.670255522e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter5.ckpt | 5_members_ensemble0_iter5.ckpt | 3.4199173e7 | 1.670253879e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter50.ckpt | 5_members_ensemble0_iter50.ckpt | 3.4199173e7 | 1.67025556e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter51.ckpt | 5_members_ensemble0_iter51.ckpt | 3.4199173e7 | 1.670255597e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter52.ckpt | 5_members_ensemble0_iter52.ckpt | 3.4199173e7 | 1.670255634e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter53.ckpt | 5_members_ensemble0_iter53.ckpt | 3.4199173e7 | 1.67025567e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter54.ckpt | 5_members_ensemble0_iter54.ckpt | 3.4199173e7 | 1.670255707e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter55.ckpt | 5_members_ensemble0_iter55.ckpt | 3.4199173e7 | 1.670255745e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter56.ckpt | 5_members_ensemble0_iter56.ckpt | 3.4199173e7 | 1.670255788e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter57.ckpt | 5_members_ensemble0_iter57.ckpt | 3.4199173e7 | 1.670255825e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter58.ckpt | 5_members_ensemble0_iter58.ckpt | 3.4199173e7 | 1.670255861e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter59.ckpt | 5_members_ensemble0_iter59.ckpt | 3.4199173e7 | 1.670255898e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter6.ckpt | 5_members_ensemble0_iter6.ckpt | 3.4199173e7 | 1.670253915e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter60.ckpt | 5_members_ensemble0_iter60.ckpt | 3.4199173e7 | 1.670255934e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter61.ckpt | 5_members_ensemble0_iter61.ckpt | 3.4199173e7 | 1.670255971e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter62.ckpt | 5_members_ensemble0_iter62.ckpt | 3.4199173e7 | 1.670256008e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter63.ckpt | 5_members_ensemble0_iter63.ckpt | 3.4199173e7 | 1.670256045e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter64.ckpt | 5_members_ensemble0_iter64.ckpt | 3.4199173e7 | 1.670256081e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter65.ckpt | 5_members_ensemble0_iter65.ckpt | 3.4199173e7 | 1.670256118e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter66.ckpt | 5_members_ensemble0_iter66.ckpt | 3.4199173e7 | 1.670256156e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter67.ckpt | 5_members_ensemble0_iter67.ckpt | 3.4199173e7 | 1.670256192e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter68.ckpt | 5_members_ensemble0_iter68.ckpt | 3.4199173e7 | 1.670256229e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter69.ckpt | 5_members_ensemble0_iter69.ckpt | 3.4199173e7 | 1.670256266e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter7.ckpt | 5_members_ensemble0_iter7.ckpt | 3.4199173e7 | 1.670253952e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter70.ckpt | 5_members_ensemble0_iter70.ckpt | 3.4199173e7 | 1.670256303e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter71.ckpt | 5_members_ensemble0_iter71.ckpt | 3.4199173e7 | 1.67025634e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter72.ckpt | 5_members_ensemble0_iter72.ckpt | 3.4199173e7 | 1.670256377e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter73.ckpt | 5_members_ensemble0_iter73.ckpt | 3.4199173e7 | 1.670256413e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter74.ckpt | 5_members_ensemble0_iter74.ckpt | 3.4199173e7 | 1.67025645e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter75.ckpt | 5_members_ensemble0_iter75.ckpt | 3.4199173e7 | 1.670256487e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter76.ckpt | 5_members_ensemble0_iter76.ckpt | 3.4199173e7 | 1.670256531e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter77.ckpt | 5_members_ensemble0_iter77.ckpt | 3.4199173e7 | 1.670256579e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter78.ckpt | 5_members_ensemble0_iter78.ckpt | 3.4199173e7 | 1.670256627e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter79.ckpt | 5_members_ensemble0_iter79.ckpt | 3.4199173e7 | 1.670256665e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter8.ckpt | 5_members_ensemble0_iter8.ckpt | 3.4199173e7 | 1.670253989e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter80.ckpt | 5_members_ensemble0_iter80.ckpt | 3.4199173e7 | 1.670256701e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter81.ckpt | 5_members_ensemble0_iter81.ckpt | 3.4199173e7 | 1.670256736e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter82.ckpt | 5_members_ensemble0_iter82.ckpt | 3.4199173e7 | 1.670256773e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter83.ckpt | 5_members_ensemble0_iter83.ckpt | 3.4199173e7 | 1.67025681e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter84.ckpt | 5_members_ensemble0_iter84.ckpt | 3.4199173e7 | 1.67025685e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter85.ckpt | 5_members_ensemble0_iter85.ckpt | 3.4199173e7 | 1.670256886e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter86.ckpt | 5_members_ensemble0_iter86.ckpt | 3.4199173e7 | 1.670256922e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter87.ckpt | 5_members_ensemble0_iter87.ckpt | 3.4199173e7 | 1.670256959e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter88.ckpt | 5_members_ensemble0_iter88.ckpt | 3.4199173e7 | 1.670257007e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter89.ckpt | 5_members_ensemble0_iter89.ckpt | 3.4199173e7 | 1.670257054e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter9.ckpt | 5_members_ensemble0_iter9.ckpt | 3.4199173e7 | 1.670254026e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter90.ckpt | 5_members_ensemble0_iter90.ckpt | 3.4199173e7 | 1.670257097e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter91.ckpt | 5_members_ensemble0_iter91.ckpt | 3.4199173e7 | 1.670257132e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter92.ckpt | 5_members_ensemble0_iter92.ckpt | 3.4199173e7 | 1.670257176e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter93.ckpt | 5_members_ensemble0_iter93.ckpt | 3.4199173e7 | 1.670257223e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter94.ckpt | 5_members_ensemble0_iter94.ckpt | 3.4199173e7 | 1.670257271e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter95.ckpt | 5_members_ensemble0_iter95.ckpt | 3.4199173e7 | 1.670257319e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter96.ckpt | 5_members_ensemble0_iter96.ckpt | 3.4199173e7 | 1.670257368e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter97.ckpt | 5_members_ensemble0_iter97.ckpt | 3.4199173e7 | 1.67025741e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter98.ckpt | 5_members_ensemble0_iter98.ckpt | 3.4199173e7 | 1.670257448e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble0_iter99.ckpt | 5_members_ensemble0_iter99.ckpt | 3.4199173e7 | 1.670257484e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter0.ckpt | 5_members_ensemble1_iter0.ckpt | 3.4199173e7 | 1.670253695e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter1.ckpt | 5_members_ensemble1_iter1.ckpt | 3.4199173e7 | 1.670253732e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter10.ckpt | 5_members_ensemble1_iter10.ckpt | 3.4199173e7 | 1.670254064e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter11.ckpt | 5_members_ensemble1_iter11.ckpt | 3.4199173e7 | 1.670254101e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter12.ckpt | 5_members_ensemble1_iter12.ckpt | 3.4199173e7 | 1.670254138e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter13.ckpt | 5_members_ensemble1_iter13.ckpt | 3.4199173e7 | 1.670254174e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter14.ckpt | 5_members_ensemble1_iter14.ckpt | 3.4199173e7 | 1.670254211e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter15.ckpt | 5_members_ensemble1_iter15.ckpt | 3.4199173e7 | 1.670254247e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter16.ckpt | 5_members_ensemble1_iter16.ckpt | 3.4199173e7 | 1.670254284e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter17.ckpt | 5_members_ensemble1_iter17.ckpt | 3.4199173e7 | 1.67025432e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter18.ckpt | 5_members_ensemble1_iter18.ckpt | 3.4199173e7 | 1.670254357e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter19.ckpt | 5_members_ensemble1_iter19.ckpt | 3.4199173e7 | 1.670254394e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter2.ckpt | 5_members_ensemble1_iter2.ckpt | 3.4199173e7 | 1.670253769e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter20.ckpt | 5_members_ensemble1_iter20.ckpt | 3.4199173e7 | 1.670254431e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter21.ckpt | 5_members_ensemble1_iter21.ckpt | 3.4199173e7 | 1.670254467e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter22.ckpt | 5_members_ensemble1_iter22.ckpt | 3.4199173e7 | 1.670254506e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter23.ckpt | 5_members_ensemble1_iter23.ckpt | 3.4199173e7 | 1.670254543e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter24.ckpt | 5_members_ensemble1_iter24.ckpt | 3.4199173e7 | 1.67025458e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter25.ckpt | 5_members_ensemble1_iter25.ckpt | 3.4199173e7 | 1.670254617e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter26.ckpt | 5_members_ensemble1_iter26.ckpt | 3.4199173e7 | 1.670254654e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter27.ckpt | 5_members_ensemble1_iter27.ckpt | 3.4199173e7 | 1.670254693e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter28.ckpt | 5_members_ensemble1_iter28.ckpt | 3.4199173e7 | 1.67025473e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter29.ckpt | 5_members_ensemble1_iter29.ckpt | 3.4199173e7 | 1.670254768e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter3.ckpt | 5_members_ensemble1_iter3.ckpt | 3.4199173e7 | 1.670253805e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter30.ckpt | 5_members_ensemble1_iter30.ckpt | 3.4199173e7 | 1.670254805e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter31.ckpt | 5_members_ensemble1_iter31.ckpt | 3.4199173e7 | 1.670254843e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter32.ckpt | 5_members_ensemble1_iter32.ckpt | 3.4199173e7 | 1.67025488e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter33.ckpt | 5_members_ensemble1_iter33.ckpt | 3.4199173e7 | 1.670254919e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter34.ckpt | 5_members_ensemble1_iter34.ckpt | 3.4199173e7 | 1.670254956e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter35.ckpt | 5_members_ensemble1_iter35.ckpt | 3.4199173e7 | 1.670254992e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter36.ckpt | 5_members_ensemble1_iter36.ckpt | 3.4199173e7 | 1.67025503e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter37.ckpt | 5_members_ensemble1_iter37.ckpt | 3.4199173e7 | 1.670255068e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter38.ckpt | 5_members_ensemble1_iter38.ckpt | 3.4199173e7 | 1.670255108e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter39.ckpt | 5_members_ensemble1_iter39.ckpt | 3.4199173e7 | 1.670255145e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter4.ckpt | 5_members_ensemble1_iter4.ckpt | 3.4199173e7 | 1.670253843e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter40.ckpt | 5_members_ensemble1_iter40.ckpt | 3.4199173e7 | 1.670255185e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter41.ckpt | 5_members_ensemble1_iter41.ckpt | 3.4199173e7 | 1.670255223e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter42.ckpt | 5_members_ensemble1_iter42.ckpt | 3.4199173e7 | 1.670255261e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter43.ckpt | 5_members_ensemble1_iter43.ckpt | 3.4199173e7 | 1.670255298e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter44.ckpt | 5_members_ensemble1_iter44.ckpt | 3.4199173e7 | 1.670255337e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter45.ckpt | 5_members_ensemble1_iter45.ckpt | 3.4199173e7 | 1.670255374e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter46.ckpt | 5_members_ensemble1_iter46.ckpt | 3.4199173e7 | 1.670255411e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter47.ckpt | 5_members_ensemble1_iter47.ckpt | 3.4199173e7 | 1.670255448e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter48.ckpt | 5_members_ensemble1_iter48.ckpt | 3.4199173e7 | 1.670255486e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter49.ckpt | 5_members_ensemble1_iter49.ckpt | 3.4199173e7 | 1.670255522e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter5.ckpt | 5_members_ensemble1_iter5.ckpt | 3.4199173e7 | 1.670253879e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter50.ckpt | 5_members_ensemble1_iter50.ckpt | 3.4199173e7 | 1.670255561e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter51.ckpt | 5_members_ensemble1_iter51.ckpt | 3.4199173e7 | 1.670255598e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter52.ckpt | 5_members_ensemble1_iter52.ckpt | 3.4199173e7 | 1.670255635e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter53.ckpt | 5_members_ensemble1_iter53.ckpt | 3.4199173e7 | 1.670255671e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter54.ckpt | 5_members_ensemble1_iter54.ckpt | 3.4199173e7 | 1.670255708e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter55.ckpt | 5_members_ensemble1_iter55.ckpt | 3.4199173e7 | 1.670255745e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter56.ckpt | 5_members_ensemble1_iter56.ckpt | 3.4199173e7 | 1.670255789e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter57.ckpt | 5_members_ensemble1_iter57.ckpt | 3.4199173e7 | 1.670255825e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter58.ckpt | 5_members_ensemble1_iter58.ckpt | 3.4199173e7 | 1.670255862e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter59.ckpt | 5_members_ensemble1_iter59.ckpt | 3.4199173e7 | 1.670255898e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter6.ckpt | 5_members_ensemble1_iter6.ckpt | 3.4199173e7 | 1.670253916e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter60.ckpt | 5_members_ensemble1_iter60.ckpt | 3.4199173e7 | 1.670255935e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter61.ckpt | 5_members_ensemble1_iter61.ckpt | 3.4199173e7 | 1.670255971e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter62.ckpt | 5_members_ensemble1_iter62.ckpt | 3.4199173e7 | 1.670256008e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter63.ckpt | 5_members_ensemble1_iter63.ckpt | 3.4199173e7 | 1.670256045e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter64.ckpt | 5_members_ensemble1_iter64.ckpt | 3.4199173e7 | 1.670256082e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter65.ckpt | 5_members_ensemble1_iter65.ckpt | 3.4199173e7 | 1.670256119e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter66.ckpt | 5_members_ensemble1_iter66.ckpt | 3.4199173e7 | 1.670256156e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter67.ckpt | 5_members_ensemble1_iter67.ckpt | 3.4199173e7 | 1.670256193e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter68.ckpt | 5_members_ensemble1_iter68.ckpt | 3.4199173e7 | 1.67025623e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter69.ckpt | 5_members_ensemble1_iter69.ckpt | 3.4199173e7 | 1.670256267e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter7.ckpt | 5_members_ensemble1_iter7.ckpt | 3.4199173e7 | 1.670253953e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter70.ckpt | 5_members_ensemble1_iter70.ckpt | 3.4199173e7 | 1.670256304e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter71.ckpt | 5_members_ensemble1_iter71.ckpt | 3.4199173e7 | 1.670256341e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter72.ckpt | 5_members_ensemble1_iter72.ckpt | 3.4199173e7 | 1.670256377e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter73.ckpt | 5_members_ensemble1_iter73.ckpt | 3.4199173e7 | 1.670256414e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter74.ckpt | 5_members_ensemble1_iter74.ckpt | 3.4199173e7 | 1.670256451e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter75.ckpt | 5_members_ensemble1_iter75.ckpt | 3.4199173e7 | 1.670256488e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter76.ckpt | 5_members_ensemble1_iter76.ckpt | 3.4199173e7 | 1.670256531e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter77.ckpt | 5_members_ensemble1_iter77.ckpt | 3.4199173e7 | 1.67025658e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter78.ckpt | 5_members_ensemble1_iter78.ckpt | 3.4199173e7 | 1.670256627e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter79.ckpt | 5_members_ensemble1_iter79.ckpt | 3.4199173e7 | 1.670256665e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter8.ckpt | 5_members_ensemble1_iter8.ckpt | 3.4199173e7 | 1.67025399e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter80.ckpt | 5_members_ensemble1_iter80.ckpt | 3.4199173e7 | 1.670256701e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter81.ckpt | 5_members_ensemble1_iter81.ckpt | 3.4199173e7 | 1.670256737e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter82.ckpt | 5_members_ensemble1_iter82.ckpt | 3.4199173e7 | 1.670256774e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter83.ckpt | 5_members_ensemble1_iter83.ckpt | 3.4199173e7 | 1.670256811e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter84.ckpt | 5_members_ensemble1_iter84.ckpt | 3.4199173e7 | 1.67025685e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter85.ckpt | 5_members_ensemble1_iter85.ckpt | 3.4199173e7 | 1.670256887e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter86.ckpt | 5_members_ensemble1_iter86.ckpt | 3.4199173e7 | 1.670256922e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter87.ckpt | 5_members_ensemble1_iter87.ckpt | 3.4199173e7 | 1.67025696e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter88.ckpt | 5_members_ensemble1_iter88.ckpt | 3.4199173e7 | 1.670257008e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter89.ckpt | 5_members_ensemble1_iter89.ckpt | 3.4199173e7 | 1.670257055e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter9.ckpt | 5_members_ensemble1_iter9.ckpt | 3.4199173e7 | 1.670254027e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter90.ckpt | 5_members_ensemble1_iter90.ckpt | 3.4199173e7 | 1.670257097e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter91.ckpt | 5_members_ensemble1_iter91.ckpt | 3.4199173e7 | 1.670257133e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter92.ckpt | 5_members_ensemble1_iter92.ckpt | 3.4199173e7 | 1.670257176e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter93.ckpt | 5_members_ensemble1_iter93.ckpt | 3.4199173e7 | 1.670257224e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter94.ckpt | 5_members_ensemble1_iter94.ckpt | 3.4199173e7 | 1.670257272e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter95.ckpt | 5_members_ensemble1_iter95.ckpt | 3.4199173e7 | 1.670257319e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter96.ckpt | 5_members_ensemble1_iter96.ckpt | 3.4199173e7 | 1.670257369e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter97.ckpt | 5_members_ensemble1_iter97.ckpt | 3.4199173e7 | 1.670257411e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter98.ckpt | 5_members_ensemble1_iter98.ckpt | 3.4199173e7 | 1.670257448e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble1_iter99.ckpt | 5_members_ensemble1_iter99.ckpt | 3.4199173e7 | 1.670257485e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter0.ckpt | 5_members_ensemble2_iter0.ckpt | 3.4199173e7 | 1.670253696e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter1.ckpt | 5_members_ensemble2_iter1.ckpt | 3.4199173e7 | 1.670253732e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter10.ckpt | 5_members_ensemble2_iter10.ckpt | 3.4199173e7 | 1.670254064e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter11.ckpt | 5_members_ensemble2_iter11.ckpt | 3.4199173e7 | 1.670254101e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter12.ckpt | 5_members_ensemble2_iter12.ckpt | 3.4199173e7 | 1.670254138e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter13.ckpt | 5_members_ensemble2_iter13.ckpt | 3.4199173e7 | 1.670254175e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter14.ckpt | 5_members_ensemble2_iter14.ckpt | 3.4199173e7 | 1.670254212e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter15.ckpt | 5_members_ensemble2_iter15.ckpt | 3.4199173e7 | 1.670254248e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter16.ckpt | 5_members_ensemble2_iter16.ckpt | 3.4199173e7 | 1.670254284e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter17.ckpt | 5_members_ensemble2_iter17.ckpt | 3.4199173e7 | 1.670254321e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter18.ckpt | 5_members_ensemble2_iter18.ckpt | 3.4199173e7 | 1.670254358e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter19.ckpt | 5_members_ensemble2_iter19.ckpt | 3.4199173e7 | 1.670254394e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter2.ckpt | 5_members_ensemble2_iter2.ckpt | 3.4199173e7 | 1.670253769e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter20.ckpt | 5_members_ensemble2_iter20.ckpt | 3.4199173e7 | 1.670254432e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter21.ckpt | 5_members_ensemble2_iter21.ckpt | 3.4199173e7 | 1.670254468e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter22.ckpt | 5_members_ensemble2_iter22.ckpt | 3.4199173e7 | 1.670254506e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter23.ckpt | 5_members_ensemble2_iter23.ckpt | 3.4199173e7 | 1.670254544e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter24.ckpt | 5_members_ensemble2_iter24.ckpt | 3.4199173e7 | 1.670254581e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter25.ckpt | 5_members_ensemble2_iter25.ckpt | 3.4199173e7 | 1.670254618e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter26.ckpt | 5_members_ensemble2_iter26.ckpt | 3.4199173e7 | 1.670254654e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter27.ckpt | 5_members_ensemble2_iter27.ckpt | 3.4199173e7 | 1.670254694e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter28.ckpt | 5_members_ensemble2_iter28.ckpt | 3.4199173e7 | 1.670254731e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter29.ckpt | 5_members_ensemble2_iter29.ckpt | 3.4199173e7 | 1.670254768e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter3.ckpt | 5_members_ensemble2_iter3.ckpt | 3.4199173e7 | 1.670253806e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter30.ckpt | 5_members_ensemble2_iter30.ckpt | 3.4199173e7 | 1.670254806e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter31.ckpt | 5_members_ensemble2_iter31.ckpt | 3.4199173e7 | 1.670254843e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter32.ckpt | 5_members_ensemble2_iter32.ckpt | 3.4199173e7 | 1.670254881e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter33.ckpt | 5_members_ensemble2_iter33.ckpt | 3.4199173e7 | 1.67025492e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter34.ckpt | 5_members_ensemble2_iter34.ckpt | 3.4199173e7 | 1.670254956e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter35.ckpt | 5_members_ensemble2_iter35.ckpt | 3.4199173e7 | 1.670254993e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter36.ckpt | 5_members_ensemble2_iter36.ckpt | 3.4199173e7 | 1.670255031e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter37.ckpt | 5_members_ensemble2_iter37.ckpt | 3.4199173e7 | 1.670255068e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter38.ckpt | 5_members_ensemble2_iter38.ckpt | 3.4199173e7 | 1.670255108e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter39.ckpt | 5_members_ensemble2_iter39.ckpt | 3.4199173e7 | 1.670255146e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter4.ckpt | 5_members_ensemble2_iter4.ckpt | 3.4199173e7 | 1.670253843e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter40.ckpt | 5_members_ensemble2_iter40.ckpt | 3.4199173e7 | 1.670255185e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter41.ckpt | 5_members_ensemble2_iter41.ckpt | 3.4199173e7 | 1.670255224e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter42.ckpt | 5_members_ensemble2_iter42.ckpt | 3.4199173e7 | 1.670255262e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter43.ckpt | 5_members_ensemble2_iter43.ckpt | 3.4199173e7 | 1.670255299e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter44.ckpt | 5_members_ensemble2_iter44.ckpt | 3.4199173e7 | 1.670255337e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter45.ckpt | 5_members_ensemble2_iter45.ckpt | 3.4199173e7 | 1.670255375e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter46.ckpt | 5_members_ensemble2_iter46.ckpt | 3.4199173e7 | 1.670255412e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter47.ckpt | 5_members_ensemble2_iter47.ckpt | 3.4199173e7 | 1.670255449e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter48.ckpt | 5_members_ensemble2_iter48.ckpt | 3.4199173e7 | 1.670255486e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter49.ckpt | 5_members_ensemble2_iter49.ckpt | 3.4199173e7 | 1.670255523e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter5.ckpt | 5_members_ensemble2_iter5.ckpt | 3.4199173e7 | 1.67025388e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter50.ckpt | 5_members_ensemble2_iter50.ckpt | 3.4199173e7 | 1.670255561e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter51.ckpt | 5_members_ensemble2_iter51.ckpt | 3.4199173e7 | 1.670255599e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter52.ckpt | 5_members_ensemble2_iter52.ckpt | 3.4199173e7 | 1.670255635e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter53.ckpt | 5_members_ensemble2_iter53.ckpt | 3.4199173e7 | 1.670255671e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter54.ckpt | 5_members_ensemble2_iter54.ckpt | 3.4199173e7 | 1.670255709e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter55.ckpt | 5_members_ensemble2_iter55.ckpt | 3.4199173e7 | 1.670255746e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter56.ckpt | 5_members_ensemble2_iter56.ckpt | 3.4199173e7 | 1.670255789e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter57.ckpt | 5_members_ensemble2_iter57.ckpt | 3.4199173e7 | 1.670255826e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter58.ckpt | 5_members_ensemble2_iter58.ckpt | 3.4199173e7 | 1.670255862e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter59.ckpt | 5_members_ensemble2_iter59.ckpt | 3.4199173e7 | 1.670255899e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter6.ckpt | 5_members_ensemble2_iter6.ckpt | 3.4199173e7 | 1.670253917e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter60.ckpt | 5_members_ensemble2_iter60.ckpt | 3.4199173e7 | 1.670255935e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter61.ckpt | 5_members_ensemble2_iter61.ckpt | 3.4199173e7 | 1.670255972e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter62.ckpt | 5_members_ensemble2_iter62.ckpt | 3.4199173e7 | 1.670256009e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/5_members_ensemble2_iter63.ckpt | 5_members_ensemble2_iter63.ckpt | 3.4199173e7 | 1.670256046e12 |
#cd /dbfs/VideoPose3D/humaneva
#gdown 1F-sn2sXUE3V3a3bqHAei7p416YNd36o- # humaneva15_train.csv
#gdown 1F3qdcK4qqCtWHGBoiB-czvQkkVzvATVS # humaneva15_test.csv
from pyspark.sql.types import StructType, StringType, DoubleType, IntegerType, ArrayType
humaneva_train_path = "/VideoPose3D/humaneva/humaneva15_train.csv"
humaneva_test_path = "/VideoPose3D/humaneva/humaneva15_test.csv"
def load_data_from_csv(file_location):
"""Load and preprocess HumanEva data
Args:
file_location: file location from which to load the data
Returns:
"""
file_type = "csv"
infer_schema = "true"
first_row_is_header = False
delimiter = ","
schema = StructType() \
.add("Idx",IntegerType(),True) \
.add("Subject",StringType(),True) \
.add("Action",StringType(),True) \
.add("Camera",StringType(),True)
for i in range(15):
schema = schema.add(f"u{i}",DoubleType(),True).add(f"v{i}",DoubleType(),True)
for i in range(15):
schema = schema.add(f"X{i}",DoubleType(),True).add(f"Y{i}",DoubleType(),True).add(f"Z{i}",DoubleType(),True)
# Load the data from file
df = spark.read.csv(file_location, header=True, schema=schema, sep=',')
return df
df_train = load_data_from_csv(humaneva_train_path)
df_test = load_data_from_csv(humaneva_test_path)
from pyspark.sql import functions as F
@F.udf(StringType())
def first_word(s, delimeter=' '):
"""Take the first word ouf of the sentence string.
Args:
s: string
Returns:
first word in string
"""
return s.split(delimeter)[0]
display(df_train.withColumn("ActionType", first_word(df_train['Action'])))
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("ZGlzcGxheShkZl90cmFpbi53aXRoQ29sdW1uKCJBY3Rpb25UeXBlIiwgZmlyc3Rfd29yZChkZl90cmFpblsnQWN0aW9uJ10pKSk=").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksView904e424")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksView904e424")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksView904e424")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksView904e424) SELECT `Subject`,COUNT(`Idx`) `column_169e9e8c17`,`Camera` FROM q GROUP BY `Camera`,`Subject`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksView904e424")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
display(df_test.withColumn("ActionType", first_word(df_test['Action'])))
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("ZGlzcGxheShkZl90cmFpbi53aXRoQ29sdW1uKCJBY3Rpb25UeXBlIiwgZmlyc3Rfd29yZChkZl90cmFpblsnQWN0aW9uJ10pKSk=").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksView19dea14")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksView19dea14")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksView19dea14")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksView19dea14) SELECT `Subject`,COUNT(`Idx`) `column_169e9e8c26` FROM q GROUP BY `Subject`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksView19dea14")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("ZGlzcGxheShkZl90ZXN0LndpdGhDb2x1bW4oIkFjdGlvblR5cGUiLCBmaXJzdF93b3JkKGRmX3Rlc3RbJ0FjdGlvbiddKSkp").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksView8eab2c9")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksView8eab2c9")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksView8eab2c9")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksView8eab2c9) SELECT `Subject`,COUNT(`Idx`) `column_169e9e8c22`,`Camera` FROM q GROUP BY `Camera`,`Subject`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksView8eab2c9")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("ZGlzcGxheShkZl90cmFpbi53aXRoQ29sdW1uKCJBY3Rpb25UeXBlIiwgZmlyc3Rfd29yZChkZl90cmFpblsnQWN0aW9uJ10pKSk=").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksView8dc0c05")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksView8dc0c05")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksView8dc0c05")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksView8dc0c05) SELECT `ActionType`,COUNT(`Idx`) `column_169e9e8c35` FROM q GROUP BY `ActionType`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksView8dc0c05")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
def get_partitioned_rdd(input_rdd, partition_size=1000):
"""Partition RDD
Args:
input_rdd: RDD to be partitioned
Returns:
Partitioned RDD
"""
return input_rdd.mapPartitions(lambda partition: partition_all(partition_size, partition))
rdd_train = get_partitioned_rdd(df_train.rdd)
rdd_test = get_partitioned_rdd(df_test.rdd)
from random import sample, seed
seed(1)
actions = [x['Action'] for x in df_train.select("Action").distinct().collect()]
num_unlabeled = 20
unlabeled_actions = sample(actions, num_unlabeled)
df_train_unlabeled = df_train.filter(df_train.Action.isin(unlabeled_actions))
df_train_labeled = df_train.filter(~df_train.Action.isin(unlabeled_actions))
display(df_train_unlabeled.withColumn("ActionType", first_word(df_train_unlabeled['Action'])))
@F.udf(StringType())
def first_word(s, delimeter=' '):
"""Take the first word ouf of the sentence string.
Args:
s: string
Returns:
first word in string
"""
return s.split(delimeter)[0]
display(df_train_labeled.withColumn("ActionType", first_word(df_train_labeled['Action'])))
data_cols = []
for i in range(15):
data_cols.append(f"u{i}")
data_cols.append(f"v{i}")
for i in range(15):
data_cols.append(f"X{i}")
data_cols.append(f"Y{i}")
data_cols.append(f"Z{i}")
df_train_grouped = df_train \
.withColumn("Data", F.concat_ws(', ', *data_cols)) \
.withColumn("Group", F.concat_ws(', ', "Subject", "Action", "Camera")) \
.select("Group", "Data")
from random import randint
count = 90
receptive_field = 27
randint(0, count - receptive_field + 1)
display(df_train_grouped)
from random import randint
from pyspark.sql.types import StructType, StringType, DoubleType, IntegerType, ArrayType
receptive_field = 27
# @F.udf(ArrayType(StringType()))
@F.udf()
# @F.udf(StringType())
def aggregate_fn(x):
count = len(x)
i = randint(0, count - receptive_field + 1) if len(x) >= receptive_field else -1
return i #[i:i+receptive_field] if i > 0 else x
display(df_train_grouped \
.groupBy("Group") \
# .agg(F.collect_list(F.col("Data"))) \
# .agg(F.count('Data').alias('Count')) \
# .filter(f'Count >= {receptive_field}')
.agg(aggregate_fn(F.collect_list(F.col("Data"))).alias("Result")) \
.filter(f'Result >= 0')
.orderBy('Result', ascending=True) \
.head(20)
)
alias
@F.udf(IntegerType())
def aggregate_fn(x):
output_count = [0, []]
for i in x:
output_count[0] += 1
output_count[1].append(x)
return output_count%python
@F.udf(IntegerType())
def aggregate_fn(x):
output_count = [0, []]
for i in x:
output_count[0] += 1
output_count[1].append(x)
return output_count
# display(df_train.groupBy('Subject','Action','Camera').count().collect())
# display(df_train.groupBy('Subject','Action','Camera').agg(aggregate_fn(F.collect_list('u0')).alias('count')).collect())
# a.groupBy('id').agg(find_a_udf(F.collect_list(F.when(F.col('value1') == 1, F.col('value2')))).alias('a_count')).show()
display(df_train.groupBy('Subject','Action','Camera').agg(*[F.collect_list(F.col(col))
for col in data_cols]))
def train_hvd():
# Initialize Horovod
hvd.init()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type == 'cuda':
torch.cuda.set_device(hvd.local_rank())
# This is just to confirm we really want to (re-) train the model
# Otherwise this cell will be skipped
cameras_train, poses_train, poses_train_2d = fetch(subjects_train, action_filter, subset=args.subset)
train_dataset = DistributedTrainingDataset(cameras_train,
poses_train,
poses_train_2d,
args.stride,
pad=pad,
causal_shift=causal_shift,
shuffle=True,
augment=args.data_augmentation,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
train_sampler = DistributedSampler(train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size//args.stride, sampler=train_sampler)
train_eval_dataset = DistributedEvaluationDataset(cameras_train,
poses_train,
poses_train_2d,
pad=pad,
causal_shift=causal_shift,
augment=False)
## we did not use train_eval_loader##
train_eval_sampler = DistributedSampler(train_eval_dataset, num_replicas=hvd.size(), rank=hvd.rank())
train_eval_loader = torch.utils.data.DataLoader(train_eval_dataset, batch_size=1, sampler=train_eval_sampler)
#print('INFO: Training on {} frames'.format(train_eval_dataset.num_frames()))
# Model
model = model_pos_train.to(device)
lr = args.learning_rate
optimizer = optim.Adam(model.parameters(), lr=lr*hvd.size(), amsgrad=True)
# Wrap the local optimizer with hvd.DistributedOptimizer so that Horovod handles the distributed optimization
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
# Broadcast initial parameters so all workers start with the same parameters
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
lr_decay = args.lr_decay
#losses_3d_train = []
#losses_3d_train_eval = []
#losses_3d_valid = []
#epoch = 0
initial_momentum = 0.1
final_momentum = 0.001
#Pos model only
for epoch in range(1,args.epochs+1):
train_one_epoch(model, device, train_loader, optimizer, epoch)
if hvd.rank() == 0:
save_checkpoint(model,optimizer,epoch)
# Decay learning rate exponentially
lr *= lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] *= lr_decay
epoch += 1
# Decay BatchNorm momentum
momentum = initial_momentum * np.exp(-epoch/args.epochs * np.log(initial_momentum/final_momentum))
model.set_bn_momentum(momentum)
def train():
# Initialize Horovod
#hvd.init()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#if device.type == 'cuda':
# torch.cuda.set_device(hvd.local_rank())
# This is just to confirm we really want to (re-) train the model
# Otherwise this cell will be skipped
cameras_train, poses_train, poses_train_2d = fetch(subjects_train, action_filter, subset=args.subset)
train_dataset = DistributedTrainingDataset(cameras_train,
poses_train,
poses_train_2d,
args.stride,
pad=pad,
causal_shift=causal_shift,
shuffle=True,
augment=args.data_augmentation,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
train_sampler = torch.utils.data.RandomSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size//args.stride, sampler=train_sampler)
train_eval_dataset = DistributedEvaluationDataset(cameras_train,
poses_train,
poses_train_2d,
pad=pad,
causal_shift=causal_shift,
augment=False)
train_eval_sampler = torch.utils.data.RandomSampler(train_eval_dataset)
train_eval_loader = torch.utils.data.DataLoader(train_eval_dataset, batch_size=1, sampler=train_eval_sampler)
#print('INFO: Training on {} frames'.format(train_eval_dataset.num_frames()))
# Model
model = model_pos_train.to(device)
lr = args.learning_rate
optimizer = optim.Adam(model.parameters(), lr=lr, amsgrad=True)
# Wrap the local optimizer with hvd.DistributedOptimizer so that Horovod handles the distributed optimization
#optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
# Broadcast initial parameters so all workers start with the same parameters
#hvd.broadcast_parameters(model.state_dict(), root_rank=0)
lr_decay = args.lr_decay
#losses_3d_train = []
#losses_3d_train_eval = []
#losses_3d_valid = []
#epoch = 0
initial_momentum = 0.1
final_momentum = 0.001
#Pos model only
for epoch in range(1,args.epochs+1):
train_one_epoch(model, device, train_loader, optimizer, epoch)
#if hvd.rank() == 0:
# save_checkpoint(model,optimzer,epoch)
# Decay learning rate exponentially
lr *= lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] *= lr_decay
epoch += 1
# Decay BatchNorm momentum
momentum = initial_momentum * np.exp(-epoch/args.epochs * np.log(initial_momentum/final_momentum))
model.set_bn_momentum(momentum)
# Non-distributed training
train()
#Distributed training using Horovod runner
if not args.evaluate:
hr = HorovodRunner(np=2)
hr.run(train_hvd)
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("ZGlzcGxheShkZl90ZXN0LndpdGhDb2x1bW4oIkFjdGlvblR5cGUiLCBmaXJzdF93b3JkKGRmX3Rlc3RbJ0FjdGlvbiddKSkp").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksViewc8fd8bd")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksViewc8fd8bd")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksViewc8fd8bd")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksViewc8fd8bd) SELECT `Subject`,COUNT(`Idx`) `column_169e9e8c29` FROM q GROUP BY `Subject`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksViewc8fd8bd")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("ZGlzcGxheShkZl90cmFpbl91bmxhYmVsZWQud2l0aENvbHVtbigiQWN0aW9uVHlwZSIsIGZpcnN0X3dvcmQoZGZfdHJhaW5fdW5sYWJlbGVkWydBY3Rpb24nXSkpKQ==").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksView52d4871")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksView52d4871")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksView52d4871")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksView52d4871) SELECT `ActionType`,COUNT(`Idx`) `column_169e9e8c44` FROM q GROUP BY `ActionType`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksView52d4871")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("ZGlzcGxheShkZl90cmFpbl9sYWJlbGVkLndpdGhDb2x1bW4oIkFjdGlvblR5cGUiLCBmaXJzdF93b3JkKGRmX3RyYWluX2xhYmVsZWRbJ0FjdGlvbiddKSkp").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksView444bd85")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksView444bd85")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksView444bd85")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksView444bd85) SELECT `ActionType`,COUNT(`Idx`) `column_169e9e8c47` FROM q GROUP BY `ActionType`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksView444bd85")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("CkBGLnVkZihJbnRlZ2VyVHlwZSgpKQpkZWYgYWdncmVnYXRlX2ZuKHgpOgogICAgcmV0dXJuIGxlbih4KQoKZGlzcGxheShkZl90cmFpbl9ncm91cGVkIFwKICAgICAgICAgICAgLmdyb3VwQnkoIkdyb3VwIikgXAogICAgICAgICAgICAuYWdnKGFnZ3JlZ2F0ZV9mbihGLmNvbGxlY3RfbGlzdChGLmNvbCgiRGF0YSIpKSkuYWxpYXMoIlJlc3VsdCIpKSBcCiAgICAgICAgICAgIC5vcmRlckJ5KCdSZXN1bHQnKSBcCiMgICAgICAgICAgICAgLmhlYWQoMTUpCiAgICAgICAp").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksViewab46dae")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksViewab46dae")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksViewab46dae")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksViewab46dae) SELECT `Group`,SUM(`Result`) `column_ab54def716` FROM q GROUP BY `Group`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksViewab46dae")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
__backend_agg_display_orig = display
__backend_agg_dfs = []
def __backend_agg_display_new(df):
__backend_agg_df_modules = ["pandas.core.frame", "databricks.koalas.frame", "pyspark.sql.dataframe", "pyspark.pandas.frame"]
if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):
__backend_agg_dfs.append(df)
display = __backend_agg_display_new
def __backend_agg_user_code_fn():
import base64
exec(base64.standard_b64decode("ZGlzcGxheShkZl90ZXN0LndpdGhDb2x1bW4oIkFjdGlvblR5cGUiLCBmaXJzdF93b3JkKGRmX3Rlc3RbJ0FjdGlvbiddKSkp").decode())
try:
# run user code
__backend_agg_user_code_fn()
#reset display function
display = __backend_agg_display_orig
if len(__backend_agg_dfs) > 0:
# create a temp view
if hasattr(__backend_agg_dfs[0], "to_spark"):
# koalas dataframe
__backend_agg_dfs[0].to_spark().createOrReplaceTempView("DatabricksView9bddd67")
elif type(__backend_agg_dfs[0]).__module__ == "pandas.core.frame" or isinstance(__backend_agg_dfs[0], list):
# pandas dataframe
spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView("DatabricksView9bddd67")
else:
__backend_agg_dfs[0].createOrReplaceTempView("DatabricksView9bddd67")
#run backend agg
display(spark.sql("""WITH q AS (select * from DatabricksView9bddd67) SELECT `ActionType`,COUNT(`Idx`) `column_169e9e8c38` FROM q GROUP BY `ActionType`"""))
else:
displayHTML("dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.")
finally:
spark.sql("drop view if exists DatabricksView9bddd67")
display = __backend_agg_display_orig
del __backend_agg_display_new
del __backend_agg_display_orig
del __backend_agg_dfs
del __backend_agg_user_code_fn
Distributed ensembles of deep neural networks Here we provide the necessary code for creating and training a distributed ensemble of deep neural networks
Assumptions
- The driver node fits a small subset of the data, but otherwise does not the data not fit on one node
- The model parameters fit in the driver node, and at least one set of model parameters fits on a worker node.
- Test set fits on driver node
TODO
- How to aggregate regressed values for each keypoint:
- Average results? Smoothing effect
- Robust mean + discard if the predictions disagree
- Change the model to predict sigma. Use sigma for the weighted mean
TODO
- Remove Camera, subject, action after grouping?
- Can we do the random split of train, into labeled and unlabeled, without collecting. What if the train data is too big to collect, but yes we only collect the group names? Set a train key (labeled/unlabeled) and map by key?
- Do we need to collect test data? Is it possible to just collect test losses? Hecne, doing the test predictions on the workers with the trained models already there.
import numpy as np
import torch
import pyspark.sql.functions as F
from pyspark.sql import Window
from pyspark.sql.functions import collect_list, size, udf
from pyspark.sql.types import BooleanType
from pyspark.sql.functions import udf
from itertools import groupby
#from pyspark.ml import Pipeline
from pyspark.rdd import PipelinedRDD
from pathlib import Path
import os
import matplotlib.pyplot as plt
ls /dbfs/VideoPose3D/humaneva/
from pyspark.sql.types import StructType, StringType, DoubleType, IntegerType
humaneva_train_path = "/VideoPose3D/humaneva/humaneva15_train.csv"
humaneva_test_path = "/VideoPose3D/humaneva/humaneva15_test.csv"
def load_data_from_csv(file_location):
"""Load and preprocess HumanEva data
Args:
file_location: file location from which to load the data
Returns:
df: spark DataFrame
"""
file_type = "csv"
infer_schema = "true"
first_row_is_header = False
delimiter = ","
schema = StructType() \
.add("Idx",IntegerType(),True) \
.add("Subject",StringType(),True) \
.add("Action",StringType(),True) \
.add("Camera",StringType(),True)
for i in range(15):
schema = schema.add(f"u{i}",DoubleType(),True).add(f"v{i}",DoubleType(),True)
for i in range(15):
schema = schema.add(f"X{i}",DoubleType(),True).add(f"Y{i}",DoubleType(),True).add(f"Z{i}",DoubleType(),True)
# Load the data from file
df = spark.read.csv(file_location, header=True, schema=schema, sep=',')
return df
df_train = load_data_from_csv(humaneva_train_path)
df_test = load_data_from_csv(humaneva_test_path)
from pyspark.sql import functions as F
df_train = df_train.withColumn("Group", F.concat_ws(', ', "Subject", "Action", "Camera")).drop("Subject", "Action", "Camera")
df_test = df_test.withColumn("Group", F.concat_ws(', ', "Subject", "Action", "Camera")).drop("Subject", "Action", "Camera")
display(df_train)
from pyspark.ml.feature import VectorAssembler
feature_names = []
target_names = []
n_keypoints = 15
for i in range(n_keypoints):
feature_names.append("u{}".format(i))
feature_names.append("v{}".format(i))
target_names.append("X{}".format(i))
target_names.append("Y{}".format(i))
target_names.append("Z{}".format(i))
feature_assembler = VectorAssembler(inputCols=feature_names, outputCol="features")
target_assembler = VectorAssembler(inputCols=target_names, outputCol="targets")
def assemble_vectors(df):
df = feature_assembler.transform(df)
df = target_assembler.transform(df)
df = df.drop(*feature_names).drop(*target_names)
return df
df_train = assemble_vectors(df_train)
df_test = assemble_vectors(df_test)
receptive_field = 27
w = Window.orderBy("Idx").partitionBy(["Group"]).rowsBetween(Window.currentRow-receptive_field//2, Window.currentRow+receptive_field//2)
def create_receptive_fields(df):
df = df.withColumn("feature_sequence", collect_list("features").over(w))
df = df.withColumn("group_sequence", collect_list("Group").over(w))
df = df.filter(size(df.group_sequence) == receptive_field)
df = df.drop("features")
return df
df_train_receptive = create_receptive_fields(df_train)
df_test_receptive = create_receptive_fields(df_test)
display(df_train_receptive)
Split training set into labeled and unlabeled based on chunks
Since we are exploring in semi-supervised learning, we will have both labeled and unlabeled training data. Therefore, here we randomly split the data, with respect to the group, into an unlabeled and labeled set. To have a realistic semi-supervised setting, we assume that the unlabeled training data is slighly larger than the labeled training data. The targets are droped for the unlabeled training set.
from random import sample, seed
## find right random seed to compensate for the different size of each chunk
seed(0) # seed 0 gives ok split
chunks = df_train_receptive.select("Group").distinct().collect()
chunks = [x["Group"] for x in chunks]
num_chunks = len(chunks)
num_unlabeled = int(num_chunks*0.6)
unlabeled_chunks = sample(chunks, num_unlabeled)
labeled_chunks = [x for x in chunks if x not in unlabeled_chunks]
df_train_receptive_unlabeled = df_train_receptive.filter(df_train_receptive.Group.isin(unlabeled_chunks))
df_train_receptive_unlabeled = df_train_receptive_unlabeled.drop("targets")
df_train_receptive_labeled = df_train_receptive.filter(~df_train_receptive.Group.isin(unlabeled_chunks))
def all_equal(iterable):
g = groupby(iterable)
return next(g, True) and not next(g, False)
udf_all_equal = udf(all_equal, BooleanType())
Define model
Here we define the 3D pose estimation model with temporal convolutions and corresponding hyperparameters. Each ensemble will use this model to train on labeled data (including pseudolabels) and make predictions on unlabeled data.
from torch import nn
class Args:
# Data arguments
num_joints = 15
# Model arguments
stride = 1 # chunk size to use during training
epochs = 10 # 100 # number of training epochs
batch_size = 128 # batch size in terms of predicted frames
dropout = 0.25 # dropout probability
learning_rate = 0.001 # initial learning rate
lr_decay = 0.996 # learning rate decay per epoch
data_augmentation = True # disable train-time flipping
test_time_augmentation = True # disable test-time flipping
architecture = '3,3,3' # filter widths separated by comma
channels = 1024 # number of channels in convolution layers
args = Args()
filter_widths = [int(x) for x in args.architecture.split(',')]
receptive_field = np.prod(filter_widths) # model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
hyperparams = [args.num_joints, 2, args.num_joints, filter_widths, args.dropout, args.channels]
class TemporalModelBase(nn.Module):
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, dropout, channels):
super().__init__()
# Validate input
for fw in filter_widths:
assert fw % 2 != 0, 'Only odd filter widths are supported'
self.num_joints_in = num_joints_in
self.in_features = in_features
self.num_joints_out = num_joints_out
self.filter_widths = filter_widths
self.drop = nn.Dropout(dropout)
self.relu = nn.ReLU(inplace=True)
self.pad = [ filter_widths[0] // 2 ]
self.expand_bn = nn.BatchNorm1d(channels, momentum=0.1)
self.shrink = nn.Conv1d(channels, num_joints_out*3, 1)
def set_bn_momentum(self, momentum):
self.expand_bn.momentum = momentum
for bn in self.layers_bn:
bn.momentum = momentum
def forward(self, pos2D):
assert len(pos2D.shape) == 4 # pos2D: B x 27 x 15 x 2
assert pos2D.shape[-2] == self.num_joints_in # 15
assert pos2D.shape[-1] == self.in_features # 2
sz = pos2D.shape[:3] # B x 27 x 15
pos2D = pos2D.view(pos2D.shape[0], pos2D.shape[1], -1) # B x 27 x 15 * 2
pos2D = pos2D.permute(0, 2, 1) # B x 15 * 2 x 27
pos3D = self._forward_blocks(pos2D)
pos3D = pos3D.permute(0, 2, 1)
pos3D = pos3D.view(sz[0], -1, self.num_joints_out, 3)
return pos3D
class TemporalModel(TemporalModelBase):
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, dropout=0.25, channels=1024):
"""
Reference 3D pose estimation model with temporal convolutions.Initialize this model.
Arg:
num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
in_features -- number of input features for each joint (typically 2 for 2D input)
num_joints_out -- number of output joints (can be different than input)
filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
dropout -- dropout probability
channels -- number of convolution channels
"""
super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, dropout, channels)
self.expand_conv = nn.Conv1d(num_joints_in*in_features, channels, filter_widths[0], bias=False)
layers_conv = []
layers_bn = []
next_dilation = filter_widths[0] # 3
for i in range(1, len(filter_widths)):
self.pad.append((filter_widths[i] - 1)*next_dilation // 2) # [1, 3, 9]
layers_conv.append(nn.Conv1d(channels, channels,
filter_widths[i],
dilation=next_dilation,
bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
next_dilation *= filter_widths[i] # 3, 9, 27
self.layers_conv = nn.ModuleList(layers_conv)
self.layers_bn = nn.ModuleList(layers_bn)
def _forward_blocks(self, pos2D):
# pos2D: B x 15 * 2 x 27
x = self.drop(self.relu(self.expand_bn(self.expand_conv(pos2D)))) # B x 1024 x 25
for i in range(len(self.pad) - 1):
pad = self.pad[i+1] # 3, 9
res = x[:, :, pad : x.shape[2] - pad] # B x 1024 x 19, B x 1024 x 1
x = self.drop(self.relu(self.layers_bn[2*i](self.layers_conv[2*i](x)))) # B x 1024 x 19, B x 1024 x 1
x = res + self.drop(self.relu(self.layers_bn[2*i + 1](self.layers_conv[2*i + 1](x))))
pos3D = self.shrink(x) # B x 15*3 x 1
return pos3D
@staticmethod
def from_state_dict(params, hyperparams):
net = TemporalModel(*hyperparams)
net.load_state_dict(params)
return net
Loss
Here we define the loss used for training and evaluation.
def mpjpe(predicted, target):
"""
Mean per-joint position error (i.e. mean Euclidean distance),
often referred to as "Protocol #1" in many papers.
"""
assert predicted.shape == target.shape
return torch.mean(torch.norm(predicted - target, dim=len(target.shape)-1))
"""
class DataSet(torch.utils.data.Dataset):
def __init__(self, pos2D, pos3D, receptive_field):
self.pos2D = pos2D # self.pos2D: [N_1 x 15 x 2, ..., N_B x 15 x 2]
self.pos3D = pos3D # self.pos3D: [N_1 x 15 x 3, ..., N_B x 15 x 3]
self.receptive_field = receptive_field
def __len__(self):
return self.x.shape[0]
def __getitem__(self, ind):
pos2D = self.pos2D[ind] # pos2D: N x 15 x 2
pos3D = self.pos3D[ind] # pos3D: N x 15 x 3
i = torch.randint(pos_3d.shape[0] - self.receptive_field + 1, [1])
pos2D_sample = pos2D[i:i+self.receptive_field] # pos2D_sample: 27 x 15 x 2
pos3D_sample = pos3D[i+(self.receptive_field - 1) // 2 ][None] # pos3D_sample: 1 x 15 x 3
return pos2D_sample, pos3D_sample
"""
class DataSet(torch.utils.data.Dataset):
def __init__(self, pos2D, pos3D):
self.pos2D = pos2D # self.pos2D: B x 27 x 15 * 2
self.pos3D = pos3D # self.pos3D: B x 1 x 15 * 3
def __len__(self):
return self.pos2D.shape[0]
def __getitem__(self, ind):
pos2D = self.pos2D[ind] # pos2D: B x 27 x 15 * 2 -> 27 x 15 * 2
pos3D = self.pos3D[ind] # pos2D: B x 1 x 15 * 3 -> 1 x 15 * 2
return pos2D, pos3D
def train(params, hyperparams, data, args):
x,y = zip(*data)
pos2D, pos3D = torch.stack(x), torch.stack(y)
model = TemporalModel.from_state_dict(params, hyperparams)
model.train()
lr = args.learning_rate
lr_decay = args.lr_decay
train_data = DataSet(pos2D, pos3D)
dataloader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True)
opt = torch.optim.Adam(model.parameters(), lr=lr, amsgrad=True)
initial_momentum = 0.1
final_momentum = 0.001
losses_3d_train = []
for epoch in range(args.epochs):
epoch_loss_3d_train = 0
N = 0
for batch in dataloader:
inputs_2d, inputs_3d = batch
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
model = model.cuda()
inputs_3d[:, :, 0] = 0
# Predict 3D poses
predicted_3d_pos = model(inputs_2d)
# Calcuclate MPJPE loss
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_train += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
loss_total = loss_3d_pos
opt.zero_grad()
loss_total.backward()
# Make one optimization step on batch
opt.step()
losses_3d_train.append(epoch_loss_3d_train / N)
print('[%d] lr %f 3d_train %f' % (
epoch + 1,
lr,
losses_3d_train[-1] * 1000))
# Decay learning rate exponentially
lr *= lr_decay
for param_group in opt.param_groups:
param_group['lr'] *= lr_decay
err = mpjpe(model(pos2D.cuda()), pos3D.cuda())
lossval = float(err.detach().cpu().numpy())
return model.state_dict(), lossval
def predict(params, hyperparams, x):
model = TemporalModel.from_state_dict(params, hyperparams)
model.eval()
if torch.cuda.is_available():
x = x.cuda()
model.cuda()
return model(x).detach().cpu()
"""
# Broadcast hyperparams of models
hyperparams_rdd = sc.broadcast(hyperparams)
model_params = []
for i in range(n_models):
model = TemporalModel(*hyperparams)
model_params.append(model.state_dict())
model_params_rdd = sc.parallelize(model_params)
def train_distributed(model_params, hyperparams):
pass
def train_ensemble_distributed(model_params, data, hyperparams):
pass
for m in len(model_params.count()):
data.mapParitions(lambda k: train_distributed(k, model_params, hyperparams))
"""
def train_ensemble(n_models, model_params, data, hyperparams):
model_data = []
args = Args()
assert model_params.count() == n_models
assert len(data) == n_models, f"Lenght mismatch, lenght of data is {len(data)}, while number of models are {n_models}"
models_trained = model_params.map(lambda t: train(*(t,hyperparams,data, args)))
models_params = models_trained.map(lambda t: t._1)
train_losses = models_trained.map(lambda t: t._2)
print(f"Training losses: {[x[1] for x in models_trained]}")
return models_params, train_losses.collect()
def ensemble_predictions(models, hyperparams, test_x):
pred_iter = _pred_models_iter(models, hyperparams, test_x)
return pred_iter.map(lambda t: predict(*t))
def ensemble_predictions_reduced(models, hyperparams, test_x, reduce_fn):
return ensemble_predictions(models, hyperparams, test_x).reduce(reduce_fn)
def _pred_models_iter(models, hyperparams, test_x):
if isinstance(models, PipelinedRDD):
return models.map(lambda model: (model, test_x))
elif isinstance(models, list): # our case
models_and_data = [(params, hyperparams, test_x) for params in models]
return sc.parallelize(models_and_data)
else:
raise TypeError("'models' must be an RDD or a list")
def evaluate_avg_on_set(models, hyperparams, dataset, n_models):
predictions_sum = ensemble_predictions_reduced(models, hyperparams, dataset, lambda x, y: x + y) # Tensor output
predictions_avg = predictions_sum/n_models
return predictions_avg
display(df_train_receptive_labeled)
### We do not have targets for unlabelled dataset
def toTensorLabeled(x):
fs = x["feature_sequence"]
target = x["targets"]
feature_tensor = []
for f in fs:
feature_tensor.append(f)
xx = torch.tensor(feature_tensor,dtype=torch.float)
yy = torch.tensor(target,dtype=torch.float)
return xx.view(27, 15, 2), yy.view(1, 15, 3)
def toTensorUnlabeled(x):
fs = x["feature_sequence"]
feature_tensor = []
for f in fs:
feature_tensor.append(f)
xx = torch.tensor(feature_tensor, dtype=torch.float)
return xx.view(27, 15, 2)
labeled_tensor = df_train_receptive_labeled.withColumn("feature_sequence", )
#unlabeled_tensor_rdd = df_train_receptive_unlabeled.rdd.map(toTensorUnlabeled)
#test_tensor_rdd = df_test_receptive.rdd.map(toTensorLabeled)
print(labeled_tensor_rdd.getNumPartitions())
print(unlabeled_tensor_rdd.getNumPartitions())
print(test_tensor_rdd.getNumPartitions())
def save_models(models_state_dict,save_models_dir: Path ,iter: int) -> None:
"""
Save models after training of iteration
Args:
models: list of state dicts of pytorch nn.Module models to be saved
save_models_dir: Path to dir where models are being saved
iter: iteration
"""
# Create saving path if it does not exist
save_models_dir.mkdir(parents=True, exist_ok=True)
for i_model, model in enumerate(models):
torch.save(model, os.path.join(save_models_dir,f"ensemble{i_model}_iter{iter}.ckpt"))
Training loop (for semi-supervised learning)
The hypothesis of training ensemble models in a distributed way is that we coud obtain better label estimation.Sepcifically, the prediction of the test sample is obtained by avergaing the prediciton from each memeber. Therefore, we incoporate th
n_models = 2
subset_size = 1000
total_size = n_models * subset_size
def get_labeled_subset():
# Data is loaded into driver's memory
data = labeled_tensor_rdd.takeSample(False, total_size)
x, y = zip(*data)
return torch.stack(x), torch.stack(y)
def get_unlabeled_subset():
# Data is loaded into driver's memory
data = unlabeled_tensor_rdd.takeSample(False, total_size)
return torch.stack(data)
def split_for_ensemble(x,y):
'''
Splits data so that each member acesses unique data for training
'''
full_size = x.shape[0]
split_size = full_size//n_models +1
x = torch.split(x, split_size)
y = torch.split(y, split_size)
return list(zip(x, y))
# collect test data
data_test = test_tensor_rdd.collect()
x_test, y_test = zip(*data_test)
x_test, y_test = torch.stack(x_test).detach(), torch.stack(y_test).detach()
iterations = 10 # 10
models = []
# initiate models
for i in range(n_models):
model = TemporalModel(*hyperparams)
models.append(model.state_dict())
# Sample a small subset of the labeled data.
# All data is loaded into driver's memory.
x_l, y_l = get_labeled_subset()
print(f"Training distributed ensemble of {len(models)} models")
# train using only labeled data
models = train_ensemble(n_models,
models,
split_for_ensemble(x_l, y_l),
hyperparams)
# evaluate on test set
with torch.no_grad():
test_preds = evaluate_avg_on_set(models, hyperparams, x_test, n_models)
test_mpjpe = mpjpe(test_preds, y_test)
print(f"MPJPE for test set: {test_mpjpe}")
print("Labeled training iteration finished")
test_mpjpes = []
#train_mpjpes_iteration = []
# train using labeled and unlabeled data
for i in range(iterations):
# evaluate on test set
test_preds = evaluate_avg_on_set(models, hyperparams, x_test, n_models)
test_mpjpe = mpjpe(test_preds, y_test)
test_mpjpes.append(test_mpjpe)
x_ul = get_unlabeled_subset()
# predict unlabeled data
unlabeled_preds = evaluate_avg_on_set(models,
hyperparams,
x_ul,
n_models)
# Random pick a subset of trainning data
x_l, y_l = get_labeled_subset()
# concat labeled and unlabeled data
x_cc = torch.concat([x_l, x_ul])
y_cc = torch.concat([y_l, unlabeled_preds])
# mix labeled and unlabeled data by shuffling
idx = torch.randperm(x_cc.shape[0])
x_cc, y_cc = x_cc[idx], y_cc[idx]
print("Running semi-supervised training iteration: {}".format(i+1))
# train using mix of labeled and pseudolabeled data
models = train_ensemble(n_models,
models,
split_for_ensemble(x_cc, y_cc),
hyperparams)
#train_mpjpes_iteration.append(train_mjpes)
saved_models_dir = Path("saved_models/humaneva/checkpoints/semi-supervised")
save_models(models, saved_models_dir,i)
# evaluate on test set
with torch.no_grad():
test_preds = evaluate_avg_on_set(models, hyperparams, x_test, n_models)
test_mpjpe = mpjpe(test_preds, y_test)
test_mpjpes.append(test_mpjpe)
print(f"MPJPE for test set: {test_mpjpe}")
%matplotlib inline
fig = plt.figure()
plt.plot(test_mpjpe)
plt.show()
%matplotlib inline
fig = plt.figure()
for i_model, mpjpes in enumerate(zip(*train_mpjpes_list))
plt.plot(mpjpes, label=f"Ensemble {i_model}")
plt.legend()
plt.show()
# evaluate on test set
test_mpjpes=[]
with torch.no_grad():
test_preds = evaluate_avg_on_set(models, hyperparams, x_test, n_models)
test_mpjpe = mpjpe(test_preds, y_test)
test_mpjpes.append(test_mpjpe)
print("MPJPE for test set:")
print(test_mpjpes)
Training loop (for supervised baseline)
We first establish the baseline, where the ensemble model is trained in a distrbuted way. Specifcially, each member of the emsemble model is trained in different work node in parallel. Besides, each member is limited to access a subset of the trainining data stored in the driver node. It is a natural idea to send the same fraction of training data to the work node. However, to avoid the scenario that the work node might no have enough space to store the subset of traning data, we set the threshold for the maximum size of the data to be stored in the work node. Pratically, the size of the subset of training data is fixed to be N=1000.
import torch
# optional to do data partition
def get_partitioned_rdd(input_rdd, partition_size=1000):
"""Partition RDD
Args:
input_rdd: RDD to be partitioned
Returns:
Partitioned RDD
"""
return input_rdd.mapPartitions(lambda partition: partition_all(partition_size, partition))
n_models = 10
subset_size = 1000
n_iterations = 1000
total_size = n_models * subset_size
models_supervised = []
# initiate models
for i in range(n_models):
model = TemporalModel(*hyperparams)
models_supervised.append(model.state_dict())
test_mpjpes_supervised = []
train_mpjpes_iteration_supervised
# train using only labeled data
for iteration in range(n_iterations):
x_l, y_l = get_labeled_subset()
models_supervised, train_mjpes_supervised = train_ensemble(n_models, models_supervised, split_for_ensemble(x_l, y_l), hyperparams, n_epochs)
train_mpjpes_iteration_supervised.append(train_mjpes_supervised)
saved_models_dir = Path("saved_models/humaneva/checkpoints/supervised")
save_models(models_supervised, saved_models_dir,epoch)
# Ealuate on test set
with torch.no_grad():
test_preds_supervised = evaluate_avg_on_set(models_supervised, hyperparams, x_test, n_models)
test_mpjpe_supervised = mpjpe(test_preds_supervised, y_test)
test_mpjpes_supervised.append(test_mpjpe_supervised)
print("MPJPE for test set (supervised baseline):")
print(test_mpjpes_supervised)
%matplotlib inline
fig = plt.figure()
plt.plot(test_mpjpe_supervised)
plt.show()
%matplotlib inline
fig = plt.figure()
for i_model, mpjpes in enumerate(zip(*train_mpjpes_list_supervised))
plt.plot(mpjpes, label=f"Ensemble {i_model}")
plt.legend()
plt.show()
ls /dbfs/VideoPose3D
ls .
| path | name | size | modificationTime |
|---|---|---|---|
| dbfs:/.../ | .../ | 0.0 | 1.670236037456e12 |
| dbfs:/.databrickscfg | .databrickscfg | 121.0 | 1.668177781e12 |
| dbfs:/<checkpoint-path>/ | <checkpoint-path>/ | 0.0 | 1.670236037456e12 |
| dbfs:/FileStore/ | FileStore/ | 0.0 | 1.670236037456e12 |
| dbfs:/Fin/ | Fin/ | 0.0 | 1.670236037456e12 |
| dbfs:/LT/ | LT/ | 0.0 | 1.670236037456e12 |
| dbfs:/Users/ | Users/ | 0.0 | 1.670236037456e12 |
| dbfs:/VideoPose3D/ | VideoPose3D/ | 0.0 | 1.670236037456e12 |
| dbfs:/WikipediaData/ | WikipediaData/ | 0.0 | 1.670236037456e12 |
| dbfs:/_checkpoint/ | _checkpoint/ | 0.0 | 1.670236037456e12 |
| dbfs:/_checkpoint1/ | _checkpoint1/ | 0.0 | 1.670236037456e12 |
| dbfs:/_checkpoint_lt/ | _checkpoint_lt/ | 0.0 | 1.670236037456e12 |
| dbfs:/_checkpoint_uppsala/ | _checkpoint_uppsala/ | 0.0 | 1.670236037456e12 |
| dbfs:/arcGISRuntime/ | arcGISRuntime/ | 0.0 | 1.670236037456e12 |
| dbfs:/data/ | data/ | 0.0 | 1.670236037456e12 |
| dbfs:/databricks/ | databricks/ | 0.0 | 1.670236037456e12 |
| dbfs:/databricks-datasets/ | databricks-datasets/ | 0.0 | 0.0 |
| dbfs:/databricks-results/ | databricks-results/ | 0.0 | 0.0 |
| dbfs:/datasets/ | datasets/ | 0.0 | 1.670236037456e12 |
| dbfs:/dbfs/ | dbfs/ | 0.0 | 1.670236037456e12 |
| dbfs:/drl_logs/ | drl_logs/ | 0.0 | 1.670236037456e12 |
| dbfs:/enwiki-latest-category.sql | enwiki-latest-category.sql | 1.12713538e8 | 1.66818283e12 |
| dbfs:/enwiki-latest-categorylinks.sql | enwiki-latest-categorylinks.sql | 2.6984372057e10 | 1.668089751e12 |
| dbfs:/enwiki-latest-page.sql | enwiki-latest-page.sql | 6.806777427e9 | 1.667490845e12 |
| dbfs:/enwiki-latest-pagelinks.sql | enwiki-latest-pagelinks.sql | 5.6914316021e10 | 1.668034606e12 |
| dbfs:/enwiki-latest-redirect.sql | enwiki-latest-redirect.sql | 5.84026026e8 | 1.668189101e12 |
| dbfs:/enwiki-page.csv/ | enwiki-page.csv/ | 0.0 | 1.670236037456e12 |
| dbfs:/files/ | files/ | 0.0 | 1.670236037456e12 |
| dbfs:/graphXcheckpointdir/ | graphXcheckpointdir/ | 0.0 | 1.670236037456e12 |
| dbfs:/graphs/ | graphs/ | 0.0 | 1.670236037456e12 |
| dbfs:/horovod/ | horovod/ | 0.0 | 1.670236037456e12 |
| dbfs:/local_disk0/ | local_disk0/ | 0.0 | 1.670236037456e12 |
| dbfs:/logs/ | logs/ | 0.0 | 1.670236037456e12 |
| dbfs:/ml/ | ml/ | 0.0 | 1.670236037456e12 |
| dbfs:/nonBlmEmbeddings/ | nonBlmEmbeddings/ | 0.0 | 1.670236037456e12 |
| dbfs:/pipelines/ | pipelines/ | 0.0 | 1.670236037456e12 |
| dbfs:/project2022/ | project2022/ | 0.0 | 1.670236037456e12 |
| dbfs:/projectMEP/ | projectMEP/ | 0.0 | 1.670236037456e12 |
| dbfs:/projects2022/ | projects2022/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety/ | roadSafety/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety2017-01-09/ | roadSafety2017-01-09/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety2017-01-31_CD/ | roadSafety2017-01-31_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety2017-02-18/ | roadSafety2017-02-18/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-01/ | roadSafety_2017-01-01/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-01_CD/ | roadSafety_2017-01-01_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-02/ | roadSafety_2017-01-02/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-02_CD/ | roadSafety_2017-01-02_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-03/ | roadSafety_2017-01-03/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-03_CD/ | roadSafety_2017-01-03_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-04/ | roadSafety_2017-01-04/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-04_CD/ | roadSafety_2017-01-04_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-05/ | roadSafety_2017-01-05/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-05_CD/ | roadSafety_2017-01-05_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-06/ | roadSafety_2017-01-06/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-06_CD/ | roadSafety_2017-01-06_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-07/ | roadSafety_2017-01-07/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-07_CD/ | roadSafety_2017-01-07_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-08/ | roadSafety_2017-01-08/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-08_CD/ | roadSafety_2017-01-08_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-09/ | roadSafety_2017-01-09/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-09_CD/ | roadSafety_2017-01-09_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-10/ | roadSafety_2017-01-10/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-10_CD/ | roadSafety_2017-01-10_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-11/ | roadSafety_2017-01-11/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-11_CD/ | roadSafety_2017-01-11_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-12/ | roadSafety_2017-01-12/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-12_CD/ | roadSafety_2017-01-12_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-13/ | roadSafety_2017-01-13/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-13_CD/ | roadSafety_2017-01-13_CD/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-14/ | roadSafety_2017-01-14/ | 0.0 | 1.670236037456e12 |
| dbfs:/roadSafety_2017-01-14_CD/ | roadSafety_2017-01-14_CD/ | 0.0 | 1.670236037457e12 |
| dbfs:/roadSafety_2017-01-15/ | roadSafety_2017-01-15/ | 0.0 | 1.670236037457e12 |
| dbfs:/roadSafety_2017-01-15_CD/ | roadSafety_2017-01-15_CD/ | 0.0 | 1.670236037457e12 |
| dbfs:/roadSafety_2017-01-16/ | roadSafety_2017-01-16/ | 0.0 | 1.670236037457e12 |
| dbfs:/roadSafety_2017-01-16_CD/ | roadSafety_2017-01-16_CD/ | 0.0 | 1.670236037457e12 |
| dbfs:/roadSafety_2017-01-17/ | roadSafety_2017-01-17/ | 0.0 | 1.670236037457e12 |
| dbfs:/roadSafety_2017-01-17_CD/ | roadSafety_2017-01-17_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-18/ | roadSafety_2017-01-18/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-18_CD/ | roadSafety_2017-01-18_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-19/ | roadSafety_2017-01-19/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-19_CD/ | roadSafety_2017-01-19_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-20/ | roadSafety_2017-01-20/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-20_CD/ | roadSafety_2017-01-20_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-21/ | roadSafety_2017-01-21/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-21_CD/ | roadSafety_2017-01-21_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-22/ | roadSafety_2017-01-22/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-22_CD/ | roadSafety_2017-01-22_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-23/ | roadSafety_2017-01-23/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-23_CD/ | roadSafety_2017-01-23_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-24/ | roadSafety_2017-01-24/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-24_CD/ | roadSafety_2017-01-24_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-25/ | roadSafety_2017-01-25/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-25_CD/ | roadSafety_2017-01-25_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-26/ | roadSafety_2017-01-26/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-26_CD/ | roadSafety_2017-01-26_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-27/ | roadSafety_2017-01-27/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-27_CD/ | roadSafety_2017-01-27_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-28/ | roadSafety_2017-01-28/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-28_CD/ | roadSafety_2017-01-28_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-29/ | roadSafety_2017-01-29/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-29_CD/ | roadSafety_2017-01-29_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-30/ | roadSafety_2017-01-30/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-30_CD/ | roadSafety_2017-01-30_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-31/ | roadSafety_2017-01-31/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-01-31_CD/ | roadSafety_2017-01-31_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-01/ | roadSafety_2017-02-01/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-01_CD/ | roadSafety_2017-02-01_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-02/ | roadSafety_2017-02-02/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-02_CD/ | roadSafety_2017-02-02_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-03/ | roadSafety_2017-02-03/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-03_CD/ | roadSafety_2017-02-03_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-04/ | roadSafety_2017-02-04/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-04_CD/ | roadSafety_2017-02-04_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-05/ | roadSafety_2017-02-05/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-05_CD/ | roadSafety_2017-02-05_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-06/ | roadSafety_2017-02-06/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-06_CD/ | roadSafety_2017-02-06_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-07/ | roadSafety_2017-02-07/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-07_CD/ | roadSafety_2017-02-07_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-08/ | roadSafety_2017-02-08/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-08_CD/ | roadSafety_2017-02-08_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-10/ | roadSafety_2017-02-10/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-10_CD/ | roadSafety_2017-02-10_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-11/ | roadSafety_2017-02-11/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-11_CD/ | roadSafety_2017-02-11_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-12/ | roadSafety_2017-02-12/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-12_CD/ | roadSafety_2017-02-12_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-13/ | roadSafety_2017-02-13/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-13_CD/ | roadSafety_2017-02-13_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-14/ | roadSafety_2017-02-14/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-14_CD/ | roadSafety_2017-02-14_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-15/ | roadSafety_2017-02-15/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-15_CD/ | roadSafety_2017-02-15_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-16/ | roadSafety_2017-02-16/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-16_CD/ | roadSafety_2017-02-16_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-17/ | roadSafety_2017-02-17/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-17_CD/ | roadSafety_2017-02-17_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-18/ | roadSafety_2017-02-18/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-18_CD/ | roadSafety_2017-02-18_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-19/ | roadSafety_2017-02-19/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-19_CD/ | roadSafety_2017-02-19_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-20/ | roadSafety_2017-02-20/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-20_CD/ | roadSafety_2017-02-20_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-21/ | roadSafety_2017-02-21/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-21_CD/ | roadSafety_2017-02-21_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-22/ | roadSafety_2017-02-22/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-22_CD/ | roadSafety_2017-02-22_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-23/ | roadSafety_2017-02-23/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-23_CD/ | roadSafety_2017-02-23_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-24/ | roadSafety_2017-02-24/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-24_CD/ | roadSafety_2017-02-24_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-25/ | roadSafety_2017-02-25/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-25_CD/ | roadSafety_2017-02-25_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-26/ | roadSafety_2017-02-26/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-26_CD/ | roadSafety_2017-02-26_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-27/ | roadSafety_2017-02-27/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-27_CD/ | roadSafety_2017-02-27_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-28/ | roadSafety_2017-02-28/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-02-28_CD/ | roadSafety_2017-02-28_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-01/ | roadSafety_2017-03-01/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-01_CD/ | roadSafety_2017-03-01_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-02/ | roadSafety_2017-03-02/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-02_CD/ | roadSafety_2017-03-02_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-03/ | roadSafety_2017-03-03/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-03_CD/ | roadSafety_2017-03-03_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-04/ | roadSafety_2017-03-04/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-04_CD/ | roadSafety_2017-03-04_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-05/ | roadSafety_2017-03-05/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-05_CD/ | roadSafety_2017-03-05_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-06/ | roadSafety_2017-03-06/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-06_CD/ | roadSafety_2017-03-06_CD/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-07/ | roadSafety_2017-03-07/ | 0.0 | 1.670236037458e12 |
| dbfs:/roadSafety_2017-03-07_CD/ | roadSafety_2017-03-07_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-08/ | roadSafety_2017-03-08/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-08_CD/ | roadSafety_2017-03-08_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-09/ | roadSafety_2017-03-09/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-09_CD/ | roadSafety_2017-03-09_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-10/ | roadSafety_2017-03-10/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-10_CD/ | roadSafety_2017-03-10_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-11/ | roadSafety_2017-03-11/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-11_CD/ | roadSafety_2017-03-11_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-12/ | roadSafety_2017-03-12/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-12_CD/ | roadSafety_2017-03-12_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-13/ | roadSafety_2017-03-13/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-13_CD/ | roadSafety_2017-03-13_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-14/ | roadSafety_2017-03-14/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-14_CD/ | roadSafety_2017-03-14_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-15/ | roadSafety_2017-03-15/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-15_CD/ | roadSafety_2017-03-15_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-16/ | roadSafety_2017-03-16/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-16_CD/ | roadSafety_2017-03-16_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-17/ | roadSafety_2017-03-17/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-17_CD/ | roadSafety_2017-03-17_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-18/ | roadSafety_2017-03-18/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-18_CD/ | roadSafety_2017-03-18_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-19/ | roadSafety_2017-03-19/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-19_CD/ | roadSafety_2017-03-19_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-20/ | roadSafety_2017-03-20/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-20_CD/ | roadSafety_2017-03-20_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-21/ | roadSafety_2017-03-21/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-21_CD/ | roadSafety_2017-03-21_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-22/ | roadSafety_2017-03-22/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-22_CD/ | roadSafety_2017-03-22_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-23/ | roadSafety_2017-03-23/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-23_CD/ | roadSafety_2017-03-23_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-24/ | roadSafety_2017-03-24/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-24_CD/ | roadSafety_2017-03-24_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-25/ | roadSafety_2017-03-25/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-25_CD/ | roadSafety_2017-03-25_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-26/ | roadSafety_2017-03-26/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-26_CD/ | roadSafety_2017-03-26_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-27/ | roadSafety_2017-03-27/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-27_CD/ | roadSafety_2017-03-27_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-28/ | roadSafety_2017-03-28/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-28_CD/ | roadSafety_2017-03-28_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-29/ | roadSafety_2017-03-29/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-29_CD/ | roadSafety_2017-03-29_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-30/ | roadSafety_2017-03-30/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-30_CD/ | roadSafety_2017-03-30_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-31/ | roadSafety_2017-03-31/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-03-31_CD/ | roadSafety_2017-03-31_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-01/ | roadSafety_2017-04-01/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-01_CD/ | roadSafety_2017-04-01_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-02/ | roadSafety_2017-04-02/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-02_CD/ | roadSafety_2017-04-02_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-03/ | roadSafety_2017-04-03/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-03_CD/ | roadSafety_2017-04-03_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-04/ | roadSafety_2017-04-04/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-04_CD/ | roadSafety_2017-04-04_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-05/ | roadSafety_2017-04-05/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-05_CD/ | roadSafety_2017-04-05_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-06/ | roadSafety_2017-04-06/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-06_CD/ | roadSafety_2017-04-06_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-07/ | roadSafety_2017-04-07/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-07_CD/ | roadSafety_2017-04-07_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-08/ | roadSafety_2017-04-08/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-08_CD/ | roadSafety_2017-04-08_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-09/ | roadSafety_2017-04-09/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-09_CD/ | roadSafety_2017-04-09_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-10/ | roadSafety_2017-04-10/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-10_CD/ | roadSafety_2017-04-10_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-11/ | roadSafety_2017-04-11/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-11_CD/ | roadSafety_2017-04-11_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-12/ | roadSafety_2017-04-12/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-12_CD/ | roadSafety_2017-04-12_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-13/ | roadSafety_2017-04-13/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-13_CD/ | roadSafety_2017-04-13_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-14/ | roadSafety_2017-04-14/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-14_CD/ | roadSafety_2017-04-14_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-15/ | roadSafety_2017-04-15/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-15_CD/ | roadSafety_2017-04-15_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-16/ | roadSafety_2017-04-16/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-16_CD/ | roadSafety_2017-04-16_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-17/ | roadSafety_2017-04-17/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-17_CD/ | roadSafety_2017-04-17_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-18/ | roadSafety_2017-04-18/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-18_CD/ | roadSafety_2017-04-18_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-19/ | roadSafety_2017-04-19/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-19_CD/ | roadSafety_2017-04-19_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-20/ | roadSafety_2017-04-20/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-20_CD/ | roadSafety_2017-04-20_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-21/ | roadSafety_2017-04-21/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-21_CD/ | roadSafety_2017-04-21_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-22/ | roadSafety_2017-04-22/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-22_CD/ | roadSafety_2017-04-22_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-23/ | roadSafety_2017-04-23/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-23_CD/ | roadSafety_2017-04-23_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-24/ | roadSafety_2017-04-24/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-24_CD/ | roadSafety_2017-04-24_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-25/ | roadSafety_2017-04-25/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-25_CD/ | roadSafety_2017-04-25_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-26/ | roadSafety_2017-04-26/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-26_CD/ | roadSafety_2017-04-26_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-27/ | roadSafety_2017-04-27/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-27_CD/ | roadSafety_2017-04-27_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-28/ | roadSafety_2017-04-28/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-28_CD/ | roadSafety_2017-04-28_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-29/ | roadSafety_2017-04-29/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-29_CD/ | roadSafety_2017-04-29_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-30/ | roadSafety_2017-04-30/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-04-30_CD/ | roadSafety_2017-04-30_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-01/ | roadSafety_2017-05-01/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-01_CD/ | roadSafety_2017-05-01_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-02/ | roadSafety_2017-05-02/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-02_CD/ | roadSafety_2017-05-02_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-03/ | roadSafety_2017-05-03/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-03_CD/ | roadSafety_2017-05-03_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-04/ | roadSafety_2017-05-04/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-04_CD/ | roadSafety_2017-05-04_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-05/ | roadSafety_2017-05-05/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-05_CD/ | roadSafety_2017-05-05_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-06/ | roadSafety_2017-05-06/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-06_CD/ | roadSafety_2017-05-06_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-07/ | roadSafety_2017-05-07/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-07_CD/ | roadSafety_2017-05-07_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-08/ | roadSafety_2017-05-08/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-08_CD/ | roadSafety_2017-05-08_CD/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-09/ | roadSafety_2017-05-09/ | 0.0 | 1.67023603746e12 |
| dbfs:/roadSafety_2017-05-09_CD/ | roadSafety_2017-05-09_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-10/ | roadSafety_2017-05-10/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-10_CD/ | roadSafety_2017-05-10_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-11/ | roadSafety_2017-05-11/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-11_CD/ | roadSafety_2017-05-11_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-12/ | roadSafety_2017-05-12/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-12_CD/ | roadSafety_2017-05-12_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-13/ | roadSafety_2017-05-13/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-13_CD/ | roadSafety_2017-05-13_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-14/ | roadSafety_2017-05-14/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-14_CD/ | roadSafety_2017-05-14_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-15/ | roadSafety_2017-05-15/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-15_CD/ | roadSafety_2017-05-15_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-16/ | roadSafety_2017-05-16/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-16_CD/ | roadSafety_2017-05-16_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-17/ | roadSafety_2017-05-17/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-17_CD/ | roadSafety_2017-05-17_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-18/ | roadSafety_2017-05-18/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-18_CD/ | roadSafety_2017-05-18_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-19/ | roadSafety_2017-05-19/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-19_CD/ | roadSafety_2017-05-19_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-20/ | roadSafety_2017-05-20/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-20_CD/ | roadSafety_2017-05-20_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-21/ | roadSafety_2017-05-21/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-21_CD/ | roadSafety_2017-05-21_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-22/ | roadSafety_2017-05-22/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-22_CD/ | roadSafety_2017-05-22_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-23/ | roadSafety_2017-05-23/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-23_CD/ | roadSafety_2017-05-23_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-24/ | roadSafety_2017-05-24/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-24_CD/ | roadSafety_2017-05-24_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-25/ | roadSafety_2017-05-25/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-25_CD/ | roadSafety_2017-05-25_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-26/ | roadSafety_2017-05-26/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-26_CD/ | roadSafety_2017-05-26_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-27/ | roadSafety_2017-05-27/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-27_CD/ | roadSafety_2017-05-27_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-28/ | roadSafety_2017-05-28/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-28_CD/ | roadSafety_2017-05-28_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-29/ | roadSafety_2017-05-29/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-29_CD/ | roadSafety_2017-05-29_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-30/ | roadSafety_2017-05-30/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-30_CD/ | roadSafety_2017-05-30_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-31/ | roadSafety_2017-05-31/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-05-31_CD/ | roadSafety_2017-05-31_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-01/ | roadSafety_2017-06-01/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-01_CD/ | roadSafety_2017-06-01_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-02/ | roadSafety_2017-06-02/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-02_CD/ | roadSafety_2017-06-02_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-03/ | roadSafety_2017-06-03/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-03_CD/ | roadSafety_2017-06-03_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-04/ | roadSafety_2017-06-04/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-04_CD/ | roadSafety_2017-06-04_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-05/ | roadSafety_2017-06-05/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-05_CD/ | roadSafety_2017-06-05_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-06/ | roadSafety_2017-06-06/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-06_CD/ | roadSafety_2017-06-06_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-07/ | roadSafety_2017-06-07/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-07_CD/ | roadSafety_2017-06-07_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-08/ | roadSafety_2017-06-08/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-08_CD/ | roadSafety_2017-06-08_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-09/ | roadSafety_2017-06-09/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-09_CD/ | roadSafety_2017-06-09_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-10/ | roadSafety_2017-06-10/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-10_CD/ | roadSafety_2017-06-10_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-11/ | roadSafety_2017-06-11/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-11_CD/ | roadSafety_2017-06-11_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-12/ | roadSafety_2017-06-12/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-12_CD/ | roadSafety_2017-06-12_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-13/ | roadSafety_2017-06-13/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-13_CD/ | roadSafety_2017-06-13_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-14/ | roadSafety_2017-06-14/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-14_CD/ | roadSafety_2017-06-14_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-15/ | roadSafety_2017-06-15/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-15_CD/ | roadSafety_2017-06-15_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-16/ | roadSafety_2017-06-16/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-16_CD/ | roadSafety_2017-06-16_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-17/ | roadSafety_2017-06-17/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-17_CD/ | roadSafety_2017-06-17_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-18/ | roadSafety_2017-06-18/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-18_CD/ | roadSafety_2017-06-18_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-19/ | roadSafety_2017-06-19/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-19_CD/ | roadSafety_2017-06-19_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-20/ | roadSafety_2017-06-20/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-20_CD/ | roadSafety_2017-06-20_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-21/ | roadSafety_2017-06-21/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-21_CD/ | roadSafety_2017-06-21_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-22/ | roadSafety_2017-06-22/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-22_CD/ | roadSafety_2017-06-22_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-23/ | roadSafety_2017-06-23/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-23_CD/ | roadSafety_2017-06-23_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-24/ | roadSafety_2017-06-24/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-24_CD/ | roadSafety_2017-06-24_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-25/ | roadSafety_2017-06-25/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-25_CD/ | roadSafety_2017-06-25_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-26/ | roadSafety_2017-06-26/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-26_CD/ | roadSafety_2017-06-26_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-27/ | roadSafety_2017-06-27/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-27_CD/ | roadSafety_2017-06-27_CD/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-28/ | roadSafety_2017-06-28/ | 0.0 | 1.670236037461e12 |
| dbfs:/roadSafety_2017-06-28_CD/ | roadSafety_2017-06-28_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-06-29/ | roadSafety_2017-06-29/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-06-29_CD/ | roadSafety_2017-06-29_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-06-30/ | roadSafety_2017-06-30/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-06-30_CD/ | roadSafety_2017-06-30_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-01/ | roadSafety_2017-07-01/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-01_CD/ | roadSafety_2017-07-01_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-02/ | roadSafety_2017-07-02/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-02_CD/ | roadSafety_2017-07-02_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-03/ | roadSafety_2017-07-03/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-03_CD/ | roadSafety_2017-07-03_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-04/ | roadSafety_2017-07-04/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-04_CD/ | roadSafety_2017-07-04_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-05/ | roadSafety_2017-07-05/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-05_CD/ | roadSafety_2017-07-05_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-06/ | roadSafety_2017-07-06/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-06_CD/ | roadSafety_2017-07-06_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-07/ | roadSafety_2017-07-07/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-07_CD/ | roadSafety_2017-07-07_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-08/ | roadSafety_2017-07-08/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-08_CD/ | roadSafety_2017-07-08_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-09/ | roadSafety_2017-07-09/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-09_CD/ | roadSafety_2017-07-09_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-10/ | roadSafety_2017-07-10/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-10_CD/ | roadSafety_2017-07-10_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-11/ | roadSafety_2017-07-11/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-11_CD/ | roadSafety_2017-07-11_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-12/ | roadSafety_2017-07-12/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-12_CD/ | roadSafety_2017-07-12_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-13/ | roadSafety_2017-07-13/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-13_CD/ | roadSafety_2017-07-13_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-14/ | roadSafety_2017-07-14/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-14_CD/ | roadSafety_2017-07-14_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-15/ | roadSafety_2017-07-15/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-15_CD/ | roadSafety_2017-07-15_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-16/ | roadSafety_2017-07-16/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-16_CD/ | roadSafety_2017-07-16_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-17/ | roadSafety_2017-07-17/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-17_CD/ | roadSafety_2017-07-17_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-18/ | roadSafety_2017-07-18/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-18_CD/ | roadSafety_2017-07-18_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-19/ | roadSafety_2017-07-19/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-19_CD/ | roadSafety_2017-07-19_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-20/ | roadSafety_2017-07-20/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-20_CD/ | roadSafety_2017-07-20_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-21/ | roadSafety_2017-07-21/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-21_CD/ | roadSafety_2017-07-21_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-22/ | roadSafety_2017-07-22/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-22_CD/ | roadSafety_2017-07-22_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-23/ | roadSafety_2017-07-23/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-23_CD/ | roadSafety_2017-07-23_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-24/ | roadSafety_2017-07-24/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-24_CD/ | roadSafety_2017-07-24_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-25/ | roadSafety_2017-07-25/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-25_CD/ | roadSafety_2017-07-25_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-26/ | roadSafety_2017-07-26/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-26_CD/ | roadSafety_2017-07-26_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-27/ | roadSafety_2017-07-27/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-27_CD/ | roadSafety_2017-07-27_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-28/ | roadSafety_2017-07-28/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-28_CD/ | roadSafety_2017-07-28_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-29/ | roadSafety_2017-07-29/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-29_CD/ | roadSafety_2017-07-29_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-30/ | roadSafety_2017-07-30/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-30_CD/ | roadSafety_2017-07-30_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-31/ | roadSafety_2017-07-31/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-07-31_CD/ | roadSafety_2017-07-31_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-01/ | roadSafety_2017-08-01/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-01_CD/ | roadSafety_2017-08-01_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-02/ | roadSafety_2017-08-02/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-02_CD/ | roadSafety_2017-08-02_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-03/ | roadSafety_2017-08-03/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-03_CD/ | roadSafety_2017-08-03_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-04/ | roadSafety_2017-08-04/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-04_CD/ | roadSafety_2017-08-04_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-05/ | roadSafety_2017-08-05/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-05_CD/ | roadSafety_2017-08-05_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-06/ | roadSafety_2017-08-06/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-06_CD/ | roadSafety_2017-08-06_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-07/ | roadSafety_2017-08-07/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-07_CD/ | roadSafety_2017-08-07_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-08/ | roadSafety_2017-08-08/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-08_CD/ | roadSafety_2017-08-08_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-09/ | roadSafety_2017-08-09/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-09_CD/ | roadSafety_2017-08-09_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-10/ | roadSafety_2017-08-10/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-10_CD/ | roadSafety_2017-08-10_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-11/ | roadSafety_2017-08-11/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-11_CD/ | roadSafety_2017-08-11_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-12/ | roadSafety_2017-08-12/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-12_CD/ | roadSafety_2017-08-12_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-13/ | roadSafety_2017-08-13/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-13_CD/ | roadSafety_2017-08-13_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-14/ | roadSafety_2017-08-14/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-14_CD/ | roadSafety_2017-08-14_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-15/ | roadSafety_2017-08-15/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-15_CD/ | roadSafety_2017-08-15_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-16/ | roadSafety_2017-08-16/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-16_CD/ | roadSafety_2017-08-16_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-17/ | roadSafety_2017-08-17/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-17_CD/ | roadSafety_2017-08-17_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-18/ | roadSafety_2017-08-18/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-18_CD/ | roadSafety_2017-08-18_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-19/ | roadSafety_2017-08-19/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-19_CD/ | roadSafety_2017-08-19_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-20/ | roadSafety_2017-08-20/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-20_CD/ | roadSafety_2017-08-20_CD/ | 0.0 | 1.670236037462e12 |
| dbfs:/roadSafety_2017-08-21/ | roadSafety_2017-08-21/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-21_CD/ | roadSafety_2017-08-21_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-22/ | roadSafety_2017-08-22/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-22_CD/ | roadSafety_2017-08-22_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-23/ | roadSafety_2017-08-23/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-23_CD/ | roadSafety_2017-08-23_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-24/ | roadSafety_2017-08-24/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-24_CD/ | roadSafety_2017-08-24_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-25/ | roadSafety_2017-08-25/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-25_CD/ | roadSafety_2017-08-25_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-26/ | roadSafety_2017-08-26/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-26_CD/ | roadSafety_2017-08-26_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-27/ | roadSafety_2017-08-27/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-27_CD/ | roadSafety_2017-08-27_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-28/ | roadSafety_2017-08-28/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-28_CD/ | roadSafety_2017-08-28_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-29/ | roadSafety_2017-08-29/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-29_CD/ | roadSafety_2017-08-29_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-30/ | roadSafety_2017-08-30/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-30_CD/ | roadSafety_2017-08-30_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-31/ | roadSafety_2017-08-31/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-08-31_CD/ | roadSafety_2017-08-31_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-01/ | roadSafety_2017-09-01/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-01_CD/ | roadSafety_2017-09-01_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-02/ | roadSafety_2017-09-02/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-02_CD/ | roadSafety_2017-09-02_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-03/ | roadSafety_2017-09-03/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-03_CD/ | roadSafety_2017-09-03_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-04/ | roadSafety_2017-09-04/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-04_CD/ | roadSafety_2017-09-04_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-05/ | roadSafety_2017-09-05/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-05_CD/ | roadSafety_2017-09-05_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-06/ | roadSafety_2017-09-06/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-06_CD/ | roadSafety_2017-09-06_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-07/ | roadSafety_2017-09-07/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-07_CD/ | roadSafety_2017-09-07_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-08/ | roadSafety_2017-09-08/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-08_CD/ | roadSafety_2017-09-08_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-09/ | roadSafety_2017-09-09/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-09_CD/ | roadSafety_2017-09-09_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-10/ | roadSafety_2017-09-10/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-10_CD/ | roadSafety_2017-09-10_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-11/ | roadSafety_2017-09-11/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-11_CD/ | roadSafety_2017-09-11_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-12/ | roadSafety_2017-09-12/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-12_CD/ | roadSafety_2017-09-12_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-13/ | roadSafety_2017-09-13/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-13_CD/ | roadSafety_2017-09-13_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-14/ | roadSafety_2017-09-14/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-14_CD/ | roadSafety_2017-09-14_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-15/ | roadSafety_2017-09-15/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-15_CD/ | roadSafety_2017-09-15_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-16/ | roadSafety_2017-09-16/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-16_CD/ | roadSafety_2017-09-16_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-17/ | roadSafety_2017-09-17/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-17_CD/ | roadSafety_2017-09-17_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-18/ | roadSafety_2017-09-18/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-18_CD/ | roadSafety_2017-09-18_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-19/ | roadSafety_2017-09-19/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-19_CD/ | roadSafety_2017-09-19_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-20/ | roadSafety_2017-09-20/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-20_CD/ | roadSafety_2017-09-20_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-21/ | roadSafety_2017-09-21/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-21_CD/ | roadSafety_2017-09-21_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-22/ | roadSafety_2017-09-22/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-22_CD/ | roadSafety_2017-09-22_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-23/ | roadSafety_2017-09-23/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-23_CD/ | roadSafety_2017-09-23_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-24/ | roadSafety_2017-09-24/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-24_CD/ | roadSafety_2017-09-24_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-25/ | roadSafety_2017-09-25/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-25_CD/ | roadSafety_2017-09-25_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-26/ | roadSafety_2017-09-26/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-26_CD/ | roadSafety_2017-09-26_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-27/ | roadSafety_2017-09-27/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-27_CD/ | roadSafety_2017-09-27_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-28/ | roadSafety_2017-09-28/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-28_CD/ | roadSafety_2017-09-28_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-29/ | roadSafety_2017-09-29/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-29_CD/ | roadSafety_2017-09-29_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-30/ | roadSafety_2017-09-30/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-09-30_CD/ | roadSafety_2017-09-30_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-01/ | roadSafety_2017-10-01/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-01_CD/ | roadSafety_2017-10-01_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-02/ | roadSafety_2017-10-02/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-02_CD/ | roadSafety_2017-10-02_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-03/ | roadSafety_2017-10-03/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-03_CD/ | roadSafety_2017-10-03_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-04/ | roadSafety_2017-10-04/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-04_CD/ | roadSafety_2017-10-04_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-05/ | roadSafety_2017-10-05/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-05_CD/ | roadSafety_2017-10-05_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-06/ | roadSafety_2017-10-06/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-06_CD/ | roadSafety_2017-10-06_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-07/ | roadSafety_2017-10-07/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-07_CD/ | roadSafety_2017-10-07_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-08/ | roadSafety_2017-10-08/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-08_CD/ | roadSafety_2017-10-08_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-09/ | roadSafety_2017-10-09/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-09_CD/ | roadSafety_2017-10-09_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-10/ | roadSafety_2017-10-10/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-10_CD/ | roadSafety_2017-10-10_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-11/ | roadSafety_2017-10-11/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-11_CD/ | roadSafety_2017-10-11_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-12/ | roadSafety_2017-10-12/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-12_CD/ | roadSafety_2017-10-12_CD/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-13/ | roadSafety_2017-10-13/ | 0.0 | 1.670236037463e12 |
| dbfs:/roadSafety_2017-10-13_CD/ | roadSafety_2017-10-13_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-14/ | roadSafety_2017-10-14/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-14_CD/ | roadSafety_2017-10-14_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-15/ | roadSafety_2017-10-15/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-15_CD/ | roadSafety_2017-10-15_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-16/ | roadSafety_2017-10-16/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-16_CD/ | roadSafety_2017-10-16_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-17/ | roadSafety_2017-10-17/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-17_CD/ | roadSafety_2017-10-17_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-18/ | roadSafety_2017-10-18/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-18_CD/ | roadSafety_2017-10-18_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-19/ | roadSafety_2017-10-19/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-19_CD/ | roadSafety_2017-10-19_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-20/ | roadSafety_2017-10-20/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-20_CD/ | roadSafety_2017-10-20_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-21/ | roadSafety_2017-10-21/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-21_CD/ | roadSafety_2017-10-21_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-22/ | roadSafety_2017-10-22/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-22_CD/ | roadSafety_2017-10-22_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-23/ | roadSafety_2017-10-23/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-23_CD/ | roadSafety_2017-10-23_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-24/ | roadSafety_2017-10-24/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-24_CD/ | roadSafety_2017-10-24_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-25/ | roadSafety_2017-10-25/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-25_CD/ | roadSafety_2017-10-25_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-26/ | roadSafety_2017-10-26/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-26_CD/ | roadSafety_2017-10-26_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-27/ | roadSafety_2017-10-27/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-27_CD/ | roadSafety_2017-10-27_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-28/ | roadSafety_2017-10-28/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-28_CD/ | roadSafety_2017-10-28_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-29/ | roadSafety_2017-10-29/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-29_CD/ | roadSafety_2017-10-29_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-30/ | roadSafety_2017-10-30/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-30_CD/ | roadSafety_2017-10-30_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-31/ | roadSafety_2017-10-31/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-10-31_CD/ | roadSafety_2017-10-31_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-01/ | roadSafety_2017-11-01/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-01_CD/ | roadSafety_2017-11-01_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-02/ | roadSafety_2017-11-02/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-02_CD/ | roadSafety_2017-11-02_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-03/ | roadSafety_2017-11-03/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-03_CD/ | roadSafety_2017-11-03_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-04/ | roadSafety_2017-11-04/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-04_CD/ | roadSafety_2017-11-04_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-05/ | roadSafety_2017-11-05/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-05_CD/ | roadSafety_2017-11-05_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-06/ | roadSafety_2017-11-06/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-06_CD/ | roadSafety_2017-11-06_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-07/ | roadSafety_2017-11-07/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-07_CD/ | roadSafety_2017-11-07_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-08/ | roadSafety_2017-11-08/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-08_CD/ | roadSafety_2017-11-08_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-09/ | roadSafety_2017-11-09/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-09_CD/ | roadSafety_2017-11-09_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-10/ | roadSafety_2017-11-10/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-10_CD/ | roadSafety_2017-11-10_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-11/ | roadSafety_2017-11-11/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-11_CD/ | roadSafety_2017-11-11_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-12/ | roadSafety_2017-11-12/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-12_CD/ | roadSafety_2017-11-12_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-13/ | roadSafety_2017-11-13/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-13_CD/ | roadSafety_2017-11-13_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-14/ | roadSafety_2017-11-14/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-14_CD/ | roadSafety_2017-11-14_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-15/ | roadSafety_2017-11-15/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-15_CD/ | roadSafety_2017-11-15_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-16/ | roadSafety_2017-11-16/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-16_CD/ | roadSafety_2017-11-16_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-17/ | roadSafety_2017-11-17/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-17_CD/ | roadSafety_2017-11-17_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-18/ | roadSafety_2017-11-18/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-18_CD/ | roadSafety_2017-11-18_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-19/ | roadSafety_2017-11-19/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-19_CD/ | roadSafety_2017-11-19_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-20/ | roadSafety_2017-11-20/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-20_CD/ | roadSafety_2017-11-20_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-21/ | roadSafety_2017-11-21/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-21_CD/ | roadSafety_2017-11-21_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-22/ | roadSafety_2017-11-22/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-22_CD/ | roadSafety_2017-11-22_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-23/ | roadSafety_2017-11-23/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-23_CD/ | roadSafety_2017-11-23_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-24/ | roadSafety_2017-11-24/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-24_CD/ | roadSafety_2017-11-24_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-25/ | roadSafety_2017-11-25/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-25_CD/ | roadSafety_2017-11-25_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-26/ | roadSafety_2017-11-26/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-26_CD/ | roadSafety_2017-11-26_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-27/ | roadSafety_2017-11-27/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-27_CD/ | roadSafety_2017-11-27_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-28/ | roadSafety_2017-11-28/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-28_CD/ | roadSafety_2017-11-28_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-29/ | roadSafety_2017-11-29/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-29_CD/ | roadSafety_2017-11-29_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-30/ | roadSafety_2017-11-30/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-11-30_CD/ | roadSafety_2017-11-30_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-12-01/ | roadSafety_2017-12-01/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-12-01_CD/ | roadSafety_2017-12-01_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-12-02/ | roadSafety_2017-12-02/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-12-02_CD/ | roadSafety_2017-12-02_CD/ | 0.0 | 1.670236037464e12 |
| dbfs:/roadSafety_2017-12-03/ | roadSafety_2017-12-03/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-03_CD/ | roadSafety_2017-12-03_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-04/ | roadSafety_2017-12-04/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-04_CD/ | roadSafety_2017-12-04_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-05/ | roadSafety_2017-12-05/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-05_CD/ | roadSafety_2017-12-05_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-06/ | roadSafety_2017-12-06/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-06_CD/ | roadSafety_2017-12-06_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-07/ | roadSafety_2017-12-07/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-07_CD/ | roadSafety_2017-12-07_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-08/ | roadSafety_2017-12-08/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-08_CD/ | roadSafety_2017-12-08_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-09/ | roadSafety_2017-12-09/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-09_CD/ | roadSafety_2017-12-09_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-10/ | roadSafety_2017-12-10/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-10_CD/ | roadSafety_2017-12-10_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-11/ | roadSafety_2017-12-11/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-11_CD/ | roadSafety_2017-12-11_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-12/ | roadSafety_2017-12-12/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-12_CD/ | roadSafety_2017-12-12_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-13/ | roadSafety_2017-12-13/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-13_CD/ | roadSafety_2017-12-13_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-14/ | roadSafety_2017-12-14/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-14_CD/ | roadSafety_2017-12-14_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-15/ | roadSafety_2017-12-15/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-15_CD/ | roadSafety_2017-12-15_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-16/ | roadSafety_2017-12-16/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-16_CD/ | roadSafety_2017-12-16_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-17/ | roadSafety_2017-12-17/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-17_CD/ | roadSafety_2017-12-17_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-18/ | roadSafety_2017-12-18/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-18_CD/ | roadSafety_2017-12-18_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-19/ | roadSafety_2017-12-19/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-19_CD/ | roadSafety_2017-12-19_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-20/ | roadSafety_2017-12-20/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-20_CD/ | roadSafety_2017-12-20_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-21/ | roadSafety_2017-12-21/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-21_CD/ | roadSafety_2017-12-21_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-22/ | roadSafety_2017-12-22/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-22_CD/ | roadSafety_2017-12-22_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-23/ | roadSafety_2017-12-23/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-23_CD/ | roadSafety_2017-12-23_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-24/ | roadSafety_2017-12-24/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-24_CD/ | roadSafety_2017-12-24_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-25/ | roadSafety_2017-12-25/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-25_CD/ | roadSafety_2017-12-25_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-26/ | roadSafety_2017-12-26/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-26_CD/ | roadSafety_2017-12-26_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-27/ | roadSafety_2017-12-27/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-27_CD/ | roadSafety_2017-12-27_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-28/ | roadSafety_2017-12-28/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-28_CD/ | roadSafety_2017-12-28_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-29/ | roadSafety_2017-12-29/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-29_CD/ | roadSafety_2017-12-29_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-30/ | roadSafety_2017-12-30/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-30_CD/ | roadSafety_2017-12-30_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-31/ | roadSafety_2017-12-31/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2017-12-31_CD/ | roadSafety_2017-12-31_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-01/ | roadSafety_2018-01-01/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-01_CD/ | roadSafety_2018-01-01_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-02/ | roadSafety_2018-01-02/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-02_CD/ | roadSafety_2018-01-02_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-03/ | roadSafety_2018-01-03/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-03_CD/ | roadSafety_2018-01-03_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-04/ | roadSafety_2018-01-04/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-04_CD/ | roadSafety_2018-01-04_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-05/ | roadSafety_2018-01-05/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-05_CD/ | roadSafety_2018-01-05_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-06/ | roadSafety_2018-01-06/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-06_CD/ | roadSafety_2018-01-06_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-07/ | roadSafety_2018-01-07/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-07_CD/ | roadSafety_2018-01-07_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-08/ | roadSafety_2018-01-08/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-08_CD/ | roadSafety_2018-01-08_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-09/ | roadSafety_2018-01-09/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-09_CD/ | roadSafety_2018-01-09_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-10/ | roadSafety_2018-01-10/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-10_CD/ | roadSafety_2018-01-10_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-11/ | roadSafety_2018-01-11/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-11_CD/ | roadSafety_2018-01-11_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-12/ | roadSafety_2018-01-12/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-12_CD/ | roadSafety_2018-01-12_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-13/ | roadSafety_2018-01-13/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-13_CD/ | roadSafety_2018-01-13_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-14/ | roadSafety_2018-01-14/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-14_CD/ | roadSafety_2018-01-14_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-15/ | roadSafety_2018-01-15/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-15_CD/ | roadSafety_2018-01-15_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-16/ | roadSafety_2018-01-16/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-16_CD/ | roadSafety_2018-01-16_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-17/ | roadSafety_2018-01-17/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-17_CD/ | roadSafety_2018-01-17_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-18/ | roadSafety_2018-01-18/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-18_CD/ | roadSafety_2018-01-18_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-19/ | roadSafety_2018-01-19/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-19_CD/ | roadSafety_2018-01-19_CD/ | 0.0 | 1.670236037465e12 |
| dbfs:/roadSafety_2018-01-20/ | roadSafety_2018-01-20/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-20_CD/ | roadSafety_2018-01-20_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-21/ | roadSafety_2018-01-21/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-21_CD/ | roadSafety_2018-01-21_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-22/ | roadSafety_2018-01-22/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-22_CD/ | roadSafety_2018-01-22_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-23/ | roadSafety_2018-01-23/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-23_CD/ | roadSafety_2018-01-23_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-24/ | roadSafety_2018-01-24/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-24_CD/ | roadSafety_2018-01-24_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-25/ | roadSafety_2018-01-25/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-25_CD/ | roadSafety_2018-01-25_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-26/ | roadSafety_2018-01-26/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-26_CD/ | roadSafety_2018-01-26_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-27/ | roadSafety_2018-01-27/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-27_CD/ | roadSafety_2018-01-27_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-28/ | roadSafety_2018-01-28/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-28_CD/ | roadSafety_2018-01-28_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-29/ | roadSafety_2018-01-29/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-29_CD/ | roadSafety_2018-01-29_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-30/ | roadSafety_2018-01-30/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-30_CD/ | roadSafety_2018-01-30_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-31/ | roadSafety_2018-01-31/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-01-31_CD/ | roadSafety_2018-01-31_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-01/ | roadSafety_2018-02-01/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-01_CD/ | roadSafety_2018-02-01_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-02/ | roadSafety_2018-02-02/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-02_CD/ | roadSafety_2018-02-02_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-03/ | roadSafety_2018-02-03/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-03_CD/ | roadSafety_2018-02-03_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-04/ | roadSafety_2018-02-04/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-04_CD/ | roadSafety_2018-02-04_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-05/ | roadSafety_2018-02-05/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-05_CD/ | roadSafety_2018-02-05_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-06/ | roadSafety_2018-02-06/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-06_CD/ | roadSafety_2018-02-06_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-07/ | roadSafety_2018-02-07/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-07_CD/ | roadSafety_2018-02-07_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-08/ | roadSafety_2018-02-08/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-08_CD/ | roadSafety_2018-02-08_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-09/ | roadSafety_2018-02-09/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-09_CD/ | roadSafety_2018-02-09_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-10/ | roadSafety_2018-02-10/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-10_CD/ | roadSafety_2018-02-10_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-11/ | roadSafety_2018-02-11/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-11_CD/ | roadSafety_2018-02-11_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-12/ | roadSafety_2018-02-12/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-12_CD/ | roadSafety_2018-02-12_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-13/ | roadSafety_2018-02-13/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-13_CD/ | roadSafety_2018-02-13_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-14/ | roadSafety_2018-02-14/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-14_CD/ | roadSafety_2018-02-14_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-15/ | roadSafety_2018-02-15/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-15_CD/ | roadSafety_2018-02-15_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-16/ | roadSafety_2018-02-16/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-16_CD/ | roadSafety_2018-02-16_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-17/ | roadSafety_2018-02-17/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-17_CD/ | roadSafety_2018-02-17_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-18/ | roadSafety_2018-02-18/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-18_CD/ | roadSafety_2018-02-18_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-19/ | roadSafety_2018-02-19/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-19_CD/ | roadSafety_2018-02-19_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-20/ | roadSafety_2018-02-20/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-20_CD/ | roadSafety_2018-02-20_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-21/ | roadSafety_2018-02-21/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-21_CD/ | roadSafety_2018-02-21_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-22/ | roadSafety_2018-02-22/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-22_CD/ | roadSafety_2018-02-22_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-23/ | roadSafety_2018-02-23/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-23_CD/ | roadSafety_2018-02-23_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-24/ | roadSafety_2018-02-24/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-24_CD/ | roadSafety_2018-02-24_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-25/ | roadSafety_2018-02-25/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-25_CD/ | roadSafety_2018-02-25_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-26/ | roadSafety_2018-02-26/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-26_CD/ | roadSafety_2018-02-26_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-27/ | roadSafety_2018-02-27/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-27_CD/ | roadSafety_2018-02-27_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-28/ | roadSafety_2018-02-28/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-02-28_CD/ | roadSafety_2018-02-28_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-01/ | roadSafety_2018-03-01/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-01_CD/ | roadSafety_2018-03-01_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-02/ | roadSafety_2018-03-02/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-02_CD/ | roadSafety_2018-03-02_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-03/ | roadSafety_2018-03-03/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-03_CD/ | roadSafety_2018-03-03_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-04/ | roadSafety_2018-03-04/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-04_CD/ | roadSafety_2018-03-04_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-05/ | roadSafety_2018-03-05/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-05_CD/ | roadSafety_2018-03-05_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-06/ | roadSafety_2018-03-06/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-06_CD/ | roadSafety_2018-03-06_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-07/ | roadSafety_2018-03-07/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-07_CD/ | roadSafety_2018-03-07_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-08/ | roadSafety_2018-03-08/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-08_CD/ | roadSafety_2018-03-08_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-09/ | roadSafety_2018-03-09/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-09_CD/ | roadSafety_2018-03-09_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-10/ | roadSafety_2018-03-10/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-10_CD/ | roadSafety_2018-03-10_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-11/ | roadSafety_2018-03-11/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-11_CD/ | roadSafety_2018-03-11_CD/ | 0.0 | 1.670236037466e12 |
| dbfs:/roadSafety_2018-03-12/ | roadSafety_2018-03-12/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-12_CD/ | roadSafety_2018-03-12_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-13/ | roadSafety_2018-03-13/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-13_CD/ | roadSafety_2018-03-13_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-14/ | roadSafety_2018-03-14/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-14_CD/ | roadSafety_2018-03-14_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-15/ | roadSafety_2018-03-15/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-15_CD/ | roadSafety_2018-03-15_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-16/ | roadSafety_2018-03-16/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-16_CD/ | roadSafety_2018-03-16_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-17/ | roadSafety_2018-03-17/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-17_CD/ | roadSafety_2018-03-17_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-18/ | roadSafety_2018-03-18/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-18_CD/ | roadSafety_2018-03-18_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-19/ | roadSafety_2018-03-19/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-19_CD/ | roadSafety_2018-03-19_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-20/ | roadSafety_2018-03-20/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-20_CD/ | roadSafety_2018-03-20_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-21/ | roadSafety_2018-03-21/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-21_CD/ | roadSafety_2018-03-21_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-22/ | roadSafety_2018-03-22/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-22_CD/ | roadSafety_2018-03-22_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-23/ | roadSafety_2018-03-23/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-23_CD/ | roadSafety_2018-03-23_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-24/ | roadSafety_2018-03-24/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-24_CD/ | roadSafety_2018-03-24_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-25/ | roadSafety_2018-03-25/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-25_CD/ | roadSafety_2018-03-25_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-26/ | roadSafety_2018-03-26/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-26_CD/ | roadSafety_2018-03-26_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-27/ | roadSafety_2018-03-27/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-27_CD/ | roadSafety_2018-03-27_CD/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-28/ | roadSafety_2018-03-28/ | 0.0 | 1.670236037467e12 |
| dbfs:/roadSafety_2018-03-28_CD/ | roadSafety_2018-03-28_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-03-29/ | roadSafety_2018-03-29/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-03-29_CD/ | roadSafety_2018-03-29_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-03-30/ | roadSafety_2018-03-30/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-03-30_CD/ | roadSafety_2018-03-30_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-03-31/ | roadSafety_2018-03-31/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-03-31_CD/ | roadSafety_2018-03-31_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-01/ | roadSafety_2018-04-01/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-01_CD/ | roadSafety_2018-04-01_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-02/ | roadSafety_2018-04-02/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-02_CD/ | roadSafety_2018-04-02_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-03/ | roadSafety_2018-04-03/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-03_CD/ | roadSafety_2018-04-03_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-04/ | roadSafety_2018-04-04/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-04_CD/ | roadSafety_2018-04-04_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-05/ | roadSafety_2018-04-05/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-05_CD/ | roadSafety_2018-04-05_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-06/ | roadSafety_2018-04-06/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-06_CD/ | roadSafety_2018-04-06_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-07/ | roadSafety_2018-04-07/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-07_CD/ | roadSafety_2018-04-07_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-08/ | roadSafety_2018-04-08/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-08_CD/ | roadSafety_2018-04-08_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-09/ | roadSafety_2018-04-09/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-09_CD/ | roadSafety_2018-04-09_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-10/ | roadSafety_2018-04-10/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-10_CD/ | roadSafety_2018-04-10_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-11/ | roadSafety_2018-04-11/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-11_CD/ | roadSafety_2018-04-11_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-12/ | roadSafety_2018-04-12/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-12_CD/ | roadSafety_2018-04-12_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-13/ | roadSafety_2018-04-13/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-13_CD/ | roadSafety_2018-04-13_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-14/ | roadSafety_2018-04-14/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-14_CD/ | roadSafety_2018-04-14_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-15/ | roadSafety_2018-04-15/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-15_CD/ | roadSafety_2018-04-15_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-16/ | roadSafety_2018-04-16/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-16_CD/ | roadSafety_2018-04-16_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-17/ | roadSafety_2018-04-17/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-17_CD/ | roadSafety_2018-04-17_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-18/ | roadSafety_2018-04-18/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-18_CD/ | roadSafety_2018-04-18_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-19/ | roadSafety_2018-04-19/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-19_CD/ | roadSafety_2018-04-19_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-20/ | roadSafety_2018-04-20/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-20_CD/ | roadSafety_2018-04-20_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-21/ | roadSafety_2018-04-21/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-21_CD/ | roadSafety_2018-04-21_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-22/ | roadSafety_2018-04-22/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-22_CD/ | roadSafety_2018-04-22_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-23/ | roadSafety_2018-04-23/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-23_CD/ | roadSafety_2018-04-23_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-24/ | roadSafety_2018-04-24/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-24_CD/ | roadSafety_2018-04-24_CD/ | 0.0 | 1.670236037468e12 |
| dbfs:/roadSafety_2018-04-25/ | roadSafety_2018-04-25/ | 0.0 | 1.670236037468e12 |
pwd
Dataset: Human3.6M
Scripts for fetaching the data is based on https://github.com/anibali/h36m-fetch.
Requirements
- numpy==1.14.5
- tqdm==4.19.8
- h5py==2.7.1
- spacepy==0.2.3
- requests==2.20.0
from subprocess import call
from os import path, makedirs, listdir
import hashlib
from tqdm import tqdm
import configparser
import requests
import tarfile
from shutil import move
import traceback
from spacepy import pycdf
import numpy as np
import h5py
from subprocess import call
from tempfile import TemporaryDirectory
from metadata import load_h36m_metadata
import xml.etree.ElementTree as ET
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
FileUtils.copyURLToFile(new URL("https://raw.githubusercontent.com/anibali/h36m-fetch/master/metadata.xml"), new File("/tmp/metadata.xml"))
FileUtils.copyURLToFile(new URL("https://raw.githubusercontent.com/anibali/h36m-fetch/master/checksums.txt"), new File("/tmp/checksums.txt"))
import java.net.URL
import java.io.File
import org.apache.commons.io.FileUtils
class H36M_Metadata:
def __init__(self, metadata_file):
self.subjects = []
self.sequence_mappings = {}
self.action_names = {}
self.camera_ids = []
tree = ET.parse(metadata_file)
root = tree.getroot()
for i, tr in enumerate(root.find('mapping')):
if i == 0:
_, _, *self.subjects = [td.text for td in tr]
self.sequence_mappings = {subject: {} for subject in self.subjects}
elif i < 33:
action_id, subaction_id, *prefixes = [td.text for td in tr]
for subject, prefix in zip(self.subjects, prefixes):
self.sequence_mappings[subject][(action_id, subaction_id)] = prefix
for i, elem in enumerate(root.find('actionnames')):
action_id = str(i + 1)
self.action_names[action_id] = elem.text
self.camera_ids = [elem.text for elem in root.find('dbcameras/index2id')]
def get_base_filename(self, subject, action, subaction, camera):
return '{}.{}'.format(self.sequence_mappings[subject][(action, subaction)], camera)
def load_h36m_metadata():
return H36M_Metadata("/tmp/metadata.xml")
metadata = load_h36m_metadata()
print(metadata.subjects)
print(metadata.sequence_mappings)
print(metadata.action_names)
print(metadata.camera_ids)
BASE_URL = 'http://vision.imar.ro/human3.6m/filebrowser.php'
subjects = [
('S1', 1),
('S5', 6),
('S6', 7),
('S7', 2),
('S8', 3),
('S9', 4),
('S11', 5),
]
def md5(filename):
hash_md5 = hashlib.md5()
with open(filename, 'rb') as f:
for chunk in iter(lambda: f.read(4096), b''):
hash_md5.update(chunk)
return hash_md5.hexdigest()
def download_file(url, dest_file, phpsessid):
call(['axel',
'-a',
'-n', '24',
'-H', 'COOKIE: PHPSESSID=' + phpsessid,
'-o', dest_file,
url])
def get_phpsessid():
# TODO: enter phpsessid here
phpsessid = ""
return phpsessid
def get_phpsessid_config():
config = configparser.ConfigParser()
config.read('config.ini')
try:
phpsessid = config['General']['PHPSESSID']
except (KeyError, configparser.NoSectionError):
print('Could not read PHPSESSID from `config.ini`.')
phpsessid = input('Enter PHPSESSID: ')
return phpsessid
def verify_phpsessid(phpsessid):
requests.packages.urllib3.disable_warnings()
test_url = 'http://vision.imar.ro/human3.6m/filebrowser.php'
resp = requests.get(test_url, verify=False, cookies=dict(PHPSESSID=phpsessid))
fail_message = 'Failed to verify your PHPSESSID. Please ensure that you ' \
'are currently logged in at http://vision.imar.ro/human3.6m/ ' \
'and that you have copied the PHPSESSID cookie correctly.'
assert resp.url == test_url, fail_message
def download_all(phpsessid):
checksums = {}
with open("/tmp/checksums.txt", 'r') as f:
for line in f.read().splitlines(keepends=False):
v, k = line.split(' ')
checksums[k] = v
files = []
for subject_id, id in subjects:
files += [
('Poses_D2_Positions_{}.tgz'.format(subject_id),
'download=1&filepath=Poses/D2_Positions&filename=SubjectSpecific_{}.tgz'.format(id)),
('Poses_D3_Positions_{}.tgz'.format(subject_id),
'download=1&filepath=Poses/D3_Positions&filename=SubjectSpecific_{}.tgz'.format(id)),
('Poses_D3_Positions_mono_{}.tgz'.format(subject_id),
'download=1&filepath=Poses/D3_Positions_mono&filename=SubjectSpecific_{}.tgz'.format(id)),
('Poses_D3_Positions_mono_universal_{}.tgz'.format(subject_id),
'download=1&filepath=Poses/D3_Positions_mono_universal&filename=SubjectSpecific_{}.tgz'.format(id)),
('Videos_{}.tgz'.format(subject_id),
'download=1&filepath=Videos&filename=SubjectSpecific_{}.tgz'.format(id)),
]
out_dir = 'archives'
makedirs(out_dir, exist_ok=True)
for filename, query in tqdm(files, ascii=True):
out_file = path.join(out_dir, filename)
if path.isfile(out_file):
checksum = md5(out_file)
if checksums.get(out_file, None) == checksum:
continue
download_file(BASE_URL + '?' + query, out_file, phpsessid)
phpsessid = get_phpsessid()
verify_phpsessid(phpsessid)
download_all(phpsessid)
subjects = ['S1', 'S5', 'S6', 'S7', 'S8', 'S9', 'S11']
# https://stackoverflow.com/a/6718435
def commonprefix(m):
s1 = min(m)
s2 = max(m)
for i, c in enumerate(s1):
if c != s2[i]:
return s1[:i]
return s1
def extract_tgz(tgz_file, dest):
if path.exists(dest):
return
with tarfile.open(tgz_file, 'r:gz') as tar:
members = [m for m in tar.getmembers() if m.isreg()]
member_dirs = [path.dirname(m.name).split(path.sep) for m in members]
base_path = path.sep.join(commonprefix(member_dirs))
for m in members:
m.name = path.relpath(m.name, base_path)
tar.extractall(dest)
def extract_all():
for subject_id in tqdm(subjects, ascii=True):
out_dir = path.join('extracted', subject_id)
makedirs(out_dir, exist_ok=True)
extract_tgz('archives/Poses_D2_Positions_{}.tgz'.format(subject_id),
path.join(out_dir, 'Poses_D2_Positions'))
extract_tgz('archives/Poses_D3_Positions_{}.tgz'.format(subject_id),
path.join(out_dir, 'Poses_D3_Positions')),
extract_tgz('archives/Poses_D3_Positions_mono_{}.tgz'.format(subject_id),
path.join(out_dir, 'Poses_D3_Positions_mono')),
extract_tgz('archives/Poses_D3_Positions_mono_universal_{}.tgz'.format(subject_id),
path.join(out_dir, 'Poses_D3_Positions_mono_universal')),
extract_tgz('archives/Videos_{}.tgz'.format(subject_id),
path.join(out_dir, 'Videos'))
extract_all()
metadata = load_h36m_metadata()
# Subjects to include when preprocessing
included_subjects = {
'S1': 1,
'S5': 5,
'S6': 6,
'S7': 7,
'S8': 8,
'S9': 9,
'S11': 11,
}
# Rather than include every frame from every video, we can instead wait for the pose to change
# significantly before storing a new example.
def select_frame_indices_to_include(subject, poses_3d_univ):
# To process every single frame, uncomment the following line:
# return np.arange(0, len(poses_3d_univ))
# Take every 64th frame for the protocol #2 test subjects
# (see the "Compositional Human Pose Regression" paper)
if subject == 'S9' or subject == 'S11':
return np.arange(0, len(poses_3d_univ), 64)
# Take only frames where movement has occurred for the protocol #2 train subjects
frame_indices = []
prev_joints3d = None
threshold = 40 ** 2 # Skip frames until at least one joint has moved by 40mm
for i, joints3d in enumerate(poses_3d_univ):
if prev_joints3d is not None:
max_move = ((joints3d - prev_joints3d) ** 2).sum(axis=-1).max()
if max_move < threshold:
continue
prev_joints3d = joints3d
frame_indices.append(i)
return np.array(frame_indices)
def infer_camera_intrinsics(points2d, points3d):
"""Infer camera instrinsics from 2D<->3D point correspondences."""
pose2d = points2d.reshape(-1, 2)
pose3d = points3d.reshape(-1, 3)
x3d = np.stack([pose3d[:, 0], pose3d[:, 2]], axis=-1)
x2d = (pose2d[:, 0] * pose3d[:, 2])
alpha_x, x_0 = list(np.linalg.lstsq(x3d, x2d, rcond=-1)[0].flatten())
y3d = np.stack([pose3d[:, 1], pose3d[:, 2]], axis=-1)
y2d = (pose2d[:, 1] * pose3d[:, 2])
alpha_y, y_0 = list(np.linalg.lstsq(y3d, y2d, rcond=-1)[0].flatten())
return np.array([alpha_x, x_0, alpha_y, y_0])
def process_view(out_dir, subject, action, subaction, camera):
subj_dir = path.join('extracted', subject)
base_filename = metadata.get_base_filename(subject, action, subaction, camera)
# Load joint position annotations
with pycdf.CDF(path.join(subj_dir, 'Poses_D2_Positions', base_filename + '.cdf')) as cdf:
poses_2d = np.array(cdf['Pose'])
poses_2d = poses_2d.reshape(poses_2d.shape[1], 32, 2)
with pycdf.CDF(path.join(subj_dir, 'Poses_D3_Positions_mono_universal', base_filename + '.cdf')) as cdf:
poses_3d_univ = np.array(cdf['Pose'])
poses_3d_univ = poses_3d_univ.reshape(poses_3d_univ.shape[1], 32, 3)
with pycdf.CDF(path.join(subj_dir, 'Poses_D3_Positions_mono', base_filename + '.cdf')) as cdf:
poses_3d = np.array(cdf['Pose'])
poses_3d = poses_3d.reshape(poses_3d.shape[1], 32, 3)
# Infer camera intrinsics
camera_int = infer_camera_intrinsics(poses_2d, poses_3d)
camera_int_univ = infer_camera_intrinsics(poses_2d, poses_3d_univ)
frame_indices = select_frame_indices_to_include(subject, poses_3d_univ)
frames = frame_indices + 1
video_file = path.join(subj_dir, 'Videos', base_filename + '.mp4')
frames_dir = path.join(out_dir, 'imageSequence', camera)
makedirs(frames_dir, exist_ok=True)
# Check to see whether the frame images have already been extracted previously
existing_files = {f for f in listdir(frames_dir)}
frames_are_extracted = True
for i in frames:
filename = 'img_%06d.jpg' % i
if filename not in existing_files:
frames_are_extracted = False
break
if not frames_are_extracted:
with TemporaryDirectory() as tmp_dir:
# Use ffmpeg to extract frames into a temporary directory
call([
'ffmpeg',
'-nostats', '-loglevel', 'error',
'-i', video_file,
'-qscale:v', '3',
path.join(tmp_dir, 'img_%06d.jpg')
])
# Move included frame images into the output directory
for i in frames:
filename = 'img_%06d.jpg' % i
move(
path.join(tmp_dir, filename),
path.join(frames_dir, filename)
)
return {
'pose/2d': poses_2d[frame_indices],
'pose/3d-univ': poses_3d_univ[frame_indices],
'pose/3d': poses_3d[frame_indices],
'intrinsics/' + camera: camera_int,
'intrinsics-univ/' + camera: camera_int_univ,
'frame': frames,
'camera': np.full(frames.shape, int(camera)),
'subject': np.full(frames.shape, int(included_subjects[subject])),
'action': np.full(frames.shape, int(action)),
'subaction': np.full(frames.shape, int(subaction)),
}
def process_subaction(subject, action, subaction):
datasets = {}
out_dir = path.join('processed', subject, metadata.action_names[action] + '-' + subaction)
makedirs(out_dir, exist_ok=True)
for camera in tqdm(metadata.camera_ids, ascii=True, leave=False):
try:
annots = process_view(out_dir, subject, action, subaction, camera)
except:
tqdm.write('!!! Error processing sequence, skipping: ' + \
repr((subject, action, subaction, camera)))
tqdm.write(traceback.format_exc())
continue
for k, v in annots.items():
if k in datasets:
datasets[k].append(v)
else:
datasets[k] = [v]
if len(datasets) == 0:
return
datasets = {k: np.concatenate(v) for k, v in datasets.items()}
with h5py.File(path.join(out_dir, 'annot.h5'), 'w') as f:
for name, data in datasets.items():
f.create_dataset(name, data=data)
def process_all():
sequence_mappings = metadata.sequence_mappings
subactions = []
for subject in included_subjects.keys():
subactions += [
(subject, action, subaction)
for action, subaction in sequence_mappings[subject].keys()
if int(action) > 1 # Exclude '_ALL'
]
for subject, action, subaction in tqdm(subactions, ascii=True, leave=False):
process_subaction(subject, action, subaction)
process_all()
wget http://humaneva.is.tue.mpg.de/main/download?file=HumanEvaI_Data_CD1.tar
ls -al *
mv download?file=HumanEvaI_Data_CD1.tar HumanEvaI_Data_CD1.tar
tar -x -M --file=HumanEvaI_Data_CD1.tar
ls dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised
| path | name | size | modificationTime |
|---|---|---|---|
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter0.ckpt | 1_members_ensemble0_iter0.ckpt | 3.4199173e7 | 1.670238244e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter1.ckpt | 1_members_ensemble0_iter1.ckpt | 3.4199173e7 | 1.670238273e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter10.ckpt | 1_members_ensemble0_iter10.ckpt | 3.4199173e7 | 1.670238499e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter100.ckpt | 1_members_ensemble0_iter100.ckpt | 3.4199173e7 | 1.670241478e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter101.ckpt | 1_members_ensemble0_iter101.ckpt | 3.4199173e7 | 1.670241504e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter102.ckpt | 1_members_ensemble0_iter102.ckpt | 3.4199173e7 | 1.670241531e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter103.ckpt | 1_members_ensemble0_iter103.ckpt | 3.4199173e7 | 1.670241558e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter104.ckpt | 1_members_ensemble0_iter104.ckpt | 3.4199173e7 | 1.670241585e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter105.ckpt | 1_members_ensemble0_iter105.ckpt | 3.4199173e7 | 1.670241612e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter106.ckpt | 1_members_ensemble0_iter106.ckpt | 3.4199173e7 | 1.67024164e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter107.ckpt | 1_members_ensemble0_iter107.ckpt | 3.4199173e7 | 1.670241667e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter108.ckpt | 1_members_ensemble0_iter108.ckpt | 3.4199173e7 | 1.670241694e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter109.ckpt | 1_members_ensemble0_iter109.ckpt | 3.4199173e7 | 1.670241721e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter11.ckpt | 1_members_ensemble0_iter11.ckpt | 3.4199173e7 | 1.670238879e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter110.ckpt | 1_members_ensemble0_iter110.ckpt | 3.4199173e7 | 1.670241748e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter111.ckpt | 1_members_ensemble0_iter111.ckpt | 3.4199173e7 | 1.670241775e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter112.ckpt | 1_members_ensemble0_iter112.ckpt | 3.4199173e7 | 1.670241802e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter113.ckpt | 1_members_ensemble0_iter113.ckpt | 3.4199173e7 | 1.670241829e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter114.ckpt | 1_members_ensemble0_iter114.ckpt | 3.4199173e7 | 1.670241856e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter115.ckpt | 1_members_ensemble0_iter115.ckpt | 3.4199173e7 | 1.670241883e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter116.ckpt | 1_members_ensemble0_iter116.ckpt | 3.4199173e7 | 1.670241909e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter117.ckpt | 1_members_ensemble0_iter117.ckpt | 3.4199173e7 | 1.670241936e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter118.ckpt | 1_members_ensemble0_iter118.ckpt | 3.4199173e7 | 1.670241963e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter119.ckpt | 1_members_ensemble0_iter119.ckpt | 3.4199173e7 | 1.670241991e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter12.ckpt | 1_members_ensemble0_iter12.ckpt | 3.4199173e7 | 1.670238915e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter120.ckpt | 1_members_ensemble0_iter120.ckpt | 3.4199173e7 | 1.670242018e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter121.ckpt | 1_members_ensemble0_iter121.ckpt | 3.4199173e7 | 1.670242045e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter122.ckpt | 1_members_ensemble0_iter122.ckpt | 3.4199173e7 | 1.670242072e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter123.ckpt | 1_members_ensemble0_iter123.ckpt | 3.4199173e7 | 1.670242099e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter124.ckpt | 1_members_ensemble0_iter124.ckpt | 3.4199173e7 | 1.670242126e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter125.ckpt | 1_members_ensemble0_iter125.ckpt | 3.4199173e7 | 1.670242153e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter126.ckpt | 1_members_ensemble0_iter126.ckpt | 3.4199173e7 | 1.67024218e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter127.ckpt | 1_members_ensemble0_iter127.ckpt | 3.4199173e7 | 1.670242207e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter128.ckpt | 1_members_ensemble0_iter128.ckpt | 3.4199173e7 | 1.670242234e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter129.ckpt | 1_members_ensemble0_iter129.ckpt | 3.4199173e7 | 1.670242261e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter13.ckpt | 1_members_ensemble0_iter13.ckpt | 3.4199173e7 | 1.670238956e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter130.ckpt | 1_members_ensemble0_iter130.ckpt | 3.4199173e7 | 1.670242288e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter131.ckpt | 1_members_ensemble0_iter131.ckpt | 3.4199173e7 | 1.670242315e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter132.ckpt | 1_members_ensemble0_iter132.ckpt | 3.4199173e7 | 1.670242342e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter133.ckpt | 1_members_ensemble0_iter133.ckpt | 3.4199173e7 | 1.670242369e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter134.ckpt | 1_members_ensemble0_iter134.ckpt | 3.4199173e7 | 1.670242395e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter135.ckpt | 1_members_ensemble0_iter135.ckpt | 3.4199173e7 | 1.670242423e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter136.ckpt | 1_members_ensemble0_iter136.ckpt | 3.4199173e7 | 1.670242449e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter137.ckpt | 1_members_ensemble0_iter137.ckpt | 3.4199173e7 | 1.670242476e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter138.ckpt | 1_members_ensemble0_iter138.ckpt | 3.4199173e7 | 1.670242503e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter139.ckpt | 1_members_ensemble0_iter139.ckpt | 3.4199173e7 | 1.67024253e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter14.ckpt | 1_members_ensemble0_iter14.ckpt | 3.4199173e7 | 1.670238997e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter140.ckpt | 1_members_ensemble0_iter140.ckpt | 3.4199173e7 | 1.670242557e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter141.ckpt | 1_members_ensemble0_iter141.ckpt | 3.4199173e7 | 1.670242584e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter142.ckpt | 1_members_ensemble0_iter142.ckpt | 3.4199173e7 | 1.670242608e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter143.ckpt | 1_members_ensemble0_iter143.ckpt | 3.4199173e7 | 1.670242632e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter144.ckpt | 1_members_ensemble0_iter144.ckpt | 3.4199173e7 | 1.670242656e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter145.ckpt | 1_members_ensemble0_iter145.ckpt | 3.4199173e7 | 1.67024268e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter146.ckpt | 1_members_ensemble0_iter146.ckpt | 3.4199173e7 | 1.670242705e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter147.ckpt | 1_members_ensemble0_iter147.ckpt | 3.4199173e7 | 1.67024273e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter148.ckpt | 1_members_ensemble0_iter148.ckpt | 3.4199173e7 | 1.670242756e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter149.ckpt | 1_members_ensemble0_iter149.ckpt | 3.4199173e7 | 1.670242783e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter15.ckpt | 1_members_ensemble0_iter15.ckpt | 3.4199173e7 | 1.670239038e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter150.ckpt | 1_members_ensemble0_iter150.ckpt | 3.4199173e7 | 1.670242811e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter151.ckpt | 1_members_ensemble0_iter151.ckpt | 3.4199173e7 | 1.670242838e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter152.ckpt | 1_members_ensemble0_iter152.ckpt | 3.4199173e7 | 1.670242865e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter153.ckpt | 1_members_ensemble0_iter153.ckpt | 3.4199173e7 | 1.67024289e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter154.ckpt | 1_members_ensemble0_iter154.ckpt | 3.4199173e7 | 1.670242915e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter155.ckpt | 1_members_ensemble0_iter155.ckpt | 3.4199173e7 | 1.670242939e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter156.ckpt | 1_members_ensemble0_iter156.ckpt | 3.4199173e7 | 1.670242963e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter157.ckpt | 1_members_ensemble0_iter157.ckpt | 3.4199173e7 | 1.670242988e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter158.ckpt | 1_members_ensemble0_iter158.ckpt | 3.4199173e7 | 1.670243012e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter159.ckpt | 1_members_ensemble0_iter159.ckpt | 3.4199173e7 | 1.670243045e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter16.ckpt | 1_members_ensemble0_iter16.ckpt | 3.4199173e7 | 1.670239079e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter160.ckpt | 1_members_ensemble0_iter160.ckpt | 3.4199173e7 | 1.670243085e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter161.ckpt | 1_members_ensemble0_iter161.ckpt | 3.4199173e7 | 1.670243125e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter162.ckpt | 1_members_ensemble0_iter162.ckpt | 3.4199173e7 | 1.670243166e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter163.ckpt | 1_members_ensemble0_iter163.ckpt | 3.4199173e7 | 1.670243206e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter164.ckpt | 1_members_ensemble0_iter164.ckpt | 3.4199173e7 | 1.670243246e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter165.ckpt | 1_members_ensemble0_iter165.ckpt | 3.4199173e7 | 1.670243287e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter166.ckpt | 1_members_ensemble0_iter166.ckpt | 3.4199173e7 | 1.670243327e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter167.ckpt | 1_members_ensemble0_iter167.ckpt | 3.4199173e7 | 1.670243368e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter168.ckpt | 1_members_ensemble0_iter168.ckpt | 3.4199173e7 | 1.670243408e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter169.ckpt | 1_members_ensemble0_iter169.ckpt | 3.4199173e7 | 1.670243448e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter17.ckpt | 1_members_ensemble0_iter17.ckpt | 3.4199173e7 | 1.670239119e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter170.ckpt | 1_members_ensemble0_iter170.ckpt | 3.4199173e7 | 1.670243488e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter171.ckpt | 1_members_ensemble0_iter171.ckpt | 3.4199173e7 | 1.670243529e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter172.ckpt | 1_members_ensemble0_iter172.ckpt | 3.4199173e7 | 1.670243569e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter173.ckpt | 1_members_ensemble0_iter173.ckpt | 3.4199173e7 | 1.670243609e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter174.ckpt | 1_members_ensemble0_iter174.ckpt | 3.4199173e7 | 1.67024365e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter175.ckpt | 1_members_ensemble0_iter175.ckpt | 3.4199173e7 | 1.670243691e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter176.ckpt | 1_members_ensemble0_iter176.ckpt | 3.4199173e7 | 1.670243731e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter177.ckpt | 1_members_ensemble0_iter177.ckpt | 3.4199173e7 | 1.670243772e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter178.ckpt | 1_members_ensemble0_iter178.ckpt | 3.4199173e7 | 1.670243812e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter179.ckpt | 1_members_ensemble0_iter179.ckpt | 3.4199173e7 | 1.670243853e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter18.ckpt | 1_members_ensemble0_iter18.ckpt | 3.4199173e7 | 1.670239159e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter180.ckpt | 1_members_ensemble0_iter180.ckpt | 3.4199173e7 | 1.670243893e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter181.ckpt | 1_members_ensemble0_iter181.ckpt | 3.4199173e7 | 1.670243934e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter182.ckpt | 1_members_ensemble0_iter182.ckpt | 3.4199173e7 | 1.670243974e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter183.ckpt | 1_members_ensemble0_iter183.ckpt | 3.4199173e7 | 1.670244016e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter184.ckpt | 1_members_ensemble0_iter184.ckpt | 3.4199173e7 | 1.670244057e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter185.ckpt | 1_members_ensemble0_iter185.ckpt | 3.4199173e7 | 1.670244097e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter186.ckpt | 1_members_ensemble0_iter186.ckpt | 3.4199173e7 | 1.670244137e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter187.ckpt | 1_members_ensemble0_iter187.ckpt | 3.4199173e7 | 1.670244178e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter188.ckpt | 1_members_ensemble0_iter188.ckpt | 3.4199173e7 | 1.670244218e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter189.ckpt | 1_members_ensemble0_iter189.ckpt | 3.4199173e7 | 1.670244258e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter19.ckpt | 1_members_ensemble0_iter19.ckpt | 3.4199173e7 | 1.6702392e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter190.ckpt | 1_members_ensemble0_iter190.ckpt | 3.4199173e7 | 1.670244298e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter191.ckpt | 1_members_ensemble0_iter191.ckpt | 3.4199173e7 | 1.670244339e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter192.ckpt | 1_members_ensemble0_iter192.ckpt | 3.4199173e7 | 1.67024438e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter193.ckpt | 1_members_ensemble0_iter193.ckpt | 3.4199173e7 | 1.670244421e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter194.ckpt | 1_members_ensemble0_iter194.ckpt | 3.4199173e7 | 1.670244461e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter195.ckpt | 1_members_ensemble0_iter195.ckpt | 3.4199173e7 | 1.670244501e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter196.ckpt | 1_members_ensemble0_iter196.ckpt | 3.4199173e7 | 1.670244541e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter197.ckpt | 1_members_ensemble0_iter197.ckpt | 3.4199173e7 | 1.670244581e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter198.ckpt | 1_members_ensemble0_iter198.ckpt | 3.4199173e7 | 1.670244622e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter199.ckpt | 1_members_ensemble0_iter199.ckpt | 3.4199173e7 | 1.670244662e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter2.ckpt | 1_members_ensemble0_iter2.ckpt | 3.4199173e7 | 1.670238298e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter20.ckpt | 1_members_ensemble0_iter20.ckpt | 3.4199173e7 | 1.670239226e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter200.ckpt | 1_members_ensemble0_iter200.ckpt | 3.4199173e7 | 1.670244702e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter201.ckpt | 1_members_ensemble0_iter201.ckpt | 3.4199173e7 | 1.670244742e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter202.ckpt | 1_members_ensemble0_iter202.ckpt | 3.4199173e7 | 1.670244782e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter203.ckpt | 1_members_ensemble0_iter203.ckpt | 3.4199173e7 | 1.670244823e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter204.ckpt | 1_members_ensemble0_iter204.ckpt | 3.4199173e7 | 1.670244863e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter205.ckpt | 1_members_ensemble0_iter205.ckpt | 3.4199173e7 | 1.670244903e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter21.ckpt | 1_members_ensemble0_iter21.ckpt | 3.4199173e7 | 1.670239266e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter22.ckpt | 1_members_ensemble0_iter22.ckpt | 3.4199173e7 | 1.670239307e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter23.ckpt | 1_members_ensemble0_iter23.ckpt | 3.4199173e7 | 1.670239347e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter24.ckpt | 1_members_ensemble0_iter24.ckpt | 3.4199173e7 | 1.670239387e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter25.ckpt | 1_members_ensemble0_iter25.ckpt | 3.4199173e7 | 1.670239428e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter26.ckpt | 1_members_ensemble0_iter26.ckpt | 3.4199173e7 | 1.670239468e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter27.ckpt | 1_members_ensemble0_iter27.ckpt | 3.4199173e7 | 1.670239508e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter28.ckpt | 1_members_ensemble0_iter28.ckpt | 3.4199173e7 | 1.670239556e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter29.ckpt | 1_members_ensemble0_iter29.ckpt | 3.4199173e7 | 1.670239595e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter3.ckpt | 1_members_ensemble0_iter3.ckpt | 3.4199173e7 | 1.670238323e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter30.ckpt | 1_members_ensemble0_iter30.ckpt | 3.4199173e7 | 1.670239635e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter31.ckpt | 1_members_ensemble0_iter31.ckpt | 3.4199173e7 | 1.670239675e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter32.ckpt | 1_members_ensemble0_iter32.ckpt | 3.4199173e7 | 1.670239715e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter33.ckpt | 1_members_ensemble0_iter33.ckpt | 3.4199173e7 | 1.670239755e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter34.ckpt | 1_members_ensemble0_iter34.ckpt | 3.4199173e7 | 1.670239796e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter35.ckpt | 1_members_ensemble0_iter35.ckpt | 3.4199173e7 | 1.670239824e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter36.ckpt | 1_members_ensemble0_iter36.ckpt | 3.4199173e7 | 1.670239849e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter37.ckpt | 1_members_ensemble0_iter37.ckpt | 3.4199173e7 | 1.670239873e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter38.ckpt | 1_members_ensemble0_iter38.ckpt | 3.4199173e7 | 1.670239898e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter39.ckpt | 1_members_ensemble0_iter39.ckpt | 3.4199173e7 | 1.670239925e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter4.ckpt | 1_members_ensemble0_iter4.ckpt | 3.4199173e7 | 1.670238348e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter40.ckpt | 1_members_ensemble0_iter40.ckpt | 3.4199173e7 | 1.670239952e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter41.ckpt | 1_members_ensemble0_iter41.ckpt | 3.4199173e7 | 1.670239979e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter42.ckpt | 1_members_ensemble0_iter42.ckpt | 3.4199173e7 | 1.670240006e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter43.ckpt | 1_members_ensemble0_iter43.ckpt | 3.4199173e7 | 1.670240033e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter44.ckpt | 1_members_ensemble0_iter44.ckpt | 3.4199173e7 | 1.67024006e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter45.ckpt | 1_members_ensemble0_iter45.ckpt | 3.4199173e7 | 1.670240087e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter46.ckpt | 1_members_ensemble0_iter46.ckpt | 3.4199173e7 | 1.670240114e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter47.ckpt | 1_members_ensemble0_iter47.ckpt | 3.4199173e7 | 1.670240141e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter48.ckpt | 1_members_ensemble0_iter48.ckpt | 3.4199173e7 | 1.670240168e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter49.ckpt | 1_members_ensemble0_iter49.ckpt | 3.4199173e7 | 1.670240195e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter5.ckpt | 1_members_ensemble0_iter5.ckpt | 3.4199173e7 | 1.670238374e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter50.ckpt | 1_members_ensemble0_iter50.ckpt | 3.4199173e7 | 1.670240222e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter51.ckpt | 1_members_ensemble0_iter51.ckpt | 3.4199173e7 | 1.670240249e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter52.ckpt | 1_members_ensemble0_iter52.ckpt | 3.4199173e7 | 1.670240273e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter53.ckpt | 1_members_ensemble0_iter53.ckpt | 3.4199173e7 | 1.670240297e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter54.ckpt | 1_members_ensemble0_iter54.ckpt | 3.4199173e7 | 1.670240322e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter55.ckpt | 1_members_ensemble0_iter55.ckpt | 3.4199173e7 | 1.670240346e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter56.ckpt | 1_members_ensemble0_iter56.ckpt | 3.4199173e7 | 1.670240371e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter57.ckpt | 1_members_ensemble0_iter57.ckpt | 3.4199173e7 | 1.670240395e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter58.ckpt | 1_members_ensemble0_iter58.ckpt | 3.4199173e7 | 1.670240419e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter59.ckpt | 1_members_ensemble0_iter59.ckpt | 3.4199173e7 | 1.670240444e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter6.ckpt | 1_members_ensemble0_iter6.ckpt | 3.4199173e7 | 1.670238399e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter60.ckpt | 1_members_ensemble0_iter60.ckpt | 3.4199173e7 | 1.670240468e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter61.ckpt | 1_members_ensemble0_iter61.ckpt | 3.4199173e7 | 1.670240492e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter62.ckpt | 1_members_ensemble0_iter62.ckpt | 3.4199173e7 | 1.670240517e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter63.ckpt | 1_members_ensemble0_iter63.ckpt | 3.4199173e7 | 1.670240542e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter64.ckpt | 1_members_ensemble0_iter64.ckpt | 3.4199173e7 | 1.670240566e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter65.ckpt | 1_members_ensemble0_iter65.ckpt | 3.4199173e7 | 1.670240591e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter66.ckpt | 1_members_ensemble0_iter66.ckpt | 3.4199173e7 | 1.670240615e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter67.ckpt | 1_members_ensemble0_iter67.ckpt | 3.4199173e7 | 1.670240639e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter68.ckpt | 1_members_ensemble0_iter68.ckpt | 3.4199173e7 | 1.670240664e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter69.ckpt | 1_members_ensemble0_iter69.ckpt | 3.4199173e7 | 1.670240688e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter7.ckpt | 1_members_ensemble0_iter7.ckpt | 3.4199173e7 | 1.670238424e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter70.ckpt | 1_members_ensemble0_iter70.ckpt | 3.4199173e7 | 1.670240713e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter71.ckpt | 1_members_ensemble0_iter71.ckpt | 3.4199173e7 | 1.670240737e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter72.ckpt | 1_members_ensemble0_iter72.ckpt | 3.4199173e7 | 1.670240763e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter73.ckpt | 1_members_ensemble0_iter73.ckpt | 3.4199173e7 | 1.670240787e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter74.ckpt | 1_members_ensemble0_iter74.ckpt | 3.4199173e7 | 1.670240811e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter75.ckpt | 1_members_ensemble0_iter75.ckpt | 3.4199173e7 | 1.670240835e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter76.ckpt | 1_members_ensemble0_iter76.ckpt | 3.4199173e7 | 1.670240859e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter77.ckpt | 1_members_ensemble0_iter77.ckpt | 3.4199173e7 | 1.670240884e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter78.ckpt | 1_members_ensemble0_iter78.ckpt | 3.4199173e7 | 1.670240908e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter79.ckpt | 1_members_ensemble0_iter79.ckpt | 3.4199173e7 | 1.670240932e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter8.ckpt | 1_members_ensemble0_iter8.ckpt | 3.4199173e7 | 1.670238449e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter80.ckpt | 1_members_ensemble0_iter80.ckpt | 3.4199173e7 | 1.670240957e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter81.ckpt | 1_members_ensemble0_iter81.ckpt | 3.4199173e7 | 1.670240982e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter82.ckpt | 1_members_ensemble0_iter82.ckpt | 3.4199173e7 | 1.670241008e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter83.ckpt | 1_members_ensemble0_iter83.ckpt | 3.4199173e7 | 1.670241032e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter84.ckpt | 1_members_ensemble0_iter84.ckpt | 3.4199173e7 | 1.670241057e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter85.ckpt | 1_members_ensemble0_iter85.ckpt | 3.4199173e7 | 1.670241082e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter86.ckpt | 1_members_ensemble0_iter86.ckpt | 3.4199173e7 | 1.670241109e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter87.ckpt | 1_members_ensemble0_iter87.ckpt | 3.4199173e7 | 1.670241136e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter88.ckpt | 1_members_ensemble0_iter88.ckpt | 3.4199173e7 | 1.670241163e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter89.ckpt | 1_members_ensemble0_iter89.ckpt | 3.4199173e7 | 1.67024119e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter9.ckpt | 1_members_ensemble0_iter9.ckpt | 3.4199173e7 | 1.670238474e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter90.ckpt | 1_members_ensemble0_iter90.ckpt | 3.4199173e7 | 1.670241216e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter91.ckpt | 1_members_ensemble0_iter91.ckpt | 3.4199173e7 | 1.670241243e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter92.ckpt | 1_members_ensemble0_iter92.ckpt | 3.4199173e7 | 1.67024127e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter93.ckpt | 1_members_ensemble0_iter93.ckpt | 3.4199173e7 | 1.670241297e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter94.ckpt | 1_members_ensemble0_iter94.ckpt | 3.4199173e7 | 1.670241323e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter95.ckpt | 1_members_ensemble0_iter95.ckpt | 3.4199173e7 | 1.670241349e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter96.ckpt | 1_members_ensemble0_iter96.ckpt | 3.4199173e7 | 1.670241376e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter97.ckpt | 1_members_ensemble0_iter97.ckpt | 3.4199173e7 | 1.670241403e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter98.ckpt | 1_members_ensemble0_iter98.ckpt | 3.4199173e7 | 1.670241429e12 |
| dbfs:/VideoPose3D/saved_models/humaneva/checkpoints/supervised/1_members_ensemble0_iter99.ckpt | 1_members_ensemble0_iter99.ckpt | 3.4199173e7 | 1.670241453e12 |
torch.load(model_state_dict, os.path.join(save_models_dir,f"{n_member}_members_ensemble{i_model}_iter{iter}.ckpt"))
Predicting the network load
Project members:
- Sofia Ek, Department of Information Technology, Uppsala University
- Oscar Stenhammar, Network and System Engineering, KTH and Ericsson
Background
Saving energy is on everybody's mind: * Energy crisis in Europe * Causing industries to cut down on energy consumption * Network operators are also affected
So, the question is how can network operators save energy with only minor impacts on end users? * One idea is to predict the network load * Based on this, cells can be put to sleep mode if the expected load is low
Dataset
The data is from a network vendor in Sweden and has been collected between 2022-09-01 and 2022-10-29 (one measurement every 15 minutes). It is real network data from an urban environment.
There are in total 308 different cells in the dataset and it contains information about the cell IDs, location of each cell, throughput volume on the downlink in each cell, and the number of active users in each cell. The cell ID and the location are anonymized, but the relative location to other cells is still valid. The throughput and the number of users is the total measure during these 15 minutes.
An example of the first 10 rows of data can be seen below.
"./01_prepare_data"
df = spark_read_data(True)
Relative location of the cells:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
sns.set(
context='paper',
font_scale=1,
style='ticks',
rc={
'lines.linewidth': 2,
'figure.figsize': (15,12),
'font.size': 60,
'lines.markersize': 8,
'axes.labelsize': 20,
'xtick.labelsize': 16,
'ytick.labelsize': 16,
'legend.fontsize': 16,
'axes.labelpad': 6
}
)
df_p = df.where(df.rdiTimeStamp == '2022-09-02 00:00:00').toPandas()
df_p.longitude = df_p.longitude - (df_p.longitude.min() + df_p.longitude.max())/2
df_p.latitude = 111*df_p.latitude + np.random.random(len(df_p))/20
df_p.longitude = 111*df_p.longitude + np.random.random(len(df_p))/20
fig, ax = plt.subplots()
sns.scatterplot(data=df_p, x='longitude', y='latitude', hue='locationIndex')
ax.set_xlabel('X-position [km]')
ax.set_ylabel('Y-position [km]')
plt.legend('',frameon=False)
plt.tight_layout()
plt.show()
For one of the cells in the dataset: A plot of the downlink volume (MB) and the number of users during 24 hours.
df_c = df.where(df.cellId == 1).toPandas()
df_d = df_c[df_c['rdiTimeStamp']<'2022-09-02']
df_d = df_d[df_d['rdiTimeStamp']>'2022-08-30']
df_d = df_d.sort_values(['rdiTimeStamp'])
df_d['pmRadioThpVolDl'] = df_d['pmRadioThpVolDl']/8000
df_d['rdiTimeStamp'] = pd.to_datetime(df_d.rdiTimeStamp)
fig, ax = plt.subplots()
sns.lineplot(ax = ax, data=df_d, x='rdiTimeStamp', y='pmRadioThpVolDl', label='Volume')
ax.set_xlabel('Time [mm-dd HH]')
ax.set_ylabel('Downlink volume [MB]')
ax.set_xticklabels(ax.get_xticklabels(), rotation=25)
ax.legend(loc="upper left")
ax1 = ax.twinx()
ax1.plot(df_d['rdiTimeStamp'], df_d['pmActiveUeDlSum'], color='black', label='Users')
ax1.legend(loc="upper right")
ax1.set_ylabel('Number of users')
plt.tight_layout()
plt.show()
Methods
We focus on three methods: 1. Autoregressiv model (AR-model) 2. Long short-term memory (LSTM) 3. Gated Recurrent Unit (GRU)
We try to predict the throughput volume on the downlink, i.e. the variable called pmRadioThpVolDl.
AR-model: This model is used as a baseline and the model is estimated with linear regression. In this case, we filter the data and only focus on one cell at the time. The model is:
\(y(t) = \beta_1 y(t - 1) + \beta_2 y(t - 2) + \beta_3 y(t - 3) + \beta_4 y(t - 96)\), where y is pmRadioThpVolDl.
LSTM and GRU: These neural network models uses all the data and creates a global model for prediction. The models are built with Keras/Tensorflow and the training is distributed using Horovod.
More details on our setup will come in the following notebooks.
References:
How to setup linear regression with pyspark: https://towardsdatascience.com/building-a-linear-regression-with-pyspark-and-mllib-d065c3ba246a
Tutoial Horovod and Tensorflow: https://learn.microsoft.com/en-us/azure/synapse-analytics/machine-learning/tutorial-horovod-tensorflow
Preparing the data
This notebook is for loading and pre-processing the dataset.
# Reads the data into a dataframe
def spark_read_data(display = False):
# File location and type
file_location = "/FileStore/tables/RDI/total_df0.csv"
file_type = "csv"
# CSV options
infer_schema = "true"
first_row_is_header = "true"
delimiter = ","
# The applied options are for CSV files. For other file types, these will be ignored.
df = spark.read.format(file_type) \
.option("inferSchema", infer_schema) \
.option("header", first_row_is_header) \
.option("sep", delimiter) \
.load(file_location)
df = df.drop("_c0")
if display:
df.show(10)
return df
# Returns a dataframe of the desired timelags for the dataset
def time_shift_data(df, lags, features):
from pyspark.sql import functions as F
from pyspark.sql.window import Window
my_window = Window.partitionBy('cellId').orderBy('rdiTimeStamp')
for i in lags:
for ii in features:
df = df.withColumn(ii+'(t-{})'.format(i), F.lag(ii,i).over(my_window))
df = df.na.drop()
return df
# Min-max scaler for the neural network models (LSTM) and (GRU)
def scale_data_manual(df, columns_to_scale):
mm_coeff = []
i = 0
for col in columns_to_scale:
k = min(df.select(col).collect())[0]
l = max(df.select(col).collect())[0]
df = df.withColumn(col,(df[col]-k)/(l-k))
mm_coeff.append([k, l])
i += 1
return df, mm_coeff
# Inverse min-max scaler for the neural network models (LSTM) and (GRU)
def scale_data_inverse_manual(df, mm_coeff, columns_to_scale = ['cellId', 'locationIndex','freqBand', 'latitude', 'longitude', 'pmRadioThpVolDl', 'pmActiveUeDlSum', 'pmActiveUeUlSum']):
#from pyspark.sql.functions import round
i = 0
for col in columns_to_scale:
df = df.withColumn(col, df[col]*(mm_coeff[i][1]-mm_coeff[i][0])+mm_coeff[i][0])
#if col in ['cellId', 'locationIndex','freqBand']:
# df = df.withColumn(col, round(df_test[col]))
i += 1
return df
# Normalizes a dataframe in the specified columns for the AR-model
def normalize(df, columns):
from pyspark.sql.functions import mean
aggExpr = []
for column in columns:
aggExpr.append(mean(df[column]).alias(column))
averages = df.agg(*aggExpr).collect()[0]
selectExpr = ['*']
for column in columns:
selectExpr.append((df[column] - averages[column]).alias('normalized_'+column))
return df.select(selectExpr), averages
# Splits train and test data in the dataset depending on the timestamp of the row (i.e. this is not a random split)
def train_test_split(df, n_train):
from pyspark.sql import functions as f
from pyspark.sql.window import Window
window=Window.orderBy('rdiTimeStamp')
df_temp=df.withColumn('row',f.row_number().over(window))
df_train = df_temp.filter((f.col('row')<=n_train))
df_test = df_temp.filter((f.col('row')>n_train))
df_train = df_train.drop('row')
df_test = df_test.drop('row')
return df_train, df_test
# Constructs the dataset for the distributed model
def get_dataset():
# ##### Setup ########
time_variables = ['pmRadioThpVolDl', 'pmActiveUeDlSum', 'pmActiveUeUlSum']
aux_variables = ['freqBand', 'latitude', 'longitude']
y_variable = 'pmRadioThpVolDl'
columns_to_scale = ['cellId', 'locationIndex','freqBand', 'latitude', 'longitude', 'pmRadioThpVolDl', 'pmActiveUeDlSum', 'pmActiveUeUlSum']
n_time = 3
lags = [x for x in range(1, n_time + 1)]
#lags += [4 * 24]
frac_train = 0.7
#############
df = spark_read_data()
#df_scaled, scalerModel, scalers = scale_data(df, columns_to_scale)
#df_scaled, mm_coeff = scale_data(df, columns_to_scale)
df_scaled, mm_coeff = scale_data_manual(df, columns_to_scale)
df_scaled = time_shift_data(df_scaled, lags, time_variables)
n_train = int(df.count() * frac_train)
df_train, df_test = train_test_split(df_scaled, n_train)
time_shifted_variables = ["{}(t-{})".format(x,t) for t in range(1,n_time+1) for x in time_variables]
x_train, x_aux_train, y_train = df_to_array(df_train, time_shifted_variables, aux_variables, y_variable, n_time)
x_test, x_aux_test, y_test = df_to_array(df_test, time_shifted_variables, aux_variables, y_variable, n_time)
# print(x_train.shape, x_aux_train.shape, y_train.shape)
# print(x_test.shape, x_aux_test.shape, y_test.shape)
return (x_train, x_aux_train, y_train), (x_test, x_aux_test, y_test), df_test, mm_coeff
# Constructs the dataset for the baseline model
def get_dataset_lr(cellId):
from pyspark.ml.feature import VectorAssembler
# ##### Setup ########
time_variable = ['pmRadioThpVolDl']
time_variable_norm = ['normalized_pmRadioThpVolDl']
n_time = 3
lags = [x for x in range(1, n_time + 1)]
lags += [4 * 24]
frac_train = 0.7
#############
df = spark_read_data()
df = df.where(df.cellId == cellId)
df_n, averages = normalize(df, time_variable)
df_shifted = time_shift_data(df_n, lags, time_variable_norm)
n_train = int(df_shifted.count() * frac_train)
df_train, df_test = train_test_split(df_shifted, n_train)
time_shifted_variables = ["{}(t-{})".format(x,t) for t in lags for x in time_variable_norm]
assembler = VectorAssembler(inputCols = time_shifted_variables, outputCol = 'features')
df_train_lr = assembler.transform(df_train)
df_train_lr = df_train_lr.select(['features', 'normalized_pmRadioThpVolDl', 'cellId','rdiTimeStamp'])
df_test_lr = assembler.transform(df_test)
df_test_lr = df_test_lr.select(['features', 'normalized_pmRadioThpVolDl', 'cellId','rdiTimeStamp'])
return df_train_lr, df_test_lr, averages
# Function for retrieving the Horovod rank and size
def get_dataset_rank(train_data, test_data, rank=0, size=1):
x_train, x_aux_train, y_train = train_data
x_test, x_aux_test, y_test = test_data
x_train = x_train[rank::size]
x_aux_train = x_aux_train[rank::size]
y_train = y_train[rank::size]
x_test = x_test[rank::size]
x_aux_test = x_aux_test[rank::size]
y_test = y_test[rank::size]
return (x_train, x_aux_train, y_train), (x_test, x_aux_test, y_test)
# Converts a dataframe of the dataset to a numpy array
def df_to_array(df, time_variables, aux_variables, y_variable, n_time=1):
import numpy as np
n_row = df.count()
x = np.array(df.select(time_variables).collect()).reshape(n_row, n_time, int(len(time_variables)/n_time))
x_aux = np.array(df.select(aux_variables).collect()).reshape(n_row, len(aux_variables))
y = np.array(df.select(y_variable).collect()).reshape(n_row)
x=x.astype(np.float32)
x_aux=x_aux.astype(np.float32)
y=y.astype(np.float32)
return (x, x_aux, y)
# Returns a dataset suitable for plotting
def get_test_dataset(df_test, cellId = -1):
# ##### Setup ########
time_variables = ['pmRadioThpVolDl', 'pmActiveUeDlSum', 'pmActiveUeUlSum']
aux_variables = ['freqBand', 'latitude', 'longitude']
y_variable = 'pmRadioThpVolDl'
n_time = 2
#############
if cellId > -1:
df_test = df_test.where(df_test.cellId == 1)
time_shifted_variables = ["{}(t-{})".format(x,t) for t in range(1,n_time+1) for x in time_variables]
x_test, x_aux_test, y_test = df_to_array(df_test, time_shifted_variables, aux_variables, y_variable, n_time)
return (x_test, x_aux_test, y_test)
Baseline model
Creating a baseline model for the machine learning model to compare with.
"./01_prepare_data"
We first train an AR-model using linear regression. This model is only for one of the cells in the network, in this case cellId = 1. We use 70% of the data for training and 30% for testing.
from pyspark.ml.regression import LinearRegression
cellId = 1
df_train_lr, df_test_lr, averages = get_dataset_lr(cellId = cellId)
lr = LinearRegression(featuresCol = 'features', labelCol='normalized_pmRadioThpVolDl', fitIntercept=False, maxIter=10, regParam=0.3, elasticNetParam=0.8)
lr_model = lr.fit(df_train_lr)
print("Coefficients: " + str(lr_model.coefficients))
# print("Intercept: " + str(lr_model.intercept))
trainingSummary = lr_model.summary
print("RMSE: %f" % trainingSummary.rootMeanSquaredError)
print("r2: %f" % trainingSummary.r2)
The model is now tested.
averages
from pyspark.ml.evaluation import RegressionEvaluator
df_test_lr_1 = df_test_lr.where(df_test_lr.cellId == cellId)
lr_predictions = lr_model.transform(df_test_lr_1)
lr_predictions.select("prediction","rdiTimeStamp","normalized_pmRadioThpVolDl","features").show(10)
lr_evaluator = RegressionEvaluator(predictionCol="prediction", \
labelCol="normalized_pmRadioThpVolDl",metricName="r2")
print("R2 on test data: %g" % lr_evaluator.evaluate(lr_predictions))
test_result = lr_model.evaluate(df_test_lr)
print("RMSE on test data: %g" % test_result.rootMeanSquaredError)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
df_plot = lr_predictions.select("prediction","rdiTimeStamp","normalized_pmRadioThpVolDl").toPandas()
df_plot = df_plot[df_plot['rdiTimeStamp']<'2022-10-15']
df_plot = df_plot[df_plot['rdiTimeStamp']>'2022-10-13']
df_plot = df_plot.sort_values(['rdiTimeStamp'])
df_plot['pmRadioThpVolDl'] = (df_plot['normalized_pmRadioThpVolDl'] + averages)/8000
df_plot['prediction'] = (df_plot['prediction'] + averages)/8000
df_plot['rdiTimeStamp'] = pd.to_datetime(df_plot.rdiTimeStamp)
fig, ax = plt.subplots()
ax.plot(df_plot['rdiTimeStamp'], df_plot['pmRadioThpVolDl'], label='True')
ax.plot(df_plot['rdiTimeStamp'], df_plot['prediction'], label='Prediction')
ax.set_xlabel('Time')
ax.set_ylabel('Downlink volume [MB]')
ax.set_xticks(df_plot['rdiTimeStamp'])
ax.set_xticklabels(df_plot['rdiTimeStamp'], rotation=45)
plt.tight_layout()
plt.show()
[df_plot.iloc[0*12]['rdiTimeStamp'], df_plot.iloc[1*12]['rdiTimeStamp'], df_plot.iloc[2*12]['rdiTimeStamp'], df_plot.iloc[3*12]['rdiTimeStamp'],df_plot.iloc[42]['rdiTimeStamp']]
Single machine
Runs the machine learning model on a single machine
./01_prepare_data
# Defining the model
def get_model(x_shape_1, x_shape_2, x_aux_shape_1):
from tensorflow.keras.layers import Dense, Input, concatenate, LSTM
from tensorflow.keras.models import Model
main_input = Input(shape=(x_shape_1, x_shape_2), name='main_input')
lstm_out = LSTM(50)(main_input)
aux_input = Input(shape=(x_aux_shape_1,), name='aux_input')
x = concatenate([lstm_out, aux_input])
x = Dense(64, activation='relu')(x)
x = Dense(32, activation='relu')(x)
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
model = Model(inputs=[main_input, aux_input], outputs=[main_output])
return model
#
def train(learning_rate=1.0, epochs=epochs):
from tensorflow import keras
(x_train, x_aux_train, y_train), (x_test, x_aux_test, y_test), df_test = get_dataset()
model = get_model(x_train.shape[1], x_train.shape[2], x_aux_train.shape[1])
# Specify the optimizer, using the learning rate input parameter of the function so that Horovod can adjust the learning rate during training
optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(optimizer=optimizer,
loss='mean_squared_error',
metrics=['accuracy'])
model.fit([x_train, x_aux_train], y_train,
batch_size=batch_size,
epochs=epochs,
verbose=2,
validation_data=([x_test, x_aux_test], y_test))
return model, df_test
batch_size = 128
epochs = 10
model, df_test = train(learning_rate=0.001, epochs)
(x_test, x_aux_test, y_test) = get_test_dataset(df_test)
loss, accuracy = model.evaluate([x_test, x_aux_test], y_test, batch_size=batch_size)
print("loss:", loss)
print("accuracy:", accuracy)
Distributed learning
Trains a machine learning model on a gpu cluster
./01_prepare_data
# Defines the LSTM model
def get_model_lstm(x_shape_1, x_shape_2, x_aux_shape_1):
from tensorflow.keras.layers import Dense, Input, concatenate, LSTM
from tensorflow.keras.models import Model
main_input = Input(shape=(x_shape_1, x_shape_2), name='main_input')
lstm_out = LSTM(50)(main_input)
aux_input = Input(shape=(x_aux_shape_1,), name='aux_input')
x = concatenate([lstm_out, aux_input])
x = Dense(64, activation='relu')(x)
x = Dense(32, activation='relu')(x)
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
model = Model(inputs=[main_input, aux_input], outputs=[main_output])
return model
# Defines the GRU model
def get_model_gru(x_shape_1, x_shape_2, x_aux_shape_1):
from tensorflow.keras.layers import Dense, Input, concatenate, GRU
from tensorflow.keras.models import Model
main_input = Input(shape=(x_shape_1, x_shape_2), name='main_input')
#gru_1 = GRU(50)(main_input)
#gru_2 = GRU(50)(gru_1)
gru_out = GRU(50)(main_input)
aux_input = Input(shape=(x_aux_shape_1,), name='aux_input')
x = concatenate([gru_out, aux_input])
x = Dense(64, activation='relu')(x)
x = Dense(32, activation='relu')(x)
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
model = Model(inputs=[main_input, aux_input], outputs=[main_output])
return model
# Defines the deep GRU model
def get_model_gru_deep(x_shape_1, x_shape_2, x_aux_shape_1):
from tensorflow.keras.layers import Dense, Input, concatenate, GRU
from tensorflow.keras.models import Model
main_input = Input(shape=(x_shape_1, x_shape_2), name='main_input')
gru_1 = GRU(60, return_sequences=True)(main_input)
gru_2 = GRU(60, return_sequences=True)(gru_1)
gru_out = GRU(60)(gru_2)
aux_input = Input(shape=(x_aux_shape_1,), name='aux_input')
x = concatenate([gru_out, aux_input])
x = Dense(60, activation='relu')(x)
x = Dense(30, activation='relu')(x)
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
model = Model(inputs=[main_input, aux_input], outputs=[main_output])
return model
import os
import time
# Remove any existing checkpoint files
#dbutils.fs.rm(("/ml/RDI/train"), recurse=True)
# Create directory
checkpoint_dir = '/dbfs/ml/RDI/train/{}/'.format(time.time())
os.makedirs(checkpoint_dir)
print(checkpoint_dir)
# Defining the training function using Horovod runner
def train_hvd(get_model, train_data, test_data, checkpoint_path, learning_rate=1.0):
# Import tensorflow modules to each worker
from tensorflow.keras import backend as K
from tensorflow.keras.models import Sequential
import tensorflow as tf
from tensorflow import keras
import horovod.tensorflow.keras as hvd
# Initialize Horovod
hvd.init()
# Pin GPU to be used to process local rank (one GPU per process)
# These steps are skipped on a CPU cluster
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
# Call the get_dataset function you created, this time with the Horovod rank and size
(x_train, x_aux_train, y_train), (x_test, x_aux_test, y_test) = get_dataset_rank(train_data, test_data, hvd.rank(), hvd.size())
model = get_model(x_train.shape[1], x_train.shape[2], x_aux_train.shape[1])
# Adjust learning rate based on number of GPUs
optimizer = keras.optimizers.Adam(learning_rate=learning_rate * hvd.size())
# Use the Horovod Distributed Optimizer
optimizer = hvd.DistributedOptimizer(optimizer)
model.compile(optimizer=optimizer,
loss='mean_squared_error',
metrics=['accuracy'])
# Create a callback to broadcast the initial variable states from rank 0 to all other processes.
# This is required to ensure consistent initialization of all workers when training is started with random weights or restored from a checkpoint.
callbacks = [
hvd.callbacks.BroadcastGlobalVariablesCallback(0),
]
# Save checkpoints only on worker 0 to prevent conflicts between workers
if hvd.rank() == 0:
callbacks.append(keras.callbacks.ModelCheckpoint(checkpoint_path, save_weights_only = True))
model.fit([x_train, x_aux_train], y_train,
batch_size=batch_size,
callbacks=callbacks,
epochs=epochs,
verbose=2,
validation_data=([x_test, x_aux_test], y_test))
# Setting training parameters
from sparkdl import HorovodRunner
batch_size = 128
epochs = 100
learning_rate = 0.0005
hr = HorovodRunner(np=2)
# Loads the dataset
train_data, test_data, df_test, mm_coeff = get_dataset()
# Run training for the LSTM model
checkpoint_path = checkpoint_dir + '/lstm_l3/checkpoint-{epoch}.ckpt'
hr.run(train_hvd, get_model= get_model_lstm, train_data = train_data, test_data = test_data, checkpoint_path=checkpoint_path, learning_rate=learning_rate)
# Run training for the GRU model
checkpoint_path = checkpoint_dir + '/gru_l3/checkpoint-{epoch}.ckpt'
hr.run(train_hvd, get_model=get_model_gru, train_data = train_data, test_data = test_data, checkpoint_path=checkpoint_path, learning_rate=learning_rate)
# Run training for the deep GRU model
checkpoint_path = checkpoint_dir + '/gru_deep_l3/checkpoint-{epoch}.ckpt'
hr.run(train_hvd, get_model=get_model_gru_deep, train_data = train_data, test_data = test_data, checkpoint_path=checkpoint_path, learning_rate=learning_rate)
import tensorflow as tf
(x_test, x_aux_test, y_test) = get_test_dataset(df_test)
import tensorflow.keras
import os
import tensorflow.keras
dir1 = '/dbfs/ml/RDI/train/'
checkpoint_dir = dir1 + os.listdir(dir1)[-2] + '/'
# Evaluate the LSTM model
hvd_model_lstm = get_model_lstm(x_test.shape[1], x_test.shape[2], x_aux_test.shape[1])
hvd_model_lstm = get_model_lstm(x_test.shape[1], x_test.shape[2], x_aux_test.shape[1])
hvd_model_lstm.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
loss='mean_squared_error',
metrics=['accuracy'])
hvd_model_lstm.load_weights(tf.train.latest_checkpoint(os.path.dirname(checkpoint_dir + 'lstm_l3/checkpoint-{epoch}.ckpt')))
loss_lstm, accuracy_lstm = hvd_model_lstm.evaluate([x_test, x_aux_test], y_test, batch_size=batch_size)
print("loaded model loss and accuracy:", loss_lstm, accuracy_lstm)
# Evaluate the GRU model
hvd_model_gru = get_model_gru(x_test.shape[1], x_test.shape[2], x_aux_test.shape[1])
hvd_model_gru.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
loss='mean_squared_error',
metrics=['accuracy'])
hvd_model_gru.load_weights(tf.train.latest_checkpoint(os.path.dirname(checkpoint_dir + '/gru_l3/checkpoint-{epoch}.ckpt')))
loss_gru, accuracy_gru = hvd_model_gru.evaluate([x_test, x_aux_test], y_test, batch_size=batch_size)
print("loaded model loss and accuracy:", loss_gru, accuracy_gru)
# Evaluate the deep GRU model
hvd_model_gru_deep = get_model_gru_deep(x_test.shape[1], x_test.shape[2], x_aux_test.shape[1])
hvd_model_gru_deep.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
loss='mean_squared_error',
metrics=['accuracy'])
hvd_model_gru_deep.load_weights(tf.train.latest_checkpoint(os.path.dirname(checkpoint_dir + '/gru_deep_l3/checkpoint-{epoch}.ckpt')))
loss_deep, accuracy_deep = hvd_model_gru_deep.evaluate([x_test, x_aux_test], y_test, batch_size=batch_size)
print("loaded model loss and accuracy:", loss_deep, accuracy_deep)
load_m = mm_coeff[-3][1]
import matplotlib.pyplot as plt
# Using the DL volume from yesterday
plt.bar(['LSTM', 'GRU', 'Deep_GRU'], [loss_lstm, loss_gru, loss_deep])
plt.show()
# 45 minutes back
plt.bar(['LSTM', 'GRU', 'Deep_GRU'], [load_m*loss_lstm, load_m*loss_gru, load_m*loss_deep])
plt.ylabel('MSE loss')
plt.show()
df_test = df_test.withColumn('cellId', df_test['cellId']*(mm_coeff[0][1]-mm_coeff[0][0])+mm_coeff[0][0])
(x_test_1, x_aux_test_1, y_test_1) = get_test_dataset(df_test, cellId=1)
yhatl = hvd_model_lstm.predict([x_test_1, x_aux_test_1])
yhatg = hvd_model_gru.predict([x_test_1, x_aux_test_1])
yhatg1 = hvd_model_gru_deep.predict([x_test_1, x_aux_test_1])
plt.plot(load_m*y_test_1, label='True')
plt.plot(load_m*yhatl, label='LSTM')
plt.plot(load_m*yhatg, label='GRU')
plt.plot(load_m*yhatg1, label='GRU1')
#plt.axis([min_plot, max_plot, -0.01, 0.15])
plt.xlim([0,100])
plt.legend()
plt.show()
plt.plot(test_data[0][:100, 0,0])
plt.plot(y_test_1, label='True')
plt.plot(yhatl, label='LSTM')
plt.plot(yhatg, label='GRU')
plt.plot(yhatg1, label='GRU1')
#plt.axis([min_plot, max_plot, -0.01, 0.15])
plt.legend()
plt.show()
Collaborative Filtering in Movie Recommender Systems
Project members:
- Jacob Lindbäck, KTH Royal Institute of Technology
- Rebecka Winqvist, KTH Royal Institute of Technology
- Robert Bereza, KTH Royal Institute of Technology
- Damianos Tranos, KTH Royal Institute of Technology
Introduction
A recommender system provides its users with personalized suggestions, based on their previous feedback and ratings. We encounter them frequently in our everyday lives: for example, in streaming services where they are used for movie and music recommendation, and in e-commerce where they are used for product recommendation.
Collaborative filtering is a widely used technique in recommender systems. The technique makes predictions/suggestions (filtering) based on preferences collected from many users (collaborative). In this project, we use a collaborative filtering method for a movie recommender system. Let's start with a simple example.
Assume that we have five users and six movies in our system. We can collect the user ratings in a matrix
in which the rows represent the users, and the columns represent the movies. We call this matrix the user-item interaction matrix or the ratings matrix, and we denote it by \(X\). This matrix actually contains many dependencies, which we will make use of when we predict "missing" ratings.
Example 1 Assume that we have the following ratings matrix
. In this case, User 3 has not rated Movie 4. Since all users appear to share the same preferences, it makes sense to guess that User 3 will also give Movie 4 a rating of 3.
Example 2 Assume the following ratings matrix
We want to guess/predict how User 3 will rate Movie 2. We see that User 1 and User 3 seem to have the same preferences. It is therefore safe to assume that User 3 will give Movie 2 a rating of 2.
Matrix Factorization
It is easy to see that as the number of users and items grows, the problem of finding these dependencies or patterns becomes untractable/cumbersome. One way of mitigating this is by introducing so called latent features to characterize the items (movies) being rated, and instead learn/estimate how prevalent these fearures are in each movie, and to what extent each user appreciates a certain feature. In a movie recommender system, features could be e.g. movie genres, movie plots, starring actors or producers.
As an example, let's look at the case where we have two features, denoted by F1 and F2. We can then construct two matrices that tells us how the users rate these features, and how prevalent they are in each movie. We call these matrices, or this information, user factors and item factors, respectively, and denote them by \(U\) and \(V\).
So, for example, if we want to predict/guess how User 1 would rate Movie 1 in this case, we simply take the dot product between the two vectors \[r_{1,1} = \left[ 3 \ 1 \right] \cdot \left[ 1 \ 1 \right]^\top = 4,\]
which we can interpret as how much the user's preferences align with the movie's features, i.e., how likely it is that the user will enjoy the movie. Using this logic, to compute the full ratings matrix, we then take the outer product of the matrices \(U\) and \(V\), i.e., \(X = UV^\top\). This is also visualized in the image below.
This method is known as Matrix Factorization, which is a class of collaborative filtering algorithms. To summarize, the method decomposes the ratings matrix into the product of two lower-dimensional matrices: one representing the user factors, and one representing the item factors. In this way, computing/predciting the full ratings matrix become more memory efficient and computationally cheape, since we now only need to learn/find the item and user factors.
Problem Formulation
Let us now formalize our problem. First, let \(x_{i,j}\) denote the rating user \(i\) has given item/movie \(j\). Further, let \(\Omega\) denote all observed user-item pairs (i.e., which movies have been rated by whom). The collected ratings are represented by the ratings matrix, \(X\).
Our goal/aim is to learn/predict the missing values of \(X\). Using matrix factorization, this corresponds to finding/learning the user and item factors, \(U\) and \(V\), that best describe/represent the collected ratings (the elements of \(X\). That is, we want to minimize the difference (or error) \[ X - UV^\top. \] Since we cannot compute an error/deviation for missing values/ratings, we introduce the masking operator \(P_\Omega\) defined by \[ {P_\Omega(X)}{i,j} = x{i,j} \text{ if } (i,j) \in \Omega, \text{\ \ \ } {P_\Omega(X)}_{i,j} = 0 \text{ otherwise }, \]
and re-formulate the problem as the optimization problem \[ \min_{U \in \mathbb{R}^{n\times k}, V \in \mathbb{R}^{m\times k}} \quad \frac{1}{2}\left\lVert P_\Omega(X - UV^\top) \right\rVert^2_F, \] where \(F\) denotes the Frobenius norm. To facilitate the training/learning, but also to mitigate the risk of overfitting, one typically introduces two regularization terms: \[ \min_{U \in \mathbb{R}^{n\times k}, V \in \mathbb{R}^{m\times k}} \quad \frac{1}{2}\left\lVert P_\Omega(X - UV^\top) \right\rVert^2_F + \frac{\lambda}{2n}\left\lVert U \right\rVert^2_F + \frac{\lambda}{2m}\left\lVert V \right\rVert^2_F.\]
Gradients
Let \[ \mathcal{l}(U,V) = \frac{1}{2} \] denote the loss. Then the gradients w.r.t. to the factor matrices are given by \[ \nabla_U \mathcal{l}(U,V) = -P_\Omega(X-UV^\top)V \] and \[ \nabla_V \mathcal{l}(U,V) = -P_\Omega^\top(X-UV^\top)U, \] respectively.
Optimization Algorithms: ALS vs. Gradient Descent/CoCaIn
The optimization problem is smooth, but not convex. It is, however, blockwise convex. When \(U\) is fixed, solving for \(V\) reduces to a least-squares problem. This is the main motivation behind the Alternatig Least Squares (ALS), which is the standard technique used for solving the problem.
Can Jacob maybe motivate why we want to use GD/CoCaIn instead?
Practical Implementation
We illustrate how the data is partitioned with an example. Assume we have collected the ratings matrix:
Assume further that we charactize the items (movies) by three factors: F1, F2 and F3. With six users, six movies and three factors, the userFactor matrix and itemFactor matrix will both be of size \(6\times 3\). See Figure below.
We want to find the optimal elements of these matrices. We do so by minimizing the loss function defined above.
Data Partitioning
To store the large factor matrices \(U\) and \(V\), they are divided up by their rows and distributed in memory across different partitions. To also reduce the communication needed to update \(U\) and \(V\), the rating matrix, which is fixed at all time, is stored in memory structures called OutBlocks and InBlocks. There are four such structures in total, two of each for both items and for users. The userInBlocks contain all the ratings each user needs for its update, partitioned in such a way that the ratings are stored on the same partition as the factor that needs them to update, thus removing the need for communicating the ratings during the optimization. The userOutBlocks simply contain information about (or adresses to) the itemFactors that are necessary to update each block of userFactors. This information is used to, at each iteration, ensure that every userFactor is only communicated exactly those item factors it needs to update, and no more. The corresponding approach is used for itemInBlocks and itemOutBlocks. Note that this way of storing the data stores in total two copies of the rating matrix (one in userInBlocks and one in itemInBlocks), but on the other hand removes the need to communicate any values of the rating matrix during the optimization.
Assume that we split each matrix computation over three partitions each. This is illustrated in the figure below. How the blocks are distributed across partitions can be random, or determined by some specification (i.e., users with even indices on partitions with even indices etc.)
The elements of the ratings matrix are then distributed over these six partitions such that all information necessary to compute a particular factor, \(u_{i,j}\) or \(v_{i,j}\), is stored in the same partition. For instance, to compute user factors \(u_{i,j}\) for \(i\in[1,2], j\in[1,3]\), we only need the ratings from users 1 and 2.
Finally, we also store...
Learning Recommendations — a Non-Convex Optimization Problem
\[ \min_{U \in \mathbb{R}^{n\times k}, V \in \mathbb{R}^{m\times k}} \quad \frac{1}{2}\left\lVert P_\Omega(X - UV^\top) \right\rVert^2_F + \frac{\lambda}{2n}\left\lVert U \right\rVert^2_F + \frac{\lambda}{2m}\left\lVert V \right\rVert^2_F.\]
Nice properties: - Although jointly, non-convex, it is blockwise convex! In fact, when one block is fixed, solving for the other block is just a least squared problem! - This is the main justification of the standard solver - ALS (Alternating Least Squared). It's a block-coordinate descent algorithm, with many proven theoretical guarantees (see e.g. https://arxiv.org/abs/1702.02505).
Potential drawbacks: - The alternating update is inherently sequential - i.e in order to update U, the working nodes must for the V-update to complete - Potentially difficult to extend. Do updates enjoy closed forms when say new regularizers are added? Can convergence be guaranteed?
Our idea: - Can gradient-based methods be used for Collaborative Filtering?
Outline (I will expand on these points later): * ~~Introduce the problem in an easy way (preferably using matrices and/or other nice pictures to show what matrix factorization is)~~ * ~~Provide mathematical formulation~~ * Briefly explain how we save/partition the data/ratings (don't go into too much detail, as that will probably be confusing) (use figures) * Birefly explain ALS/CoCaIn and the difference between them (why are we using CoCaIn/GD instead of ALS?) * Walk through code? (or do we include code snippets as we go perhaps? We can decide and change later) * Results?
Creating the Collaborative Filtering (ColFil) object
Almost all our code is used to define one large object. Since our code is based on the ALS-package, large parts of it are well optimized but therefore also quite difficult to read. Therefore, it is best to just run the cell below, and after it we will pick out some of the more relevant parts of the code and explain how they work. To read the following block, it is strongly recommended to use the DataBricks editor that Raaz recommended on Canvas, as it makes it possible to collapse functions and improves readability (it's very very simple to enable).
package org.apache.spark.ml.collaborative_filtering
import org.apache.spark.ml.recommendation._
import java.{util => ju}
import java.io.IOException
import java.util.Locale
import scala.collection.mutable
import scala.reflect.ClassTag
import scala.util.{Sorting, Try}
import scala.util.hashing.byteswap64
import com.google.common.collect.{Ordering => GuavaOrdering}
import org.apache.hadoop.fs.Path
import org.json4s.DefaultFormats
import org.json4s.JsonDSL._
import org.apache.spark.{Partitioner, SparkException}
import org.apache.spark.annotation.Since
import org.apache.spark.internal.Logging
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.linalg.BLAS
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util._
import org.apache.spark.mllib.linalg.CholeskyDecomposition
import org.apache.spark.mllib.optimization.NNLS
import org.apache.spark.rdd.{DeterministicLevel, RDD}
import org.apache.spark.sql.{DataFrame, Dataset}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.Utils
import org.apache.spark.util.collection.{OpenHashMap, OpenHashSet, SortDataFormat, Sorter}
import org.apache.spark.util.random.XORShiftRandom
import com.databricks.service.DBUtils
object ColFil extends DefaultParamsReadable[ALS] with Logging {
/**
* Rating class for better code readability.
*/
case class Rating[@specialized(Int, Long) ID](user: ID, item: ID, rating: Float)
override def load(path: String): ALS = super.load(path)
/**
* Implementation of Collaborative filtering algorithm. Similar to the implementation of the
* ALS algorithm from org.apache.spark.ml.recommendation, therefore the remainder of this comment
* is actually the same as for the train() function for the ALS object.
*
* This implementation of the ALS factorization algorithm partitions the two sets of factors among
* Spark workers so as to reduce network communication by only sending one copy of each factor
* vector to each Spark worker on each iteration, and only if needed. This is achieved by
* precomputing some information about the ratings matrix to determine which users require which
* item factors and vice versa. See the Scaladoc for `InBlock` for a detailed explanation of how
* the precomputation is done.
*
* In addition, since each iteration of calculating the factor matrices depends on the known
* ratings, which are spread across Spark partitions, a naive implementation would incur
* significant network communication overhead between Spark workers, as the ratings RDD would be
* repeatedly shuffled during each iteration. This implementation reduces that overhead by
* performing the shuffling operation up front, precomputing each partition's ratings dependencies
* and duplicating those values to the appropriate workers before starting iterations to solve for
* the factor matrices. See the Scaladoc for `OutBlock` for a detailed explanation of how the
* precomputation is done.
*
* Note that the term "rating block" is a bit of a misnomer, as the ratings are not partitioned by
* contiguous blocks from the ratings matrix but by a hash function on the rating's location in
* the matrix. If it helps you to visualize the partitions, it is easier to think of the term
* "block" as referring to a subset of an RDD containing the ratings rather than a contiguous
* submatrix of the ratings matrix.
*/
def train[ID: ClassTag](
ratings: RDD[Rating[ID]],
rank: Int = 10,
numUserBlocks: Int = 10,
numItemBlocks: Int = 10,
maxIter: Int = 10,
stepSize: Float = 0.1f,
regParam: Double = 0.1, // TODO: DELETE
implicitPrefs: Boolean = false, // TODO: DELETE
alpha: Double = 1.0, // TODO: DELETE
nonnegative: Boolean = false, // TODO: DELETE
intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK,
finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK,
checkpointInterval: Int = 10,
seed: Long = 0L)(
implicit ord: Ordering[ID]): (RDD[(ID, Array[Double])], RDD[(ID, Array[Double])]) = {
// ---------- The following block of code is identical to the ALS class ---------------
require(!ratings.isEmpty(), s"No ratings available from $ratings")
require(intermediateRDDStorageLevel != StorageLevel.NONE,
"Collaborative filtering is not designed to run without persisting intermediate RDDs.")
val sc = ratings.sparkContext
// Precompute the rating dependencies of each partition
val userPart = new ALSPartitioner(numUserBlocks)
val itemPart = new ALSPartitioner(numItemBlocks)
val blockRatings = partitionRatings(ratings, userPart, itemPart)
.persist(intermediateRDDStorageLevel)
val (userInBlocks, userOutBlocks) =
makeBlocks("user", blockRatings, userPart, itemPart, intermediateRDDStorageLevel)
userOutBlocks.count() // materialize blockRatings and user blocks
val swappedBlockRatings = blockRatings.map {
case ((userBlockId, itemBlockId), RatingBlock(userIds, itemIds, localRatings)) =>
((itemBlockId, userBlockId), RatingBlock(itemIds, userIds, localRatings))
}
val (itemInBlocks, itemOutBlocks) =
makeBlocks("item", swappedBlockRatings, itemPart, userPart, intermediateRDDStorageLevel)
itemOutBlocks.count() // materialize item blocks
// Encoders for storing each user/item's partition ID and index within its partition using a
// single integer; used as an optimization
val userLocalIndexEncoder = new LocalIndexEncoder(userPart.numPartitions)
val itemLocalIndexEncoder = new LocalIndexEncoder(itemPart.numPartitions)
// These are the user and item factor matrices that, once trained, are multiplied together to
// estimate the rating matrix. The two matrices are stored in RDDs, partitioned by column such
// that each factor column resides on the same Spark worker as its corresponding user or item.
val seedGen = new XORShiftRandom(seed)
var userFactors = initialize(userInBlocks, rank, seedGen.nextLong())
var itemFactors = initialize(itemInBlocks, rank, seedGen.nextLong())
// val solver = if (nonnegative) new NNLSSolver else new CholeskySolver // DELETE
var previousCheckpointFile: Option[String] = None
var previousUserCheckpointFile: Option[String] = None
val shouldCheckpoint: Int => Boolean = (iter) =>
sc.checkpointDir.isDefined && checkpointInterval != -1 && (iter % checkpointInterval == 0)
val deletePreviousCheckpointFile: () => Unit = () =>
previousCheckpointFile.foreach { file =>
try {
val checkpointFile = new Path(file)
checkpointFile.getFileSystem(sc.hadoopConfiguration).delete(checkpointFile, true)
} catch {
case e: IOException =>
logWarning(s"Cannot delete checkpoint file $file:", e)
}
}
// --------------- This code is different from the ALS class, in part written by us ---------------
var previousCachedItemFactors: Option[RDD[(Int, FactorBlock)]] = None
var previousCachedUserFactors: Option[RDD[(Int, FactorBlock)]] = None
for (iter <- 0 until maxIter) {
val factorTuple = computeFactors(userFactors, itemFactors, userOutBlocks, itemOutBlocks,
userInBlocks, itemInBlocks, rank, regParam, userLocalIndexEncoder, itemLocalIndexEncoder, stepSize)
userFactors = factorTuple._1
itemFactors = factorTuple._2
// This doesn't actually work properly in our case. We have to checkpoint both itemFactors and userFactors
// since we don't alternate, but we haven't managed to figure out how to set up the checkpointing for that case
if (shouldCheckpoint(iter)) {
itemFactors.setName(s"itemFactors-$iter").persist(intermediateRDDStorageLevel)
itemFactors.checkpoint()
itemFactors.count() // checkpoint item factors and cut lineage
itemFactors.cleanShuffleDependencies()
deletePreviousCheckpointFile()
previousCachedItemFactors.foreach(_.unpersist())
previousCheckpointFile = itemFactors.getCheckpointFile
previousCachedItemFactors = Option(itemFactors)
}
}
val userIdAndFactors = userInBlocks
.mapValues(_.srcIds)
.join(userFactors)
.mapPartitions({ items =>
items.flatMap { case (_, (ids, factors)) =>
ids.iterator.zip(factors.iterator)
}
// Preserve the partitioning because IDs are consistent with the partitioners in userInBlocks
// and userFactors.
}, preservesPartitioning = true)
.setName("userFactors")
.persist(finalRDDStorageLevel)
val itemIdAndFactors = itemInBlocks
.mapValues(_.srcIds)
.join(itemFactors)
.mapPartitions({ items =>
items.flatMap { case (_, (ids, factors)) =>
ids.iterator.zip(factors.iterator)
}
}, preservesPartitioning = true)
.setName("itemFactors")
.persist(finalRDDStorageLevel)
if (finalRDDStorageLevel != StorageLevel.NONE) {
userIdAndFactors.count()
userInBlocks.unpersist()
userOutBlocks.unpersist()
itemOutBlocks.unpersist()
blockRatings.unpersist()
itemIdAndFactors.count()
itemFactors.unpersist()
itemInBlocks.unpersist()
}
// ------------------------- A more modern version of the ALS interface is available in the "mllib" package,
// in contrast to the ALS train()-function which resides in the ALS object that is part of the "ml" pacakge.
// This block of code here simply transforms the output of our train()-function so that it works the same way
// as the ALS train()-function from the "mllib" package -----------------------------------------------------
val mllibUserFactors = userIdAndFactors
.mapValues(_.map(_.toDouble))
.setName("users")
.persist(finalRDDStorageLevel)
val mllibItemFactors = itemIdAndFactors
.mapValues(_.map(_.toDouble))
.setName("products")
.persist(finalRDDStorageLevel)
if (finalRDDStorageLevel != StorageLevel.NONE) {
mllibUserFactors.count()
mllibItemFactors.count()
}
(mllibUserFactors, mllibItemFactors)
}
/**
* Factor block that stores factors (Array[Float]) in an Array.
*/
type FactorBlock = Array[Array[Float]]
/**
* A mapping of the columns of the items factor matrix that are needed when calculating each row
* of the users factor matrix, and vice versa.
*
* Specifically, when calculating a user factor vector, since only those columns of the items
* factor matrix that correspond to the items that that user has rated are needed, we can avoid
* having to repeatedly copy the entire items factor matrix to each worker later in the algorithm
* by precomputing these dependencies for all users, storing them in an RDD of `OutBlock`s. The
* items' dependencies on the columns of the users factor matrix is computed similarly.
*
* =Example=
*
* Using the example provided in the `InBlock` Scaladoc, `userOutBlocks` would look like the
* following:
*
* {{{
* userOutBlocks.collect() == Seq(
* 0 -> Array(Array(0, 1), Array(0, 1)),
* 1 -> Array(Array(0), Array(0))
* )
* }}}
*
* Each value in this map-like sequence is of type `Array[Array[Int]]`. The values in the
* inner array are the ranks of the sorted user IDs in that partition; so in the example above,
* `Array(0, 1)` in partition 0 refers to user IDs 0 and 6, since when all unique user IDs in
* partition 0 are sorted, 0 is the first ID and 6 is the second. The position of each inner
* array in its enclosing outer array denotes the partition number to which item IDs map; in the
* example, the first `Array(0, 1)` is in position 0 of its outer array, denoting item IDs that
* map to partition 0.
*
* In summary, the data structure encodes the following information:
*
* * There are ratings with user IDs 0 and 6 (encoded in `Array(0, 1)`, where 0 and 1 are the
* indices of the user IDs 0 and 6 on partition 0) whose item IDs map to partitions 0 and 1
* (represented by the fact that `Array(0, 1)` appears in both the 0th and 1st positions).
*
* * There are ratings with user ID 3 (encoded in `Array(0)`, where 0 is the index of the user
* ID 3 on partition 1) whose item IDs map to partitions 0 and 1 (represented by the fact that
* `Array(0)` appears in both the 0th and 1st positions).
*/
type OutBlock = Array[Array[Int]]
/**
* In-link block for computing user and item factor matrices.
*
* The ALS algorithm partitions the columns of the users factor matrix evenly among Spark workers.
* Since each column of the factor matrix is calculated using the known ratings of the correspond-
* ing user, and since the ratings don't change across iterations, the ALS algorithm preshuffles
* the ratings to the appropriate partitions, storing them in `InBlock` objects.
*
* The ratings shuffled by item ID are computed similarly and also stored in `InBlock` objects.
* Note that this means every rating is stored twice, once as shuffled by user ID and once by item
* ID. This is a necessary tradeoff, since in general a rating will not be on the same worker
* when partitioned by user as by item.
*
* =Example=
*
* Say we have a small collection of eight items to offer the seven users in our application. We
* have some known ratings given by the users, as seen in the matrix below:
*
* {{{
* Items
* 0 1 2 3 4 5 6 7
* +---+---+---+---+---+---+---+---+
* 0 | |0.1| | |0.4| | |0.7|
* +---+---+---+---+---+---+---+---+
* 1 | | | | | | | | |
* +---+---+---+---+---+---+---+---+
* U 2 | | | | | | | | |
* s +---+---+---+---+---+---+---+---+
* e 3 | |3.1| | |3.4| | |3.7|
* r +---+---+---+---+---+---+---+---+
* s 4 | | | | | | | | |
* +---+---+---+---+---+---+---+---+
* 5 | | | | | | | | |
* +---+---+---+---+---+---+---+---+
* 6 | |6.1| | |6.4| | |6.7|
* +---+---+---+---+---+---+---+---+
* }}}
*
* The ratings are represented as an RDD, passed to the `partitionRatings` method as the `ratings`
* parameter:
*
* {{{
* ratings.collect() == Seq(
* Rating(0, 1, 0.1f),
* Rating(0, 4, 0.4f),
* Rating(0, 7, 0.7f),
* Rating(3, 1, 3.1f),
* Rating(3, 4, 3.4f),
* Rating(3, 7, 3.7f),
* Rating(6, 1, 6.1f),
* Rating(6, 4, 6.4f),
* Rating(6, 7, 6.7f)
* )
* }}}
*
* Say that we are using two partitions to calculate each factor matrix:
*
* {{{
* val userPart = new ALSPartitioner(2)
* val itemPart = new ALSPartitioner(2)
* val blockRatings = partitionRatings(ratings, userPart, itemPart)
* }}}
*
* Ratings are mapped to partitions using the user/item IDs modulo the number of partitions. With
* two partitions, ratings with even-valued user IDs are shuffled to partition 0 while those with
* odd-valued user IDs are shuffled to partition 1:
*
* {{{
* userInBlocks.collect() == Seq(
* 0 -> Seq(
* // Internally, the class stores the ratings in a more optimized format than
* // a sequence of `Rating`s, but for clarity we show it as such here.
* Rating(0, 1, 0.1f),
* Rating(0, 4, 0.4f),
* Rating(0, 7, 0.7f),
* Rating(6, 1, 6.1f),
* Rating(6, 4, 6.4f),
* Rating(6, 7, 6.7f)
* ),
* 1 -> Seq(
* Rating(3, 1, 3.1f),
* Rating(3, 4, 3.4f),
* Rating(3, 7, 3.7f)
* )
* )
* }}}
*
* Similarly, ratings with even-valued item IDs are shuffled to partition 0 while those with
* odd-valued item IDs are shuffled to partition 1:
*
* {{{
* itemInBlocks.collect() == Seq(
* 0 -> Seq(
* Rating(0, 4, 0.4f),
* Rating(3, 4, 3.4f),
* Rating(6, 4, 6.4f)
* ),
* 1 -> Seq(
* Rating(0, 1, 0.1f),
* Rating(0, 7, 0.7f),
* Rating(3, 1, 3.1f),
* Rating(3, 7, 3.7f),
* Rating(6, 1, 6.1f),
* Rating(6, 7, 6.7f)
* )
* )
* }}}
*
* @param srcIds src ids (ordered)
* @param dstPtrs dst pointers. Elements in range [dstPtrs(i), dstPtrs(i+1)) of dst indices and
* ratings are associated with srcIds(i).
* @param dstEncodedIndices encoded dst indices
* @param ratings ratings
* @see [[LocalIndexEncoder]]
*/
case class InBlock[@specialized(Int, Long) ID: ClassTag](
srcIds: Array[ID],
dstPtrs: Array[Int],
dstEncodedIndices: Array[Int],
ratings: Array[Float]) {
/** Size of the block. */
def size: Int = ratings.length
require(dstEncodedIndices.length == size)
require(dstPtrs.length == srcIds.length + 1)
}
/**
* Initializes factors randomly given the in-link blocks.
*
* @param inBlocks in-link blocks
* @param rank rank
* @return initialized factor blocks
*/
def initialize[ID](
inBlocks: RDD[(Int, InBlock[ID])],
rank: Int,
seed: Long): RDD[(Int, FactorBlock)] = {
// Choose a unit vector uniformly at random from the unit sphere. This can be done by choosing
// elements distributed as Normal(0,1), and then normalizing.
// This appears to create factorizations that have a slightly better reconstruction
// (<1%) compared picking elements uniformly at random in [0,1].
inBlocks.mapPartitions({ iter =>
iter.map {
case (srcBlockId, inBlock) =>
val random = new XORShiftRandom(byteswap64(seed ^ srcBlockId))
val factors = Array.fill(inBlock.srcIds.length) {
val factor = Array.fill(rank)(random.nextGaussian().toFloat)
val nrm = BLAS.nativeBLAS.snrm2(rank, factor, 1)
BLAS.nativeBLAS.sscal(rank, 1.0f / nrm, factor, 1)
factor
}
(srcBlockId, factors)
}
}, preservesPartitioning = true)
}
/**
* A rating block that contains src IDs, dst IDs, and ratings, stored in primitive arrays.
*/
case class RatingBlock[@specialized(Int, Long) ID: ClassTag](
srcIds: Array[ID],
dstIds: Array[ID],
ratings: Array[Float]) {
/** Size of the block. */
def size: Int = srcIds.length
require(dstIds.length == srcIds.length)
require(ratings.length == srcIds.length)
}
/**
* Builder for [[RatingBlock]]. `mutable.ArrayBuilder` is used to avoid boxing/unboxing.
*/
class RatingBlockBuilder[@specialized(Int, Long) ID: ClassTag]
extends Serializable {
val srcIds = mutable.ArrayBuilder.make[ID]
val dstIds = mutable.ArrayBuilder.make[ID]
val ratings = mutable.ArrayBuilder.make[Float]
var size = 0
/** Adds a rating. */
def add(r: Rating[ID]): this.type = {
size += 1
srcIds += r.user
dstIds += r.item
ratings += r.rating
this
}
/** Merges another [[RatingBlockBuilder]]. */
def merge(other: RatingBlock[ID]): this.type = {
size += other.srcIds.length
srcIds ++= other.srcIds
dstIds ++= other.dstIds
ratings ++= other.ratings
this
}
/** Builds a [[RatingBlock]]. */
def build(): RatingBlock[ID] = {
RatingBlock[ID](srcIds.result(), dstIds.result(), ratings.result())
}
}
/**
* Groups an RDD of [[Rating]]s by the user partition and item partition to which each `Rating`
* maps according to the given partitioners. The returned pair RDD holds the ratings, encoded in
* a memory-efficient format but otherwise unchanged, keyed by the (user partition ID, item
* partition ID) pair.
*
* Performance note: This is an expensive operation that performs an RDD shuffle.
*
* Implementation note: This implementation produces the same result as the following but
* generates fewer intermediate objects:
*d
* {{{
* ratings.map { r =>
* ((srcPart.getPartition(r.user), dstPart.getPartition(r.item)), r)
* }.aggregateByKey(new RatingBlockBuilder)(
* seqOp = (b, r) => b.add(r),
* combOp = (b0, b1) => b0.merge(b1.build()))
* .mapValues(_.build())
* }}}
*
* @param ratings raw ratings
* @param srcPart partitioner for src IDs
* @param dstPart partitioner for dst IDs
* @return an RDD of rating blocks in the form of ((srcBlockId, dstBlockId), ratingBlock)
*/
def partitionRatings[ID: ClassTag](
ratings: RDD[Rating[ID]],
srcPart: Partitioner,
dstPart: Partitioner): RDD[((Int, Int), RatingBlock[ID])] = {
val numPartitions = srcPart.numPartitions * dstPart.numPartitions
ratings.mapPartitions { iter =>
val builders = Array.fill(numPartitions)(new RatingBlockBuilder[ID])
iter.flatMap { r =>
val srcBlockId = srcPart.getPartition(r.user)
val dstBlockId = dstPart.getPartition(r.item)
val idx = srcBlockId + srcPart.numPartitions * dstBlockId
val builder = builders(idx)
builder.add(r)
if (builder.size >= 2048) { // 2048 * (3 * 4) = 24k
builders(idx) = new RatingBlockBuilder
Iterator.single(((srcBlockId, dstBlockId), builder.build()))
} else {
Iterator.empty
}
} ++ {
builders.iterator.zipWithIndex.filter(_._1.size > 0).map { case (block, idx) =>
val srcBlockId = idx % srcPart.numPartitions
val dstBlockId = idx / srcPart.numPartitions
((srcBlockId, dstBlockId), block.build())
}
}
}.groupByKey().mapValues { blocks =>
val builder = new RatingBlockBuilder[ID]
blocks.foreach(builder.merge)
builder.build()
}.setName("ratingBlocks")
}
/**
* Builder for uncompressed in-blocks of (srcId, dstEncodedIndex, rating) tuples.
*
* @param encoder encoder for dst indices
*/
class UncompressedInBlockBuilder[@specialized(Int, Long) ID: ClassTag](
encoder: LocalIndexEncoder)(
implicit ord: Ordering[ID]) {
val srcIds = mutable.ArrayBuilder.make[ID]
val dstEncodedIndices = mutable.ArrayBuilder.make[Int]
val ratings = mutable.ArrayBuilder.make[Float]
/**
* Adds a dst block of (srcId, dstLocalIndex, rating) tuples.
*
* @param dstBlockId dst block ID
* @param srcIds original src IDs
* @param dstLocalIndices dst local indices
* @param ratings ratings
*/
def add(
dstBlockId: Int,
srcIds: Array[ID],
dstLocalIndices: Array[Int],
ratings: Array[Float]): this.type = {
val sz = srcIds.length
require(dstLocalIndices.length == sz)
require(ratings.length == sz)
this.srcIds ++= srcIds
this.ratings ++= ratings
var j = 0
while (j < sz) {
this.dstEncodedIndices += encoder.encode(dstBlockId, dstLocalIndices(j))
j += 1
}
this
}
/** Builds a [[UncompressedInBlock]]. */
def build(): UncompressedInBlock[ID] = {
new UncompressedInBlock(srcIds.result(), dstEncodedIndices.result(), ratings.result())
}
}
/**
* A block of (srcId, dstEncodedIndex, rating) tuples stored in primitive arrays.
*/
class UncompressedInBlock[@specialized(Int, Long) ID: ClassTag](
val srcIds: Array[ID],
val dstEncodedIndices: Array[Int],
val ratings: Array[Float])(
implicit ord: Ordering[ID]) {
/** Size the of block. */
def length: Int = srcIds.length
/**
* Compresses the block into an `InBlock`. The algorithm is the same as converting a sparse
* matrix from coordinate list (COO) format into compressed sparse column (CSC) format.
* Sorting is done using Spark's built-in Timsort to avoid generating too many objects.
*/
def compress(): InBlock[ID] = {
val sz = length
assert(sz > 0, "Empty in-link block should not exist.")
sort()
val uniqueSrcIdsBuilder = mutable.ArrayBuilder.make[ID]
val dstCountsBuilder = mutable.ArrayBuilder.make[Int]
var preSrcId = srcIds(0)
uniqueSrcIdsBuilder += preSrcId
var curCount = 1
var i = 1
while (i < sz) {
val srcId = srcIds(i)
if (srcId != preSrcId) {
uniqueSrcIdsBuilder += srcId
dstCountsBuilder += curCount
preSrcId = srcId
curCount = 0
}
curCount += 1
i += 1
}
dstCountsBuilder += curCount
val uniqueSrcIds = uniqueSrcIdsBuilder.result()
val numUniqueSrdIds = uniqueSrcIds.length
val dstCounts = dstCountsBuilder.result()
val dstPtrs = new Array[Int](numUniqueSrdIds + 1)
var sum = 0
i = 0
while (i < numUniqueSrdIds) {
sum += dstCounts(i)
i += 1
dstPtrs(i) = sum
}
InBlock(uniqueSrcIds, dstPtrs, dstEncodedIndices, ratings)
}
def sort(): Unit = {
val sz = length
// Since there might be interleaved log messages, we insert a unique id for easy pairing.
val sortId = Utils.random.nextInt()
logDebug(s"Start sorting an uncompressed in-block of size $sz. (sortId = $sortId)")
val start = System.nanoTime()
val sorter = new Sorter(new UncompressedInBlockSort[ID])
sorter.sort(this, 0, length, Ordering[KeyWrapper[ID]])
val duration = (System.nanoTime() - start) / 1e9
logDebug(s"Sorting took $duration seconds. (sortId = $sortId)")
}
}
/**
* A wrapper that holds a primitive key.
*
* @see [[UncompressedInBlockSort]]
*/
class KeyWrapper[@specialized(Int, Long) ID: ClassTag](
implicit ord: Ordering[ID]) extends Ordered[KeyWrapper[ID]] {
var key: ID = _
override def compare(that: KeyWrapper[ID]): Int = {
ord.compare(key, that.key)
}
def setKey(key: ID): this.type = {
this.key = key
this
}
}
/**
* [[SortDataFormat]] of [[UncompressedInBlock]] used by [[Sorter]].
*/
class UncompressedInBlockSort[@specialized(Int, Long) ID: ClassTag](
implicit ord: Ordering[ID])
extends SortDataFormat[KeyWrapper[ID], UncompressedInBlock[ID]] {
override def newKey(): KeyWrapper[ID] = new KeyWrapper()
override def getKey(
data: UncompressedInBlock[ID],
pos: Int,
reuse: KeyWrapper[ID]): KeyWrapper[ID] = {
if (reuse == null) {
new KeyWrapper().setKey(data.srcIds(pos))
} else {
reuse.setKey(data.srcIds(pos))
}
}
override def getKey(
data: UncompressedInBlock[ID],
pos: Int): KeyWrapper[ID] = {
getKey(data, pos, null)
}
def swapElements[@specialized(Int, Float) T](
data: Array[T],
pos0: Int,
pos1: Int): Unit = {
val tmp = data(pos0)
data(pos0) = data(pos1)
data(pos1) = tmp
}
override def swap(data: UncompressedInBlock[ID], pos0: Int, pos1: Int): Unit = {
swapElements(data.srcIds, pos0, pos1)
swapElements(data.dstEncodedIndices, pos0, pos1)
swapElements(data.ratings, pos0, pos1)
}
override def copyRange(
src: UncompressedInBlock[ID],
srcPos: Int,
dst: UncompressedInBlock[ID],
dstPos: Int,
length: Int): Unit = {
System.arraycopy(src.srcIds, srcPos, dst.srcIds, dstPos, length)
System.arraycopy(src.dstEncodedIndices, srcPos, dst.dstEncodedIndices, dstPos, length)
System.arraycopy(src.ratings, srcPos, dst.ratings, dstPos, length)
}
override def allocate(length: Int): UncompressedInBlock[ID] = {
new UncompressedInBlock(
new Array[ID](length), new Array[Int](length), new Array[Float](length))
}
override def copyElement(
src: UncompressedInBlock[ID],
srcPos: Int,
dst: UncompressedInBlock[ID],
dstPos: Int): Unit = {
dst.srcIds(dstPos) = src.srcIds(srcPos)
dst.dstEncodedIndices(dstPos) = src.dstEncodedIndices(srcPos)
dst.ratings(dstPos) = src.ratings(srcPos)
}
}
/**
* Creates in-blocks and out-blocks from rating blocks.
*
* @param prefix prefix for in/out-block names
* @param ratingBlocks rating blocks
* @param srcPart partitioner for src IDs
* @param dstPart partitioner for dst IDs
* @return (in-blocks, out-blocks)
*/
def makeBlocks[ID: ClassTag](
prefix: String,
ratingBlocks: RDD[((Int, Int), RatingBlock[ID])],
srcPart: Partitioner,
dstPart: Partitioner,
storageLevel: StorageLevel)(
implicit srcOrd: Ordering[ID]): (RDD[(Int, InBlock[ID])], RDD[(Int, OutBlock)]) = {
val inBlocks = ratingBlocks.map {
case ((srcBlockId, dstBlockId), RatingBlock(srcIds, dstIds, ratings)) =>
// The implementation is a faster version of
// val dstIdToLocalIndex = dstIds.toSet.toSeq.sorted.zipWithIndex.toMap
val start = System.nanoTime()
val dstIdSet = new OpenHashSet[ID](1 << 20)
dstIds.foreach(dstIdSet.add)
val sortedDstIds = new Array[ID](dstIdSet.size)
var i = 0
var pos = dstIdSet.nextPos(0)
while (pos != -1) {
sortedDstIds(i) = dstIdSet.getValue(pos)
pos = dstIdSet.nextPos(pos + 1)
i += 1
}
assert(i == dstIdSet.size)
Sorting.quickSort(sortedDstIds)
val dstIdToLocalIndex = new OpenHashMap[ID, Int](sortedDstIds.length)
i = 0
while (i < sortedDstIds.length) {
dstIdToLocalIndex.update(sortedDstIds(i), i)
i += 1
}
logDebug(
"Converting to local indices took " + (System.nanoTime() - start) / 1e9 + " seconds.")
val dstLocalIndices = dstIds.map(dstIdToLocalIndex.apply)
(srcBlockId, (dstBlockId, srcIds, dstLocalIndices, ratings))
}.groupByKey(new ALSPartitioner(srcPart.numPartitions))
.mapValues { iter =>
val builder =
new UncompressedInBlockBuilder[ID](new LocalIndexEncoder(dstPart.numPartitions))
iter.foreach { case (dstBlockId, srcIds, dstLocalIndices, ratings) =>
builder.add(dstBlockId, srcIds, dstLocalIndices, ratings)
}
builder.build().compress()
}.setName(prefix + "InBlocks")
.persist(storageLevel)
val outBlocks = inBlocks.mapValues { case InBlock(srcIds, dstPtrs, dstEncodedIndices, _) =>
val encoder = new LocalIndexEncoder(dstPart.numPartitions)
val activeIds = Array.fill(dstPart.numPartitions)(mutable.ArrayBuilder.make[Int])
var i = 0
val seen = new Array[Boolean](dstPart.numPartitions)
while (i < srcIds.length) {
var j = dstPtrs(i)
ju.Arrays.fill(seen, false)
while (j < dstPtrs(i + 1)) {
val dstBlockId = encoder.blockId(dstEncodedIndices(j))
if (!seen(dstBlockId)) {
activeIds(dstBlockId) += i // add the local index in this out-block
seen(dstBlockId) = true
}
j += 1
}
i += 1
}
activeIds.map { x =>
x.result()
}
}.setName(prefix + "OutBlocks")
.persist(storageLevel)
(inBlocks, outBlocks)
}
/**
* Compute dst factors by constructing and solving least square problems.
*
* @param srcFactorBlocks src factors
* @param srcOutBlocks src out-blocks
* @param dstInBlocks dst in-blocks
* @param rank rank
* @param regParam regularization constant
* @param srcEncoder encoder for src local indices
* @param implicitPrefs whether to use implicit preference
* @param alpha the alpha constant in the implicit preference formulation
* @param solver solver for least squares problems
* @return dst factors
*/
def computeFactors[ID](
userFactorBlocks: RDD[(Int, FactorBlock)],
itemFactorBlocks: RDD[(Int, FactorBlock)],
userOutBlocks: RDD[(Int, OutBlock)],
itemOutBlocks: RDD[(Int, OutBlock)],
userInBlocks: RDD[(Int, InBlock[ID])],
itemInBlocks: RDD[(Int, InBlock[ID])],
rank: Int,
regParam: Double,
userEncoder: LocalIndexEncoder,
itemEncoder: LocalIndexEncoder,
stepSize: Float,
implicitPrefs: Boolean = false, // TODO: DELETE every reference to implicitPrefs
alpha: Double = 1.0): (RDD[(Int, FactorBlock)], RDD[(Int, FactorBlock)]) = {
val numSrcBlocks = userFactorBlocks.partitions.length
val numDstBlocks = itemFactorBlocks.partitions.length
// val YtY = if (implicitPrefs) Some(computeYtY(userFactorBlocks, rank)) else None // DELETE
val userOut = userOutBlocks.join(userFactorBlocks).flatMap {
case (userBlockId, (userOutBlock, userFactors)) =>
userOutBlock.iterator.zipWithIndex.map { case (activeIndices, itemBlockId) =>
(itemBlockId, (userBlockId, activeIndices.map(idx => userFactors(idx))))
}
}
val itemOut = itemOutBlocks.join(itemFactorBlocks).flatMap {
case (itemBlockId, (itemOutBlock, itemFactors)) =>
itemOutBlock.iterator.zipWithIndex.map { case (activeIndices, userBlockId) =>
(userBlockId, (itemBlockId, activeIndices.map(idx => itemFactors(idx))))
}
}
val mergedUser = userOut.groupByKey(new ALSPartitioner(itemInBlocks.partitions.length))
val mergedItem = itemOut.groupByKey(new ALSPartitioner(userInBlocks.partitions.length))
// SPARK-28927: Nondeterministic RDDs causes inconsistent in/out blocks in case of rerun.
// It can cause runtime error when matching in/out user/item blocks.
val isBlockRDDNondeterministic =
itemInBlocks.outputDeterministicLevel == DeterministicLevel.INDETERMINATE ||
userOutBlocks.outputDeterministicLevel == DeterministicLevel.INDETERMINATE ||
userInBlocks.outputDeterministicLevel == DeterministicLevel.INDETERMINATE ||
itemOutBlocks.outputDeterministicLevel == DeterministicLevel.INDETERMINATE
// NOTE: Potentially confusing naming, itemFactors and userFactors are names of things
// in the same scope as this comment, but below we define a new dummy variable
// userFactors which then has the same name!!!
val itemFactors = itemFactorBlocks.join(itemInBlocks.join(mergedUser)).mapValues {
case (myItemFactors, (InBlock(itemIds, userPtrs, userEncodedIndices, ratings), userFactors)) =>
val sortedSrcFactors = new Array[FactorBlock](numSrcBlocks)
userFactors.foreach { case (userBlockId, factors) =>
sortedSrcFactors(userBlockId) = factors
}
val itemFactors = new Array[Array[Float]](itemIds.length)
var j = 0
// Iterates over all destination ids (factors) in the considered partition
while (j < itemIds.length) {
var i = userPtrs(j)
var numExplicits = 0
var currentItemFactor = myItemFactors(j) // TODO: Also here probably don't need to declare a variable, can just use myItemFactors(j) directly
var gradient = Array.fill(rank)(0f)
// Iterates over all relevant source factors
while (i < userPtrs(j + 1)) {
val encoded = userEncodedIndices(i)
val blockId = userEncoder.blockId(encoded)
val localIndex = userEncoder.localIndex(encoded)
var userFactor: Array[Float] = null
try {
userFactor = sortedSrcFactors(blockId)(localIndex)
} catch {
case a: ArrayIndexOutOfBoundsException if isBlockRDDNondeterministic =>
val errMsg = "A failure detected when matching In/Out blocks of users/items. " +
"Because at least one In/Out block RDD is found to be nondeterministic now, " +
"the issue is probably caused by nondeterministic input data. You can try to " +
"checkpoint training data to make it deterministic. If you do `repartition` + " +
"`sample` or `randomSplit`, you can also try to sort it before `sample` or " +
"`randomSplit` to make it deterministic."
throw new SparkException(errMsg, a)
}
val rating = ratings(i) // TODO: We probably don't need a variable here, since we don't store a copy of ratings(i) in the same way that ALS did, we just use it to compute coeff
var coeff = rating - (userFactor,currentItemFactor).zipped.map(_ * _).sum
var update = userFactor.map(-stepSize*coeff*_)
gradient = (gradient, update).zipped.map(_ + _)
numExplicits += 1
i += 1
}
itemFactors(j) = (currentItemFactor, gradient).zipped.map(_ + _)
j += 1
}
itemFactors
}
val userFactors = userFactorBlocks.join(userInBlocks.join(mergedItem)).mapValues {
case (myUserFactors, (InBlock(userIds, itemPtrs, itemEncodedIndices, ratings), itemFactors)) =>
val sortedDstFactors = new Array[FactorBlock](numDstBlocks)
itemFactors.foreach { case (itemBlockId, factors) =>
sortedDstFactors(itemBlockId) = factors
}
val userFactors = new Array[Array[Float]](userIds.length)
var j = 0
while (j < userIds.length) {
var i = itemPtrs(j)
var numExplicits = 0
var currentUserFactor = myUserFactors(j) // TODO: Also here probably don't need to declare a variable, can just use myItemFactors(j) directly
var gradient = Array.fill(rank)(0f)
while (i < itemPtrs(j + 1)) {
val encoded = itemEncodedIndices(i)
val blockId = itemEncoder.blockId(encoded)
val localIndex = itemEncoder.localIndex(encoded)
var itemFactor: Array[Float] = null
try {
itemFactor = sortedDstFactors(blockId)(localIndex)
} catch {
case a: ArrayIndexOutOfBoundsException if isBlockRDDNondeterministic =>
val errMsg = "A failure detected when matching In/Out blocks of users/items. " +
"Because at least one In/Out block RDD is found to be nondeterministic now, " +
"the issue is probably caused by nondeterministic input data. You can try to " +
"checkpoint training data to make it deterministic. If you do `repartition` + " +
"`sample` or `randomSplit`, you can also try to sort it before `sample` or " +
"`randomSplit` to make it deterministic."
throw new SparkException(errMsg, a)
}
val rating = ratings(i) // TODO: We probably don't need a variable here, since we don't store a copy of ratings(i) in the same way that ALS did, we just use it to compute coeff
var coeff = rating - (currentUserFactor,itemFactor).zipped.map(_ * _).sum
var update = itemFactor.map(-stepSize*coeff*_)
gradient = (gradient, update).zipped.map(_ + _)
numExplicits += 1
i += 1
}
userFactors(j) = (currentUserFactor, gradient).zipped.map(_ + _)
j += 1
}
userFactors
}
return (userFactors, itemFactors)
}
/**
* Encoder for storing (blockId, localIndex) into a single integer.
*
* We use the leading bits (including the sign bit) to store the block id and the rest to store
* the local index. This is based on the assumption that users/items are approximately evenly
* partitioned. With this assumption, we should be able to encode two billion distinct values.
*
* @param numBlocks number of blocks
*/
class LocalIndexEncoder(numBlocks: Int) extends Serializable {
require(numBlocks > 0, s"numBlocks must be positive but found $numBlocks.")
final val numLocalIndexBits =
math.min(java.lang.Integer.numberOfLeadingZeros(numBlocks - 1), 31)
final val localIndexMask = (1 << numLocalIndexBits) - 1
/** Encodes a (blockId, localIndex) into a single integer. */
def encode(blockId: Int, localIndex: Int): Int = {
require(blockId < numBlocks)
require((localIndex & ~localIndexMask) == 0)
(blockId << numLocalIndexBits) | localIndex
}
/** Gets the block id from an encoded index. */
@inline
def blockId(encoded: Int): Int = {
encoded >>> numLocalIndexBits
}
/** Gets the local index from an encoded index. */
@inline
def localIndex(encoded: Int): Int = {
encoded & localIndexMask
}
}
/**
* Partitioner used by ALS. We require that getPartition is a projection. That is, for any key k,
* we have getPartition(getPartition(k)) = getPartition(k). Since the default HashPartitioner
* satisfies this requirement, we simply use a type alias here.
*/
type ALSPartitioner = org.apache.spark.HashPartitioner
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
A closer look at the ColFil object
Let's start at looking at some of the pre-amble (lines 1-3)
package org.apache.spark.ml.collaborative_filtering
import org.apache.spark.ml.recommendation._
The above code cell is a so-called package cell, so we defined a package called collaborative_filtering, part of org.apache.spark.ml. Since our code has a lot in common with the ALS class from the pacakge org.apache.spark.ml.recommendation, we import everything from there so that we only have to change the things that are different for our method. In particular, we replace the ALS-object with an ColFil-object. The only difference between the two objects is that the ColFil object has fewer methods and similar, and a different train()-function and computeFactors()-function.
Now, let's look at (lines 99-113)
// Precompute the rating dependencies of each partition
val userPart = new ALSPartitioner(numUserBlocks)
val itemPart = new ALSPartitioner(numItemBlocks)
val blockRatings = partitionRatings(ratings, userPart, itemPart)
.persist(intermediateRDDStorageLevel)
val (userInBlocks, userOutBlocks) =
makeBlocks("user", blockRatings, userPart, itemPart, intermediateRDDStorageLevel)
userOutBlocks.count() // materialize blockRatings and user blocks
val swappedBlockRatings = blockRatings.map {
case ((userBlockId, itemBlockId), RatingBlock(userIds, itemIds, localRatings)) =>
((itemBlockId, userBlockId), RatingBlock(itemIds, userIds, localRatings))
}
val (itemInBlocks, itemOutBlocks) =
makeBlocks("item", swappedBlockRatings, itemPart, userPart, intermediateRDDStorageLevel)
itemOutBlocks.count() // materialize item blocks
This block of code basically rearranges and partitions the data so that it will be more efficient to work this. In particular, it creates the inBlock and outBlock objects that we discussed in the presentation notebook. However, the exact implementation of the InBlock and outBlock objects is more complicated than the examples we gave, since it's more efficient that way. It's not really worth to dive deep into exactly how they are defined unless you, like us, want to extend the ALS class. If you're curious, you can see how the inBlock and outBlock objects are defined on lines 228 and 270 respectively.
The most relevant parts of our code, i.e. those that differ the most from the ALS implementation, can be found in the computeFactors()-function on line 824. The part that is notably different from the ALS implementation is the code block starting on line 867. However, let's first look at some other parts (parts of lines 841-854)
val userOut = userOutBlocks.join(userFactorBlocks).flatMap {
case (userBlockId, (userOutBlock, userFactors)) =>
userOutBlock.iterator.zipWithIndex.map { case (activeIndices, itemBlockId) =>
(itemBlockId, (userBlockId, activeIndices.map(idx => userFactors(idx))))
}
}
val mergedUser = userOut.groupByKey(new ALSPartitioner(itemInBlocks.partitions.length))
This block of can be hard to understand without deeper insight into the structure of outBlock objects, but the result it produces is quite intuitive. MAYBE ADD SOME NICE PICTURE HERE, IN REBECKA'S STYLE???
Now, let's look at part of the code deepest within all blocks and loops of the computeFactors()-function (lines 942-945. Note that gradient is initialized to zero.)
val rating = ratings(i)
var coeff = rating - (currentUserFactor,itemFactor).zipped.map(_ * _).sum
var update = itemFactor.map(-stepSize*coeff*_)
gradient = (gradient, update).zipped.map(_ + _)
The second line sets coef by subtracting the inner product of the currently considered userFactor and the updated itemFactor (This inner product could probably have been done using some linear algebra functionality from BLAS or something, but at the time of writing it felt easier to do this way) from the relevant rating. This corresponds to the expression \((x_{ij}-\InP{u_i}{v_j})\) (FIX FORMATTING! Refer to presentation?). This coefficient, scaled by the step size of the gradient descent, is then multiplied with the relevant item factor, before it's added to the currently held value of the gradient. This corresponds exactly to the formula \(\nabla_{u_i} \ell \overset{+}{=} -(x_{ij}-\InP{u_i}{v_j})v_j\) (FIX THIS AS WELL). UPDATE SO THAT THIS IS FOR ITEM FACTORS
After that, the corresponding procedure is done to the user factors. Since we do not use an alternating approach, the user factors and item factors can in fact be updated in parallel, but we haven't implemented this due to a shortage of time caused by other challenges with the implementation. This is just a very rough overview of what the code does, to get a deeper understanding one unfortunately has to spend quite some time studying the code in more details, since the ALS implementation it is based on is quite complicated.
Using the ColFil object
Now, let us use the ColFil object we have created to actually perform some collaborative filtering
| path | name | size | modificationTime |
|---|---|---|---|
| dbfs:/datasets/sds/cs100/lab4/data-001/movies.dat | movies.dat | 171308.0 | 1.664296001e12 |
| dbfs:/datasets/sds/cs100/lab4/data-001/ratings.dat.gz | ratings.dat.gz | 2837683.0 | 1.664296001e12 |
import org.apache.spark.ml.collaborative_filtering.ColFil
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.ml.collaborative_filtering.ColFil.Rating
// Creates a very simple data-set, should probably be updated to at least repeat the example from the ALS notebook that was part of the course
val rdd = sc.parallelize(Array(Rating(0, 1, 0.1f), Rating(0, 4, 0.4f), Rating(0, 7, 0.7f), Rating(3, 1, 3.1f), Rating(3, 4, 3.4f), Rating(3, 7, 3.7f), Rating(6, 1, 6.1f), Rating(6, 4, 6.4f), Rating(6, 7, 6.7f)))
/* This would be used to activate checkpointing, but we never got checkpointing to work for our implementation
dbutils.fs.rm("dbfs:/my_checkpoints/",true)
dbutils.fs.mkdirs("dbfs:/my_checkpoints/")
sc.setCheckpointDir("dbfs:/my_checkpoints")*/
val rank = 10 // Number of "features" of users and movies
val numIterations = 10
val stepSize = 0.0001f // The gradient in gradient descent is scaled with this factor
val (userFactors, itemFactors) = ColFil.train(rdd, rank, 10, 10, numIterations, stepSize, 0.01)
val colFilModel = new MatrixFactorizationModel(rank, userFactors, itemFactors)
// For small problems, this can be used to print out the entire factor matrices. DO NOT USE FOR LARGE PROBLEMS.
/*
val justUserFactors = userFactors.map({case (ind, factor) => factor})
val justItemFactors = itemFactors.map({case (ind, factor) => factor})
justUserFactors.collect().map(row => println(row.toArray.mkString(" ")))
println("-----------------")
justItemFactors.collect().map(row => println(row.toArray.mkString(" ")))
*/
And this one???
import org.apache.spark.ml.collaborative_filtering.ColFil
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.ml.collaborative_filtering.ColFil.Rating
rdd: org.apache.spark.rdd.RDD[org.apache.spark.ml.collaborative_filtering.ColFil.Rating[Int]] = ParallelCollectionRDD[2483] at parallelize at command-1708846914807632:6
rank: Int = 10
numIterations: Int = 10
stepSize: Float = 1.0E-4
userFactors: org.apache.spark.rdd.RDD[(Int, Array[Double])] = users MapPartitionsRDD[2748] at mapValues at <notebook>:210
itemFactors: org.apache.spark.rdd.RDD[(Int, Array[Double])] = products MapPartitionsRDD[2749] at mapValues at <notebook>:214
colFilModel: org.apache.spark.mllib.recommendation.MatrixFactorizationModel = org.apache.spark.mllib.recommendation.MatrixFactorizationModel@563d2b3d
Let's actually test our model
import org.apache.spark.mllib.recommendation.Rating
// NOTE: There is some annoyance stemming from the fact that Scala (reasonably so) treats the ColFil.Rating-class and the mllib Rating-class as separate classes.
// In this block then, when it's written only "Rating", it means the mllib version is used, while writing "ColFil.Rating" means the ColFil version is used
// Evaluate the model on test data. TODO: Actually separate test and training data
val usersProductsTest = rdd.map { case ColFil.Rating(user, product, rate) =>
(user, product)
}
// get the predictions on test data
val predictions = colFilModel.predict(usersProductsTest)
.map { case Rating(user, product, rate)
=> ((user, product), rate)
}
// find the actual ratings and join with predictions
val ratesAndPreds = rdd.map { case ColFil.Rating(user, product, rate)
=> ((user, product), rate)
}.join(predictions)
val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
val err = (r1 - r2)
err * err
}.mean()
println("rank and Mean Squared Error for test data = " + rank + " and " + MSE)
rank and Mean Squared Error for test data = 10 and 17.142201953816162
import org.apache.spark.mllib.recommendation.Rating
usersProductsTest: org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[2754] at map at command-1708846914807648:4
predictions: org.apache.spark.rdd.RDD[((Int, Int), Double)] = MapPartitionsRDD[2764] at map at command-1708846914807648:10
ratesAndPreds: org.apache.spark.rdd.RDD[((Int, Int), (Float, Double))] = MapPartitionsRDD[2768] at join at command-1708846914807648:17
MSE: Double = 17.142201953816162
ratesAndPreds.collect().foreach(println)
((3,1),(3.1,0.0045716965109128815))
((0,4),(0.4,0.1102635904057618))
((0,1),(0.1,0.1999331822157152))
((3,4),(3.4,-0.5708581042986286))
((6,4),(6.4,-0.15920289788866512))
((6,7),(6.7,0.20590612770930694))
((3,7),(3.7,0.2185307432977005))
((6,1),(6.1,0.5100095114860081))
((0,7),(0.7,0.17896701855541736))
F
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